The effective implementation of sustainable forest management depends largely on carrying out sustainable forest operations (Marchi et al., 2018), which can prove to be more challenging in the context of climate change. The intensity and frequency of extreme climate events and severe insect outbreaks are predicted consequences of climate change and will alter the natural dynamics of the forests and drastically alter the local environment (Spittlehouse, 2005). For instance, operational deployment could be impeded by sudden flooding, early thawing, catastrophic tree damage, etc. The feedback loop to tactical planning in such situations happens rapidly and more frequently (Fig. 27.1).

**Fig. 27.2** Schematic showing various forest operations along the stand development stages *(dark gray boxes*), including the critical operations (*bold*)

The success and efficient deployment and the completion of any response or action depend on a prompt, timely, and reliable information feed at the planning, deployment, and operational stages. The status of vegetation (e.g., tree species or stem quality) and terrain (e.g., slope or ground-bearing capacity) features are critical information needs (Table 27.1). Their level of detail, intensity, and periodicity is defined by the complexity of the type of operation or the environmental conditions in which the operation must be completed along the stand developmental stages (Table 27.1). For example, harvesting a sustainably managed mixedwood stand growing mainly on complex terrain conditions requires safe access to the site and detailed information on the targeted species, e.g., stem quality. It is critical to properly identify the seed trees and create microsites that favor natural regeneration during operations. Hence, information needs tend to relate to planning, i.e., a priori, and during the actual operation, i.e., real time. The recentness of the acquired data is also important. Moreover, detail intensity increases from a homogeneous plantation to a heterogeneous natural stand. The level of detail for planning a harvest operation may be at the tree to stand level for vegetation, whereas accessibility (surface, slope, skid trails, landings, and wood catchment zones) is generally required at the block level (Table 27.2). However, during the harvest operation itself, the required details are instantaneous, repetitive, and intense within the operator's line of sight.

Traditionally, data used for planning purposes has been based mainly on a priori ground surveys (e.g., walk-throughs, cruising—a method to determine value of a specific area—or inventory of plot installations) or coarse interpreted images. Treatment execution is completed using visual assessments and compliance reporting with independent surveys. Recent innovations in remote sensing technology for rapidly gathering, processing, and accessing information have significantly modernized how forest operations are planned and conducted. This chapter documents current remote

**Table 27.1** Required information for vegetation and the post-harvest to complete a forest operation along stand development stages

*PHS*, preharvest survey; *Harv*, harvesting; *FibRec*, fiber recovery, i.e., the process of calculating the recovery rate, removing residual fiber, secondary use of fiber, piling, burning; *SitePrep*, site preparation; *Sd/Plnt*, seeding/planting; *Compet*, competition, i.e., weed control; *PCT*, precommercial thinning; *CT*, commercial thinning

sensing technologies suitable for understanding, monitoring, and mapping forest conditions at local scales to plan, perform, and report forest operations successfully.

**Table 27.2** Required information for the terrain and derived features to complete a forest operation along stand development stages

*PHS*, preharvest survey; *Harv*, harvesting; *FibRec*, fiber recovery, i.e., the process of calculating the recovery rate, removing residual fiber, secondary use of fiber, piling, burning; *SitePrep*, site preparation; *Sd/Plnt*, seeding/planting; *Compet*, competition, i.e., weed control; *PCT*, precommercial thinning; *CT*, commercial thinning

### **27.2 Remote Sensing Platforms for Operational Forestry**

Remote sensing is a platform-sensor combination (PSC) used to gather information about an object without being in physical contact with the object. PSC has the advantage of providing quick, synoptic, and repeated information over large and multiple spaces. The level of detail (coverage, resolution, timing, and frequency) varies with combinations of these various parameters (Table 26.2). Sensors are either passive (e.g., imaging/reflectance and thermal/radiation) or active (e.g., LiDAR or laser scanners and RaDAR or microwave scanners). The periodicity of the satellite data is fixed on the basis of constellations (daily to a few days; see Table 26.2), whereas all other acquisitions are programmed as per need (Tables 27.3 and 27.4).

Perception sensors help describe surface objects or perceive the environment. Positional or pose sensors (e.g., Global Navigation Satellite System–Global Positioning System, GNSS-GPS; Realtime Kinematics, RTK; Inertial Navigation System, INS; Inertial Measurement Unit, IMU; gyros and wheel encoders) determine the location and pose of the platform. A sensor platform refers to its carrier; these include

**Table**

**Fig. 27.3** Comparison and coregistration of LiDAR point clouds of a coniferous stand as captured from various platforms. *ALS*, aerial LiDAR; *ULS*, UAV-based LiDAR; *TLS*, terrestrial LiDAR)

mobile platforms, such as satellites, airborne platforms (aircraft and drones, also known as *unmanned aerial vehicles* or UAV), manned or unmanned ground vehicles, and human or stationary platforms, such as towers and tripods. Sensors are also sometimes distinguished by the platform they carry; for example, Liang et al. (2015) classified laser scanners as being either airborne (ALS), terrestrial (TLS), mobile (MLS), or personal (PLS). Platforms above the forest canopy can provide a synoptic view over large contiguous areas to provide a top-to-bottom description. In contrast, platforms in proximity, below the canopy, or closer to the ground provide vertical stem information and a detailed terrain description that is not feasible or possible from above-canopy platform systems (Fig. 27.3). For example, Kankare et al. (2014) demonstrated that TLS produces preharvest tree- and stand-level bucking details at a greater degree of accuracy than conventional means. Such data can help estimate the stumpage value of a stand or more suitable wood assortments.

Optical sensors capture the reflectance from materials within, e.g., standard digital RGB camera, and beyond the visible spectrum, e.g., infrared, whereas thermal sensors capture radiation from materials. Multispectral sensors (MSS) capture reflectance in limited or broad spectral regions (bands), and hyperspectral sensors have narrower but multiple bands. The spatial resolution of the image represents the ground sampling distance (GSD), which varies on the basis of the focal length of the sensor, the altitude at which the sensor is placed, and the speed with which the platform moves (Table 26.2). For instance, depending on the sensor platform, GSD may vary from a subcentimeter (e.g., drone), to submeter (e.g., WorldView series) to kilometer (e.g., AVHRR) scale. GSD is important in determining the spatial resolvability (*mappability*) of the feature on the image. Typically, assuming a reasonable contrast of the target feature from its background, more than 3 cm GSD is recommended for manually discerning trees as small as 0.4 m in height with a 35 cm crown diameter or form on an image (Pitt et al., 1997).

LiDAR (light detection and ranging) and RaDAR (radio detection and ranging) are active ranging sensors. RaDAR transmits microwave radio signals, whereas LiDAR transmits infrared energy. Both emit pulses that can penetrate through smoke, cloud, and small openings in tree canopies to reach the forest floor as well as measure the reflected backscatter. The range is converted to distance to provide precise locations (*x, y, z*) of the point of interaction with an object in space, and these sensors are best suited to describing the structure of an object, e.g., crown shape or tree height. Available radar systems provide a spatial resolution larger than 1 m and are better suited for large-scale mapping relevant to strategic or tactical forest management.

In terms of the capabilities of data recording, LiDAR systems can be fullwave (complete distribution of intercepted and returned laser pulse along the pathway) or discrete return (few observations are recorded from a laser pulse that is intercepted and reflected from targets). As they record the entire pathway along with the additional attributes of amplitude and intensity, fullwave systems are better suited for detailed above- and below-canopy characterization. Fullwave recording requires large-scale data management and algorithms, and this approach still remains at the experimental stage; however, discrete LiDAR is currently in operational use (Crespo-Peremarch et al., 2020). LiDAR systems are also differentiated by laser footprint size. A small footprint (less than one meter) on the ground provides a good link between the LiDAR beam and the structural vegetation attributes that are subtle among or within individual trees. By segregating the returns, e.g., vegetation versus ground, the points can be interpolated to describe continuous object (*digital surface model, DSM*) and terrain (*digital terrain model, DTM*) surfaces. Their arithmetic differences represent the aboveground surfaces, e.g., *canopy height model* (*CHM*). Point clouds, as well as surface models, are used to extract features. Point density and the power of the laser signal to penetrate through the canopy define feature resolvability and the estimated dimensions. Because imaging sensors receive the resulting light reaction from a particular surface, they tend to be best suited for understanding floristic compositional/structural characteristics related to the object, e.g., species, vigor, canopy cover, and density. Imagery is a 2D raster, and LiDAR is 3D point data or vertical profile; however, when images are gathered either as a stereo or overlapping sequence, they can provide photogrammetric 3D data useful for describing the structure of objects, such as canopy structure. Table 26.2 highlights the estimable direct/indirect features relevant to forestry on commonly available platforms.

The selection of PSC for a forest operation depends on the spatial extent and patterns of the area of interest, the timing, the recentness of the acquired information, and the repetitiveness between triggers for the required monitoring/reporting, specifically for vegetation status. Preharvest surveys should be within a year of the operation, whereas site preparation for competition control is conducted within a month, and regeneration surveys are two to five years after stand initiation (Table 26.1). The availability of certain RS platforms, such as satellite or aerial platforms, may be limited. Similarly, the phenology of target vegetation is an important consideration, as coniferous crops remain distinctly visible during the early spring or late fall, whereas deciduous vegetation is transparent during these periods.

Measurements with static terrestrial platforms provide a single or a small number of viewpoints and hence are limited to the validation or calibration of models built on higher platforms (Liang et al., 2018). On the other hand, mobile systems have the potential to provide near-real-time data and detailed below-canopy data relevant for operational decisions (Holmgren et al., 2019).

### *27.2.1 Positioning and Tracking Systems*

Precise positioning in space and navigation is essential for safe and effective target action through localization (e.g., fire, salvage, or herbicide sprays) or tracking activity data (e.g., machine movements or harvest operations) for assessing efficiency, quality, and productivity (Keefe et al., 2019). The most commonly used positioning tools are *global positioning systems* (GPS) operating via a constellation of satellites, 24 as in GNSS. A GPS receiver can provide latitude, longitude, elevation, and the vector heading to monitor one's location on topographic/thematic maps or imagery (Picchio et al., 2019). This information is generally integrated with a geographic information system (GIS) and can be visualized by the operator. Differential GPS or RTK systems can improve locational accuracy. Given the poor precision in forested environments because of canopies blocking satellite signals, additional sensors like INS can be used to estimate relative position and orientation of a mobile vehicle, e.g., an operating forestry machine. The heart of INS is the IMU—a combination of gyroscopes, accelerometers, and magnetic sensors used for determining translational and rotational velocity to provide a navigational solution. More recently, simultaneous localization and mapping (SLAM), a technique popular with autonomous systems, has also been tested for use in forestry (Chandail & Vepakomma, 2020; Tang et al., 2015). SLAM involves creating a map for an unknown environment while simultaneously determining the agent's location using a laser or RGB-D camera to estimate depth in combination with other location sensors, such as GPS and IMU.

### **27.3 Remote Sensing–Based Feature Extraction for Forest Operations**

In the context of a forest operation deployment sequence, we can essentially discuss remote sensing technologies as those (1) providing information on the forest environment for operational planning, monitoring, or assessing the effectiveness of an operation and/or reporting compliance; (2) gathering environmental information during the operations; and (3) relating to the operations themselves. Essentially, planning is a priori information that has a recentness from the day to a few months previous and helps determine the selection and use of machine systems. Information used for compliance or when monitoring effectiveness following a treatment must also be recent, whereas data needs are real time to near–real time for the deployment of actual operations. Above-canopy platforms are more suitable for planning and monitoring, especially in contiguous spaces, whereas close-range, terrestrial, and mobile platforms are most suitable for real-time operations. The following subsections are organized to understand how remote sensing, especially using platforms closer to the canopies, can be used for information feed, particularly in relation to vegetation and the underlying terrain, as highlighted in Table 27.1, along the sequence of a forest operation deployment. We provide, where possible, examples of different applications.

### *27.3.1 Vegetation Features*

During the stand development stages, many prescriptions call for vegetation changes to the established stands, e.g., harvesting of crop trees, regeneration cuttings in shelterwood or group selection systems, thinning, sanitation removals of diseased or infested trees, or the spraying of herbicides on competing shrubs that affect crop tree growth. These prescriptions require accurate data at the subtree, tree, or, at the least, microstand level for efficient and effective management. This relevant data includes assessing tree height, form, quality, vigor, and species, as well as the tree's surrounding environment, e.g., stocking, growing space, species mix, and competition.

#### **<IP_ADDRESS> Pretreatment Assessment**

Given its ability to reconstruct 3D forest structures and reliably estimate several biophysical parameters describing within- and below-canopy structure and function, LiDAR has become an essential component of operational forest inventories in numerous countries (Maltamo et al., 2021; Næsset, 2007; White et al., 2016). Two main approaches for LiDAR have been developed: an *area-based* and an *individualtree* approach. The former is aimed at large-scale assessments that have a coarse point density effective for producing a stand portrait. As the name suggests, the individualtree approach relies on identifying and delineating trees, including species identification, direct estimation of height and crown parameters, modeled diameter at breast height, basal area, and volume. The area-based approach (ABA) is a model-based estimate in which canopy descriptors or metrics are predicted on the basis of regression or discriminant analysis using accurate in situ plot data and height distribution (quantiles, percentiles, etc.) of LiDAR beam reflection (White et al., 2013). This method has demonstrated an accuracy of 4–8% for stem height, 6–12% for mean stem diameter, 9–12% for basal area, 17–22% for stem density, and 11–14% for volume estimates in boreal forest studies attempting to capture within-stand variability (Holmgren, 2004; Maltamo et al., 2010; Næsset, 2007; Sibona et al., 2017; White et al., 2013). In the absence of tree-level information, this stand or microstand level of characterization has been applied in eastern Canada to aid silvicultural prescriptions, such as commercial thinning or salvaging (Lussier & Meek, 2014; Meek & Lussier, 2008). Integrating vigor information with LiDAR canopy stratification helped machine operators improve productivity by 4% (Fig. 27.4; Gaudreau & Lirette, 2020). Area-based estimates using digital aerial photogrammetry collected across a range of boreal forest types is comparable with that obtained via aerial LiDAR (Goodbody et al., 2019). McRoberts et al. (2018) and Fekety et al. (2015) note, however, some challenges of using ABA models in relation to their shelf life and temporal transferability.

**Fig. 27.4** LiDAR-based stratification combined with image-based vigor for silvicultural prescription and operator assistance **a** ortho image, **b** vigor class, **c** canopy height model, and **d** logging map

The extended history of aerial and satellite platforms carrying optical sensors, more recently combined with LiDAR, has produced a large body of work demonstrating the successful implementation of remote sensing to studies of canopy vegetation (Cerrejón et al., 2021). Multiple approaches exist for quantifying and estimating the structural and compositional parameters of interest and spatially mapping these parameters at various spatial scales. Generally, very-high-resolution imagery in 2D, stereoscopic, or overlapping imagery in 3D is visually interpreted based on the calibration of a series of field plots combined with guidelines related to the vegetation in terms of foliage color, texture, crown shape, and branching structure (Corbane et al., 2015). Semi- or fully automated workflows can be summarized as segmenting the image into homogeneous objects (a tree, a collection of trees, or a stand) and then (1) estimating directly the structural or compositional parameters of interest or (2) estimating these parameters indirectly through proxy variables. Segmentation, in particular individual tree crowns (ITC), is 2D raster-based (either multispectral images, grayscale images, or CHM) and 3D point clouds (photogrammetric or LiDAR).

Separating vegetation from its background and assuming the brightest pixel to the highest point of the foliage on high-resolution 2D images (similar to raster-based CHM models), rule-based semi- or fully automated approaches can then extract tree crowns. Accuracy varies with GSD and by partitioning images into homogeneous forest stands; for instance, an accuracy of 60% (70 cm resolution) to 89% (31 cm) has been estimated for open coniferous to more complex mixedwood boreal forests, respectively (Katoh & Gougeon, 2012; Leckie et al., 2005). The number of trees per species depends on ITC accuracy, which improves when understory species are eliminated. Two-dimensional models may help with segmentation when estimating species density, although the structural assessment of canopies also requires determining canopy height.

*Digital aerial photos* (DAPs) combined with stereoscopic (visual) or digital photogrammetry can reconstruct a 3D forest canopy. Image matching and, more recently, computer-vision techniques such as SIFT (*scale-invariant feature transform*) combined with *structure from motion* (SfM) are very commonly used to estimate the 3D forest canopy from sequences of overlapping 2D images, e.g., images captured from a drone. If an accurate DTM is derivable, which can be difficult in complex, mature stands, DAPs can estimate the structural variables of the uppermost canopy, e.g., height, basal area, volume, quite accurately, comparable with the accuracy obtained using aerial LiDAR (Baltsavias, 1999; Goodbody et al., 2019). When DTM from an image is not derivable, a simple solution is to have a coregistered LiDAR or SRTM DTM for DAP point normalization or canopy surface generation (St-Onge et al., 2015). Three-dimensional forest canopy models can be useful for silvicultural prescriptions when data acquisition is optimally timed before an operation is planned. It is also possible that rapid and near-real-time inventory measurements, e.g., canopy cover, based on ocular estimates are made with an improved precision using nadir—the sensor looking vertically downward—images from a drone. UAV-SfM estimates of several inventory variables are comparable to those of LiDAR in terms of root mean square error for dominant height (3.5%), Lorey's height (13.3–14.4%), stem density (38.6%), basal area (15.4–23.9%), and timber volume (14.9–26.1%) (Puliti et al., 2015; Tuominen et al., 2015).

Although raster-based ITC approaches can segment most of the top canopy, potential segmentation within the multilayered vertical structure of the canopy to capture subcanopy elements—especially using LiDAR echoes from above-canopy platforms—is possible through point-based clustering. Hamraz et al. (2016) obtained >94% detection rate for dominant and codominant trees in complex stands. Because of the ultra-high-density data in current LiDAR systems, there is also a greater possibility of extending techniques to direct and nondestructive estimates of a suite of stemquality determinants with a high level of accuracy, including estimates of crown base, clear stem, stem taper, stem straightness, and branchiness (Vepakomma & Cormier, 2017, 2019). This offers great potential in the more refined selection of trees on the basis of target mill product specifications and automated bucking, where each tree can be analyzed at the stump to optimize its market value (Fig. 27.5).

Distinct tree architecture and branching patterns can be observed from high density LiDAR (Fig. 27.5). A 77.8% accuracy has been achieved in distinguishing predominantly boreal tree species by correlating estimated LiDAR features to vertical and horizontal foliage patterns (Li & Hu, 2012). Use of the textural or spectral intensity of multiwavelength LiDAR improved the accuracy (Budei et al., 2018). However, given the easy availability of optical images, spectral-based species discrimination is the most suitable, rapid, and pragmatic means of mapping large forest spaces.

Accuracy in tree species classification has improved through a priori crown extraction (Dalponte et al., 2014), although Heinzel and Koch (2011) found pixel-based classification improved the undersegmentation of crowns. While very-high spatial and super-spectral-resolution images such as the Worldview series are promising, the automated discrimination of more than ten tree species has been achieved with 82% accuracy using high-spatial-resolution MSS data (Immitzer et al., 2019). Accuracy

improved greatly when models were adapted to narrowband hyperspectral sensors (Fassnacht et al., 2014; Modzelewska et al., 2020). Hyperspectral data, nevertheless, is data- and process-intensive and is restricted to being the most successful when collected in bright light conditions. Some researchers have found that sensorfusion approaches, such as MSS or hyperspectral data with LiDAR, have improved species discriminability in boreal regions (Dalponte et al., 2014; Trier et al., 2018). These models identified as many as 19 species at 87% accuracy. Because temporal variability is a critical factor for species discrimination and there is an existing insufficiency of training samples, drone-based solutions can serve to map at local scales and develop a reference database (Fassnacht et al., 2014; Natesan et al., 2020). After iteratively building tree libraries from drone-based simple RGB images acquired in variable light-season-year conditions, Natesan et al. (2020) discriminated five conifer species at 73–91% accuracy (Fig. 27.6) and adaptively improved this library to identify six more deciduous taxa at over 79% accuracy in boreal regions.

An indicator of forest health is a forest's resistance and resilience to disturbances and its ability to adapt to climate change over the long term. Altered structure, functioning, or taxonomy because of the physiological stress of resource limitation, disease, or disturbances occur at all spatial (vertical and horizontal) and temporal

**Fig. 27.6** Tree species recognition using an extensive image library from UAV-based RGB images; **a** automated crown delineation, **b** extracted crowns for training, **c** softwood species classification. Modified with permission from Canadian Science Publishing, permission conveyed through Copyright Clearance Center, Inc., from Natesan et al. (2020)

scales; nevertheless, capturing early signs can help minimize disturbance-related damage. Using the concepts of spectral traits and their variability (direct or proxy variables of forest health), Lausch et al. (2013) conducted an extensive review of the best PSC and available techniques for quantifying or qualifying short- to long-term monitoring of vigor. Close-range sensing improves precision or calibrates spectral responses of stress or disturbance in airborne remote sensing (Fassnacht et al., 2014). Slight declines in chlorophyll or moisture levels (identifiable through hyperspectral sensing) have helped provide early warnings of bark beetle (Fassnacht et al., 2014; Safonova et al., 2019) and herbivorous insect (Cardil et al., 2017; Meng et al., 2018; Vepakomma et al., 2021) infestations. There has been some success in identifying isolated impacted trees to group mortality (Fassnacht et al., 2014; Sylvain et al., 2019) and distinguishing the effects of multiple disturbances, e.g., pine blister rust and mountain pine beetle (Coops et al., 2003; Hatala et al., 2010).

#### **<IP_ADDRESS> During Treatment**

Planning and executing forest operations is as much about following best practices as it is avoiding changing or damaging cultural remnants and special biotopes or transgressing property borders. LiDAR has been used to detect cultural heritage sites (Risbøl et al., 2014) and map habitat characteristics with the possibility of earmarking areas that must be avoided (Evju & Sverdrup-Thygeson, 2016). Proximal scanning also shows a strong potential in providing decision support during operations. For example, the rapid detection and estimates of stand density, tree position, and stem diameter (Holmgren et al., 2019) has helped with thinning tree selection and allowed data to be collected on individual tree selection by harvester operators through modeling of which tree the operator might select a priori (Brunner & Gizachew, 2014) or during operations (Gaudreau & Lirette, 2020).

#### **<IP_ADDRESS> Post-operation Monitoring**

In most jurisdictions, standard practice involves compliance of contractual or regulatory frameworks and a post-operation follow-up; for example, these can entail assessment of post-harvest renewal or establishment monitoring to ensure sustainable production. The desired management objectives are typically to control stocking, species composition, survival, and growth. The distinct conical shape of conifer seedlings allows their easy detection for both planted trees and natural irregularly spaced stems. Vepakomma et al. (2015) distinguished conifer seedlings at least 0.3 m in height and estimated their size with a low average bias of 0.02 m through simple RGB images obtained from a drone. The data formed a basis for evaluating stocking, growing space, and regeneration gaps. By distinguishing competitive species, the models were further extended to qualify free-growing trees and assess regeneration compliance (Fig. 27.7). Pouliot et al. (2002) found that although the automated detection of six-year-old planted conifers was significantly high (at 91%), crown size extraction was sensitive to pixel resolution. In their case, they noted an 18% error compared with field assessments.

### *27.3.2 Terrain Features*

The cost, efficiency, and potential environmental impact of forest operations all depend greatly on terrain features, e.g., surface roughness, slope, obstacles, and hydrographic data (flow channels, slope, drainage, and wet areas). These features can be described at macro-, meso-, and microlevels. DTM at corresponding resolutions are derivable using RaDAR interferometry, e.g., inSAR from satellite, available SRTM (Shuttle RaDAR Topographic Mission) data, LiDAR, or images using photogrammetric techniques where the ground is visible (Talbot & Rahif, 2017). Given the current technologies, LiDAR has proven to be the best available and most accurate tool for terrain assessments of mature stands. However, ALS with coarse data density can still provide a resolution greater than what planning methods can actually use (Talbot & Rahif, 2017).

Knowledge of terrain surface roughness and the number of potential hazards, especially under a dense canopy or on steep slopes, is critical for operational safety. Full-waveform LiDAR has a higher chance of returns from dense terrain and enables the successful detection of hazards, such as protruding rocks over 2 m wide (Chhatkuli

**Fig. 27.7** Automated coniferous regeneration assessment for UAV-based RGB images for reporting compliance; **a** orthorectified image, **b** species group map, **c** species-wise detected individual stems, **d** stem height calibration model

et al., 2012). Slope and hydrographic features are directly derivable from a DTM, and model-based indices are used to identify wet areas and surface roughness (Ågren et al., 2014; Murphy et al., 2008). Identifying surface features that could be potential hazards helps ensure the safe driving of machinery at a harvest site (Fig. 27.8; Li and Vepakomma, 2020). Wet-area maps, which are characterized by indices such as cartographic depth-to-water (DTW) or the topographic wetness index (TWI) help to assess soil, vegetation, and drainage type and are used by the machine operators during forest operations to avoid or mitigate site damage (Ring et al., 2020). Such maps constitute a considerable improvement in recent forest management data and the planning of forest operations (Talbot & Astrup, 2021).

#### **<IP_ADDRESS> Pretreatment Assessment**

**Harvesting planning**. Forest operations alter the environment, which, most often, is desired and intended. Undesirable impacts occur in particular when moving material or equipment into the forest (Rummer, 2002). In practice, machine operators in Europe and the Americas use tree cover and ground information as a canvas to plan harvesting or the moving of equipment. Providing an automated feature extraction

**Fig. 27.8** Hazard detection and model-based drivability map using ultra-high-density LiDAR data from a UAV; **a** detected hazards overlying an orthorectified RGB image, **b** modeled drivability index showing a gradient of drivable (*green*) to no-go areas (*red*), **c** digital elevation model, **d** rock outcrops captured by mobile LiDAR, **e** extracted stumps. Modified with permission from Li and Vepakomma (2020)

from remote sensing as part of harvest planning can significantly minimize detrimental factors. These features can then be fed into algorithms to identify potential landings adjacent to roads or aid route optimization (Flisberg et al., 2021). In groundbased harvesting, the skid trail layout should be adapted to both the topography and the soil bearing capacity. Rönnqvist et al. (2020) combined digital elevation models, depth-to-water maps, and LiDAR-based tree volumes to spatially optimize extraction routes. In steep terrain, identifying suitable load paths for cable yarding and maximizing the use of each yarder setup is essential for optimizing economic performance. Detection of suitable end trees (tail spars) and intermediate support trees and discerning actual terrain form between contour lines—previously carried out by manual profile surveys—can now be easily replaced by LiDAR assessments (Dupire et al., 2015; Søvde, 2015).

**Roads and transport**. Monitoring forest road conditions includes gathering information on road geometries, surface conditions, condition of the drainage system, the presence of vegetation, and seasonal damage (Talbot & Rahif, 2017). Regular geometric shapes such as roads are easily discernable on images and high-resolution LiDAR, which can be used to provide information on widths, curve geometries, and slope (White et al., 2010). Similar to the in-field driving applications, Waga et al. (2020) used LiDAR-derived TWI models to predict road quality. They obtained an accuracy of up to 70 and 86% when the models were combined with other variables, such as surface quality index and soil type. Surface quality factors, including roughness, gradient, and camber, can also be recorded from a vehicle using a profilograph and then entered into the model to determine the effect of these factors on fuel consumption during timber hauling (Svenson & Fjeld, 2016).

#### **<IP_ADDRESS> Post-treatment Assessment of Disturbances**

Harvest compliance in many jurisdictions includes minimizing rutting and damage to soils (Talbot & Rahif, 2017). Remote sensing can help locate and characterize ruts and assess the level of soil disturbance caused by an operation (Pierzchała et al., 2016). Haas et al. (2016) used photogrammetry to quantify variations in rutting related to tires of differing dimensions and the use of steel bands on forwarders. In a similar analysis, Marra et al. (2018) considered differences in tire pressure and the effect of several forwarder passes on rut development. Although this information is helpful at an individual rut level, remote sensing can also help locate and characterize ruts at the site level. Nevalainen et al. (2017) proposed a method for measuring rut depths from point clouds derived from images captured from a UAV. Talbot et al. (2018) used UAVbased orthomosaics to determine the extent and severity of rutting at a stand level and developed a method to reduce the need for field sampling in assessing site impacts. However, photogrammetry-based solutions have their limitations; for example, light and weather conditions can affect accuracy. Moreover, although surface models can be generated, occlusion greatly limits information related to site conditions for sites under a partial canopy or under brush mats on the ground.

### **27.4 Remote Sensing–Enabling Autonomy**

The automation of the remote sensing information feed for active decision support and adaptive forest management is very close to reality. Embedded sensors that were used to remotely monitor hazardous or inaccessible environments (e.g., nuclear reactors or rail tracks) are being applied to the proximal monitoring of machine movements and perception of surrounding forest environments (Holmgren et al., 2019). SLAM, through onboard sensors such as 2D LiDAR scanners and stereocameras, has demonstrated its potential in estimating machine pose with reference to its complex unstructured forest surroundings either in combination with GPS (Pierzchała et al., 2018; Tang et al., 2015) or using only visual odometry in GPSdenied environments (Chandail & Vepakomma, 2020).

The last decade has seen a gradual paradigm shift toward developing "intelligent" machines converging with sensing systems, thereby moving from automation to autonomously navigating and negotiating different entities. In the aerial sector, miniaturized vision systems and artificial intelligence (AI) combined with remotely piloted systems or fully autonomous UAV swarms can now monitor and provide real-time situational awareness. For example, Hummingbird drones mounted with infrared-sensing instruments and AI are now used for fire monitoring (www.hum mingbirddrones.ca). There is a movement away from man-heavy to man-light operations in manufacturing, agriculture, and mining sectors focusing on improving productivity or safety under challenging conditions. Although the forestry sector mandates environmentally friendly systems, the harsh diverse forest environment and obstacle-ridden forest floor may tax the limits and the reliability of all types of instruments (Billingsley et al., 2008). Although challenging to implement, the automation and autonomizing of future forestry is the focus of considerable research through programs, such as Forestry 4.0 (Canada, https://web.fpinnovations.ca/for est-operations-solutions-to-help-the-canadian-forest-industry/forestry-4-0/, https:// www.youtube.com/watch?v=r4vhLQ8OEP0) or Auto2 (Sweden, Gelin et al., 2021), and the application of such programs to forestry issues (e.g., forest fire management, Sahal et al., 2021).

The success of any current or future forest management, particularly when dealing with triggered changes from extreme climate change–induced events, will require a prompt, timely, and reliable information feed. Remote sensing has been evolving, emerging, and converging with enabling technologies and offers reliable decision support and can ensure safer forest operations.

### **References**

countries. *Canadian Journal of Forest Research, 51*(2), 257–266. https://doi.org/10.1139/cjfr-2020-0322.

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

### **Part IX Trends and Challenges**

### **Chapter 28 Network Framework for Forest Ecology and Management**

### **Élise Filotas, Isabelle Witté, Núria Aquilué, Chris Brimacombe, Pierre Drapeau, William S. Keeton, Daniel Kneeshaw, Christian Messier, and Marie-Josée Fortin**

**Abstract** Applications of network science to forest ecology and management are rapidly being adopted as important conceptualization and quantitative tools. This chapter highlights the potential of network analysis to help forest managers develop strategies that foster forest resilience in our changing environment. We describe how networks have been used to represent different types of associations within forest ecosystems by providing examples of species interaction networks, spatial and spatiotemporal networks, and social and social-ecological networks. We then review basic measures used to describe their topology and explain their relevance to different management situations. We conclude by presenting the challenges and potential opportunities for an effective integration of network analysis with forest ecology and management.

I. Witté e-mail<EMAIL_ADDRESS>
N. Aquilué e-mail<EMAIL_ADDRESS>

É. Filotas

Département Science et Technologie, Université TÉLUQ, 5800 St-Denis, Montréal, QC H2S 3L4, Canada

I. Witté PatriNat, OFB-CNRS-MNHN, 36 Rue Geoffroy St Hilaire, CEDEX 05, CP 41, 75231 Paris, France

© The Author(s) 2023 M. M. Girona et al. (eds.), *Boreal Forests in the Face of Climate Change*, Advances in Global Change Research 74, https://doi.org/10.1007/978-3-031-15988-6\_28

685

É. Filotas (B) · I. Witté · N. Aquilué · D. Kneeshaw · C. Messier

Centre for Forest Research, Université du Québec à Montréal, P.O. Box 8888, Stn. Centre-Ville, Montréal, QC H3C 3P8, Canada e-mail<EMAIL_ADDRESS>
### **28.1 Introduction**

Understanding how human activities modify the structure and function of forest ecosystems is a central challenge for achieving sustainable forest management. To this end, in recent decades, forest scientists have started applying network theory to ecosystem management (Dale & Fortin, 2010, 2021; Fall et al., 2007; Hamilton et al., 2019; Martin & Eadie, 1999; Rayfield et al., 2011). Network theory provides a novel framework for designing effective strategies intended to maintain forest functions while conserving biodiversity (Aquilué et al., 2020; D'Aloia et al., 2019; Messier et al., 2019; Ruppert et al., 2016).

Forest ecosystems are composed of highly heterogeneous elements—organisms to forest stands—that interact through ecological processes over a wide range of temporal, spatial, and organizational scales (Filotas et al., 2014). Specifically, network theory can be used to model forest ecosystems as ensembles of connected elements (Aquilué et al., 2020; Mina et al., 2021; Ruppert et al., 2016). Examples include food webs linking species across several trophic levels (Eveleigh et al., 2007), nest webs linking species across microhabitat structures such as tree cavities (Martin et al., 2004), isolated forest fragments connected by wind or animal dispersed seeds (Aquilué et al., 2020), and social organizations engaged in a common management effort (Fischer & Jasny, 2017). Network analysis focuses on describing the topology of interactions linking elements together and can establish a relationship between this network topology and forest functions for management purposes (Ruppert et al., 2016). In particular, network analysis can be used to quantify the alteration of forest

C. Brimacombe · M.-J. Fortin

Department of Ecology and Evolutionary Biology, University of Toronto, 25 Willcocks Street, Toronto, ON M5S 3B2, Canada e-mail<EMAIL_ADDRESS>
M.-J. Fortin e-mail<EMAIL_ADDRESS>
P. Drapeau

Centre for Forest Research and UQAT-UQAM Chair in Sustainable Forest Management, Université du Québec à Montréal, P.O. Box 8888, Stn. Centre-Ville, Montréal, QC H3C 3P8, Canada

e-mail<EMAIL_ADDRESS>
#### W. S. Keeton

Rubenstein School of Environment and Natural Resources and Gund Institute for Environment, University of Vermont, 81 Carrigan Drive, Burlington, VT 05405, USA e-mail<EMAIL_ADDRESS>
#### C. Messier

Département des Sciences Naturelles, Institut des Sciences de La Forêt Tempérée, Université du Québec en Outaouais, 58, Rue Principale, Ripon, QC J0V 1V0, Canada

M.-J. Fortin

Département des Sciences Naturelles, Institut des Sciences de La Forêt Tempérée, 58 Rue Principale, Ripon, QC J0V 1V0, Canada

functions resulting from human-mediated and natural disturbances that directly or indirectly modify the ecological components of forest ecosystems, including their interactions and spatial setting (Aquilué et al., 2020).

A network is a simplified representation of a system based on connections links—among its component elements—nodes. A food web, for example, is a network representing the trophic interactions among an ecosystem's constituent species (Pimm et al., 1991). Each element in a network is represented by a node, also called a vertex, which may be connected to other nodes by links, also called edges, representing potential or realized interactions between two elements. Nodes are defined by one or more attributes and their connections to other nodes. Links may be unidirectional or bidirectional and may be weighted to express the strength of an interaction. In a food web, for instance, nodes represent species, and links represent predator–prey interactions among species (Ings et al., 2009). A unidirectional link would represent a predator species feeding on a prey, whereas a bidirectional link could represent a mutual interaction or dependency between two species. Moreover, a node could be characterized by its species' abundance, and a link could be weighted to represent a predator's relative preference for a given prey.

Network science originates from graph theory, a fundamental topic in the field of discrete mathematics that can be traced to the work of Euler in the eighteenth century (Newman, 2003). Nowadays, the study of networks is pervasive across all fields of science, including molecular biology, neuroscience, linguistics, and epidemiology (Newman, 2003; Strogatz, 2001; Turnbull et al., 2018). The World Wide Web, social media networks, and global plane travel networks are only a few of many examples of networks present in our everyday life.

Network science continues to develop tools that characterize the topology of networks, a concept referring to the architecture of nodes and links. Moreover, it studies the possible relationships between a network topology and the ability of the corresponding system to function and adapt to disturbances. Generally, the strength of network science is the universality of tools available for studying disparate systems, varying widely in their nature and scale (Albert & Barabási, 2002). For example, the structure of a network can provide information about its vulnerability or adaptability to the loss or addition of nodes and links or the efficiency with which resources and information are propagated within the network (Fig. 28.1; Barabási & Albert, 1999; Watts & Strogatz, 1998). Will a food web collapse following the extinction of a given species? Is an epidemic more likely to spread within a population if a given demographic group is infected? Can consensus within a community divided over an environmental issue be improved by creating new communication channels? These and other important basic and applied science questions can be answered using the methods from network science.

The application of network theory to ecology and evolutionary biology has seen a remarkable development over the past 20 years (Dale & Fortin, 2010; Kool et al., 2013; Proulx et al., 2005). Well-studied ecological networks include protein and gene networks (Jeong et al., 2001; Vidal et al., 2011), pollination networks (Bascompte et al., 2003; Memmott et al., 2004), food webs (Dunne et al., 2002a), nest webs (Martin et al., 2004), and habitat conservation networks (Urban & Keitt, 2001).

**Fig. 28.1** Different topologies of undirected networks. **a** Regular network in which all nodes have the same number of connections; **b** a *small-world* network constructed by rewiring a few nodes of a regular network, thereby reducing its diameter and making each node easily accessible from any other nodes of the network (Watts & Strogatz, 1998); **c** a *scale-free* network created by adding connections to nodes with a probability that increases with their number of connections such that well-connected nodes become even more connected. Such networks are more vulnerable to disturbances that target hubs (Barabási & Albert, 1999). **d** Random networks in which the number of connections is randomly assigned to each node

Specific applications of network science to forest ecology and management are more recent, but this approach is rapidly gaining adoption as an important conceptualization and quantitative tool. For example, networks are used to understand how locally interacting entities drive forest ecosystem functions and inform management strategies that more directly integrate cross-scale interactions (Messier et al., 2019).

This chapter highlights the potential of network thinking to address key issues of cross-scale interactions in forest ecology and management. First, we describe how networks have been used to represent different types of associations within forest ecosystems by reviewing examples of species interaction networks, spatial and spatiotemporal networks, and social and social-ecological networks. We explain how nodes and links can be defined and synthesize the particular features that characterize each network type. Then, we review basic measures used to quantify the structure of networks and explain their relevance to different management situations. We conclude by presenting the challenges and potential opportunities for an effective integration of network analysis with forest ecology and management. The network framework may prove invaluable in helping forest managers to better anticipate and adapt to global change.

### **28.2 Representing Forests with Networks**

As with any network, the identification of nodes and links varies with the questions of interest and with respect to how the system under study can be decomposed into sets of distinct and interacting components (Table 28.1). Here, we describe different network categories employed in forest ecology and management. These categories differ in the nature of nodes, including individual species, forest stands, and governance institutions. Consequently, the type and scale of interaction among nodes also vary between these categories.

**Table 28.1** Different categories of networks employed in forest ecology and management. For each category, examples of nodes, links, and relevant references are provided Network category

28 Network Framework for Forest Ecology and Management 691

### *28.2.1 Species Interaction Networks*

In networks of species interactions, a single species sometimes provides a natural unit for denoting a node. This is the case, for example, in pollination networks (Devoto et al., 2011; Vázquez et al., 2009), host–parasitoid networks (Memmott et al., 1994), and nest webs (Martin & Eadie, 1999; Martin et al., 2004). However, other systems may highlight the need for different aggregation units, such as species playing a common function or a guild of species with a similar trophic position (Dunne et al., 2002a). Links between nodes denote potential or realized interspecific interactions that may or may not involve biomass transfer, including antagonistic (e.g., plant– herbivore and host–parasitoid networks), mutualistic (e.g., pollination networks), symbiotic (e.g., mycorrhizal network), and commensal associations (e.g., nest webs) (Delmas et al., 2019).

Networks of interspecific interactions may be unipartite, meaning that any two nodes may interact, or they may be ordered over multiple hierarchical levels where only nodes in different levels can interact (Fig. 28.2a; Delmas et al., 2019). In nest webs, which represent the relationships among tree species and cavity-nesting vertebrates, links connect tree species to one or more *nidic* levels (Martin & Eadie, 1999; Martin et al., 2004; Ruggera et al., 2016). These levels consist of cavities that originate either from tree decay or from animal excavators, and also include obligate cavity users, which cannot excavate a cavity and thus depend entirely on existing cavities for nesting (Cockle et al., 2019; Martin et al., 2004). Host–parasitoid networks may also encompass lower (plant–herbivore) and higher (parasitoid–hyperparasitoid) trophic levels (Eveleigh et al., 2007). On the other hand, some networks focus on representing the associations between two levels only, such as pollinator–plant (Devoto et al., 2011; Gómez-Martínez et al., 2020), plant–herbivore (Cagnolo et al., 2011), and plant–frugivore networks (Chama et al., 2013). Such networks, termed bipartite networks (Fig. 28.2b), can also be used to represent nest webs (Cockle & Martin, 2015; Ruggera et al., 2016) and host–parasitoid networks over narrower scales of interspecific organization (Tylianakis et al., 2007; Van Veen et al., 2008).

Ecological networks can also be used to represent mycorrhizal associations between plant roots and fungi, or relationships among algae, fungi, and sometimes bacteria within lichen (Southworth et al., 2005). Two different approaches may be

**Fig. 28.2** Different categories of network in forest ecology. **a** Network extending over multiple hierarchical levels, e.g., food webs and nest webs; **b** a bipartite network where nodes are separated into two levels, e.g., pollinators (*yellow*) and plants (*green*); **c** an undirected spatial network where links denote potential least-cost movement between patches of habitat (*green polygons*)

adopted (Table 28.1). The first studies the bipartite network formed by the symbiotic interactions between the hosts (plant or algae species) and their associated (endophytic or endolichenic) fungi (Chagnon et al., 2012; Toju et al., 2015). The second adopts a phytocentric perspective where tree boles in a sampling plot correspond to nodes and links. This represents the pairwise connection of trees through the same fungal genet (Beiler et al., 2010, 2015; Simard, 2009; Van Dorp et al., 2020).

### *28.2.2 Spatial and Spatiotemporal Networks of Forest Ecosystems*

In spatial and spatiotemporal networks, nodes are conceptualized as spatially localized units of contiguous area, such as forest stands that, when aggregated, compose forested landscapes (Table 28.1; Bunn et al., 2000; Fall et al., 2007; Pelletier et al., 2017; Urban & Keitt, 2001). We can distinguish between habitat-patch networks and forest-stand networks. The former stresses the relationship between habitat patches for wildlife connectivity—usually for the conservation of a specific species or group of species of concern (Gurrutxaga et al., 2011; James et al., 2005; Ruppert et al., 2016) or to predict the spread of undesirable species (de la Fuente et al., 2018; Ferrari et al., 2014; Wildemeersch et al., 2019)—whereas the latter focuses on the connectivity of tree communities (Aquilué et al., 2020; Craven et al., 2016; Saura et al., 2011). Nodes are defined either by the GPS locations of organisms, bird nests (Melles et al., 2012), and territories/home ranges or by delineated forested patches according to specific criteria, e.g., stand age, structure, and species composition (Aquilué et al., 2020). Nodes can be characterized by spatial, e.g., area, shape, edge/area ratio, and nonspatial attributes, e.g., species diversity, habitat quality.

In spatial networks, links between nodes denote the movement of animals or plant seeds, either as a potential or a relative measure (Bunn et al., 2000; Fall et al., 2007; Urban & Keitt, 2001). Links can be determined according to species' dispersal abilities and behavioral responses to the intervening landscape that facilitates or impedes organism movement (i.e., functional connectivity; Rayfield et al., 2010). Thus, links can be represented by the Euclidean distances between patches or as a function of movement cost. In this case, the distance between patches is weighted by the additional difficulty for a given species to disperse through the given matrix cover types (James et al., 2005). Consequently, spatial networks provide a framework to evaluate the functional connectivity of a landscape for a particular species or tree community, transcending simpler structural connectivity assessments.

Unlike species interaction networks where links are mostly directed, thereby expressing relationships between consumers and their resource, spatial networks can have both directed and nondirected links and do not form a hierarchical structure (Fig. 28.2c). In habitat networks, links are nondirected because an animal's ability to move between two habitat patches can, theoretically, be assumed to be the same in both directions (Ruppert et al., 2016). On the other hand, in forest-stand networks, a node contains a community of tree species that differ in their seed dispersal ability (Tamme et al., 2014). Thus, the flux of seeds dispersing from one stand to another is not equivalent in both directions, leading to directed links between nodes (Aquilué et al., 2020).

#### **Box 28.1 Spatiotemporal networks**

In spatiotemporal networks, habitat patches or forest stands are dynamic, where: **a** the weights of both nodes and links change through time but not the network topology, **b** as in **a** although the topology changes through time, and **c** the nodes and links are given by organisms' movements.

To determine the degree of functional connectivity of a habitat network and how it changes through time, one can quantify connectivity at specific times as a series of static snapshots. However, the degree of connectivity can be affected by the temporal dimension of the forest dynamics relative to the species' longevity (Zeigler & Fagan, 2014). For this reason, one cannot treat habitat networks at different times as independent static snapshots. To address the effects of such transient dynamics of habitat patches, Martensen et al. (2017) proposed a novel spatiotemporal connectivity algorithm to quantify the sequential spatial overlaps of habitat patches that are available to account for a temporal window matching species life history. Martensen et al. (2017) showed—by considering explicitly in their algorithm the spatiotemporal dimension of habitat patches and species dispersal abilities—that the transient use of habitat patches can favor a higher degree of connectivity compared with static spatial connectivity values.

Spatiotemporal networks express relationships within and between spatial networks at different times (Huang et al., 2020; Martensen et al., 2017). They add the temporal dimension to spatial networks by integrating the dynamic nature of forest stands and habitat patches (Box 28.1). They capture the fact that ecological processes and disturbances affect the persistence and attributes of spatial nodes. For example, tree communities within forest stands and habitat patches undergo successional changes and are modified by natural disturbances (e.g., fire, insect outbreak, drought, and windthrow) and human activities (e.g., harvesting and land-use change). If network nodes change too quickly or are destroyed, organisms may not have time to reach other suitable nodes. Therefore, nodes and links that were present in a static spatial network could be absent in a spatiotemporal network. Moreover, this framework allows for the representation of indirect links between patches to indicate that an organism has moved through an intermediate stepping-stone patch that has been gained or lost during the two different time observations.

### *28.2.3 Social and Social-Ecological Networks*

Nodes in social networks represent any social entity, from single individuals, e.g., a forest owner or user, to collectives of individuals, e.g., forest management organizations, forest-based communities, or groups of stakeholders sharing similar interests or belonging to the same governance sectors (Guerrero et al., 2020). Links between these social entities can correspond to both formal and informal relationships and represent (1) flows (e.g., information, resources, and money), (2) social relations (e.g., employee of, neighbor of) and interactions (e.g., work with, share information to), and (3) similarities (e.g., same location, same attitude) (Borgatti et al., 2009; Guerrero et al., 2020). Nodes may be characterized by demographic and social/cultural attributes (e.g., age and occupation), attitudes and behaviors toward a management or conservation issue, and features of the corresponding organization, e.g., size, mission, and governance level. Links can be weighted according to the strength of the relationship or frequency of the interaction (Guerrero et al., 2020).

Depending on the social system under study and the types of relationships considered, links in social networks can be directed, e.g., sharing information to, or undirected, e.g., same conservation goal as another entity, and form different hierarchical structures ranging from one to multiple levels of governance that include several jurisdictions and geographic areas (Fischer, 2018; Guerrero et al., 2020). Moreover, social networks are shaped by processes specific to human and social interactions, such as *homophily*, *intentionality*, and *reciprocity* (Fischer & Jasny, 2017; Guerrero et al., 2020; Knoot & Rickenbach, 2014). Homophily refers to the tendency to be connected to people having similar values and goals, whereas intentionality refers to the conscious choice to associate (or not) with someone else, and reciprocity is the tendency for mutual interactions.

Social-ecological systems can also be represented by networks (Folke, 2006; Kleindl et al., 2018) and aim to capture the interplay and possible feedbacks between human decisions and actions in managing an ecosystem and the structure and function of that ecosystem (Bodin, 2017; Bodin & Tengö, 2012; Fischer, 2018; Janssen et al., 2006). Generally, social-ecological networks are used in the context of governance challenges emerging from (1) a scale mismatch between the ecological and the social processes operating in the system, (2) competition for access, use, or management of a shared ecological resource, and (3) sensitivity to the order with which management activities are realized, e.g., steps to take to reduce risk (Bodin et al., 2019; Hamilton et al., 2019). Therefore, social-ecological networks comprise both ecological and social nodes and focus on the interdependencies between these various kinds of nodes. For example, a social-ecological link could represent timber harvesting by a forest owner (social node) in their forest stand (ecological node). Ecological nodes usually consist of groups of plants or animals, or have a spatial dimension, such as specific forest patches. However, more aggregated biophysical forms, e.g., ecosystem services (Dee et al., 2017), may be a more appropriate node representation when social-ecological interactions are associated with specific ecological functions that are produced by multiple ecological entities (Bodin et al., 2019).

Social-ecological networks may develop via human activities that create interactions between ecological elements (Janssen et al., 2006). For example, firewood movement between localities is associated with the wide dispersal of emerald ash borer (*Agrilus planipennis*) across North American forests (Siegert et al., 2015), and the construction of forest roads has been associated with increased gray wolf (*Canis lupus*) movement across managed forest stands (Courbin et al., 2014). Social interactions may also emerge from ecological connections. For instance, when two organizations managing distinct forest lands decide to collaborate on a wildfire risk mitigation strategy following a forest fire that has burned across both lands (Hamilton et al., 2019). Sayles et al. (2019) distinguished between different kinds of socialecological networks depending on how nodes and links are defined: (1) *multiplex networks* in which all nodes can be connected by social and ecological links; (2) *multilevel networks* in which social and ecological nodes are viewed as being on different layers and only one interaction between any two nodes is considered; and (3) *multidimensional networks* in which nodes are represented as in multilevel networks, but multiple interactions between nodes are possible (Fig. 28.3).

**Fig. 28.3** Different frameworks to represent social-ecological networks, as suggested by Sayles et al. (2019). **a** Multiplex network where nodes (*black circles*) can be either social or ecological and connected by both social and ecological links; **b** and **c** multilevel networks with social (*blue circles*) and ecological (*green polygons*) nodes on different layers. Nodes are connected by intralayer links (*blue* or *green*) and/or interlayer links (*orange*). In **b,** only one link exists between pairs of nodes, and in **c** multiple interactions between nodes are possible, as in multiplex networks

### **28.3 Network Analysis**

Multiple statistical measures can be extracted from networks to describe their architecture and determine the degree to which a system is connected, how interactions are distributed among nodes, and whether specific nodes occupy important positions. These measures are then used as indicators to better understand the system's function and its robustness or capacity to adapt to changing conditions and disturbances. Table 28.2 presents a few key fundamental network metrics; more in-depth discussions can be found in the literature on networks (Newman, 2003; Strogatz, 2001) and their application to community ecology (Bersier et al., 2002; Blüthgen et al., 2006; Delmas et al., 2019; Proulx et al., 2005), conservation biology (Dale & Fortin, 2010; D'Aloia et al., 2019; Galpern et al., 2011; Rayfield et al., 2011) and social (Bodin et al., 2006) and social-ecological systems (Janssen et al., 2006). Most measures described in Table 28.2 are general and apply to all types of networks, emphasizing the universality of many network metrics. However, a small number are specific to certain types of networks. For example, specialization is a measure used in bipartite networks, whereas connectivity is used in spatial networks. Network measures are termed qualitative when they apply to binary networks, i.e., networks with unweighted links that only report the presence or absence of interactions, or quantitative when they apply to weighted networks in which links represent the strength or frequency of interactions. Table 28.2 largely focuses on qualitative measures but includes some quantitative measures, e.g., specialization.

The most general measures used to describe a network are its *order*, meaning the number of nodes in the network, and its *size*, which is the number of links. These descriptors already provide an idea of the extent and possible complexity of the network. The average number of links per node measures the *density* of the network. In social webs, a high density is often associated with a better exchange of information among actors. This can facilitate the development of new ideas and also improve collective actions in natural resource governance (Bodin et al., 2006). Conversely, an extremely dense network of actors can homogenize information and impede the development of new knowledge. It can also be associated with a reduced diversity of management practices that could lead to lock-in and limit the capacity of actors and organizations to come up with novel strategies to adapt to changing conditions (Bodin et al., 2006; Janssen et al., 2006).

A measure similar to linkage density is *connectance*, the proportion of potential interactions that occur. Connectance is the term used to determine species interactions within ecological networks. It can be a good indicator of the sensitivity of ecological communities to disturbances resulting in the loss of species (Dunne et al., 2002b; Montoya et al., 2006). Connectance is also associated with community dynamics and may be used to understand variations in population density or infer potential indirect interactions (Van Veen et al., 2008). A spatial analog of connectance is functional *connectivity*, which applies to species-habitat and forest-stand networks. Functional connectivity is a species-specific measurement, as species perceive forest fragmentation differently depending on their movement ability. Multiple indices of connectivity exist, all with the general purpose of determining the availability **Table 28.2** Measures to quantify network structures and the corresponding illustrations. **a** Two networks having the same order, but the smaller-sized network (*right*) has a smaller linkage density and connectance but a longer diameter (depicted by the number of links separating the two blue nodes); **b** two spatial networks of the same order but one (*left*) has a larger size and a higher connectivity. **c** The yellow node has a lower degree than the red node. The vulnerability (*number of blue links*) of the red node is identical to its generality (*number of purple links*). **d** Pink nodes are generalists, whereas blue nodes are specialists. **e** The red node (*right*) has a low clustering coefficient, whereas the red node (*left*) has a high clustering coefficient; thus, it forms a clique with its neighbors. **f** The red node has the highest betweenness centrality in this network. **g** The degree distribution is homogeneous when all nodes have similar degrees (*left*) and is heterogeneous when degrees vary among nodes. **h** The bipartite network (*left*) has a nested structure contrary to that on the right. **i** A high modularity network (*left*) contains six modules, whereas the other network (*right*) lacks a modular structure. This table constitutes a nonexhaustive list of measures. Interested readers should consult references cited in the main text for a deeper exploration of network measures

#### **Table 28.2** (continued)

of habitat for a given species (Rayfield et al., 2011). Therefore, these indices are modulated not only by the number of patches and their connections but also by their area. For example, the probability of connectivity index is a quantitative measure that corresponds to the probability that two individuals randomly placed in habitat patches across the landscape can reach each other (Saura & Pascual-Hortal, 2007).

Functional connectivity measures are useful for conservation planning, such as designing reserve networks (D'Aloia et al. 2019; James et al., 2005; Saura & Pascual-Hortal, 2007) or evaluating changes in forest connectivity over time (Saura et al., 2011). They may also be used when planning harvesting operations. Ruppert et al. (2016) developed a heuristic procedure to schedule timber harvesting on the basis of a trade-off between wood volume and habitat connectivity for the woodland caribou (*Rangifer tarandus caribou*). Tittler et al. (2015) compared the habitat connectivity of various wildlife species across management strategies that differed in their distribution and aggregation of forest cuts. Functional connectivity is also considered a critical component of forest resilience (Box 28.2; Aquilué et al., 2020; Craven et al., 2016; Mina et al., 2021). High connectivity implies that source–sink dynamics may be possible in a fragmented forest landscape whereby disturbed forest patches can regenerate by receiving seeds from unaltered patches (Craven et al., 2016).

The variations in patch centrality in a spatial network of forest patches across increasing levels of timber harvesting; **a** no harvesting, harvesting at **b** 5%, and **c** 10% tree cover. Nodes are colored according to the size of their corresponding forest patch. The diameter of each node is proportional to its betweenness centrality. Links between patches are directed and weighted according to the tree species composition within each node as well as the seed dispersal capacity of each tree species

Spatial networks can guide landscape-scale forest management. Aquilué et al. (2020) used a network approach to model fragmented forest patches in a rural landscape in central Québec, southeastern Canada, and explore how connectivity among patches varied according to different management strategies—functional enrichment of current forest patches, plantations in newly created forest patches—and under different disturbance scenarios— timber harvesting, drought-induced mortality, and pest outbreak. Interested readers should read Chap. 31 for a discussion of the effect of functional enrichment on the resilience of fragmented landscapes.

The above figure illustrates how tree harvesting affects the betweenness centrality of forest patches. Indeed, cutting trees has the effect of removing small patches and reducing the flux of seeds that can travel among patches, thereby affecting the entire functional connectivity of the landscape. As a result, the importance of each patch in maintaining connectivity is altered by disturbance. This figure also illustrates that small patches (*dark color*) can have a high centrality value (*large diameter*), emphasizing that the contribution of a forest patch to the connectivity of the landscape is not simply based on its surface area.

The *diameter* of a network is the maximum number of links between any two network nodes and thus measures the extent to which nodes are accessible to each other (Janssen et al., 2006). The small diameter of a habitat network can indicate its susceptibility to the rapid spread of an invasive plant (Minor et al., 2009). In organizational networks, a small diameter implies the existence of efficient channels to diffuse information (Bodin et al., 2006). Although a short diameter may be correlated with network density, this is not always the case. Linkage density does not account for how links are distributed among nodes. Therefore, it is possible to have a dense network characterized by a large diameter whenever nodes are distributed in a few well-connected clusters that are isolated from each other (Janssen et al., 2006). In social networks characterized by such a topology, shared information will tend to remain within clusters.

Network analysis allows for the identification of nodes that play a key role in structuring the system. The *degree* is a node-level measure that, in an undirected network, corresponds to the number of links that connect a node. In directed networks, the degree can be decomposed into in-degree and out-degree. In food webs, the former is an indication of the vulnerability of a species, i.e., the number of predators, whereas the latter relates to its generality, i.e., the number of resources (Delmas et al., 2019). In a bipartite network, such as pollination, frugivore, and host–parasitoid networks, a similar concept is that of *specialization*, which corresponds to the diversity of interacting partners of a species (Blüthgen et al., 2006; Chama et al., 2013; Gómez-Martínez et al., 2020). Gómez-Martínez et al. (2020) found that the level of specialization in bumblebee pollination networks decreased with the increased fragmentation of the surrounding forest landscape. Another related descriptor used in nest web studies is the *species importance index*. When measured for a tree species, this index corresponds to the proportion of bird species that use the particular tree species' cavities relative to the number of other tree species used by the same bird species. Identifying keystone tree species that cavity users and excavators routinely use is essential to define more specific conservation guidelines (Ruggera et al., 2016).

The degree of a node is, therefore, a measure of its influence on other nodes and is one of multiple measures assessing the *centrality* of a node. For instance, in studying the mycorrhizal networks of interior Douglas fir (*Pseudotsuga menziesii*), Beiler et al. (2015) found that large trees had a higher *degree centrality* in xeric plots compared with mesic plots. This analysis suggests that the role of large trees in facilitating the survival and productivity of newly established seedlings through shared myccorhizal fungi—is more important under water-deficit conditions.

In social webs, the organization having the highest degree can play a determining role in coordinating a group of organizations with diverging opinions on the best risk mitigation strategy, e.g., forest fire, toward a consensus (Bodin et al., 2006; Hamilton et al., 2019). Yet, a node with a low degree can also exert a central importance within the network if, for example, its position connects clusters of nodes that would otherwise be isolated. Such nodes are said to have a high *betweenness centrality*. Actors or organizations that occupy these bridging positions in social webs are essential for developing trust among parties holding conflicting views. In species-habitat and forest-stand networks, determining patches of high betweenness centrality helps identify patches that are not necessarily large but that still have a high conservation value because they enable wildlife species to move across the landscape from one region of well-connected patches to another (Aquilué et al., 2020; Gurrutxaga et al., 2011). A spatial network is more vulnerable to the destruction of nodes having a high betweenness centrality because their loss can cause the fragmentation of the landscape into unconnected components (Box 28.2; Aquilué et al., 2020). This destruction could result, for example, from harvesting, pest infestation, or forest fire.

Different measurements can provide information about the possible asymmetric distribution of interactions within networks. The simplest approach is to derive the frequency distribution of degrees within a network, i.e., its *degree distribution*, which describes the level of degree heterogeneity. For example, scale-free networks (Fig. 28.1c) are characterized by a few highly connected nodes and a large number of poorly connected nodes. In one example, the degree distribution of the mycorrhizal networks of Douglas-fir trees followed a scale-free distribution (Beiler et al., 2010). The large mature trees in a plot had the most connections, suggesting that such networks are robust to the random loss of trees but fragile to the loss of large trees with consequences for the regeneration of the entire community of connected trees (Beiler et al., 2010).

In weighted networks, one can measure the interaction diversity—a Shannon diversity of links—to quantify how degrees are distributed among nodes. For example, Cockle and Martin (2015) found that the interaction diversity of a nest web increased during a mountain pine beetle outbreak because the greater availability of cavity trees allowed for a wider variety of excavators and new opportunities of interactions with secondary cavity nesters.

The *clustering coefficient* of a node measures the extent to which neighbors of that node are closely connected. In social webs, interconnected nodes with high clustering coefficients are said to form a *clique*. The formation of cliques, or clusters, results from the tendency of social partners to interact, a property of social interactions called *transitivity*. The presence of clusters may help maintain a heterogeneity of knowledge and experiences across the network. This may prove essential for innovation and adaptation to novel environmental conditions (Bodin et al., 2006).

*Modularity* measures the extent to which a network is divided into modules of well-connected nodes (also called compartments). Modularity is thus a concept similar to clustering. But while clustering applies to neighboring nodes, modularity is measured at the scale of the entire network (Delmas et al., 2019; Guimerà & Amaral, 2005). The modularity of a nest web, for example, can indicate whether a conservation strategy for a particular tree species will have a positive influence on an entire bird community of cavity excavators and nesters (if no modules are present) or whether strategies focusing on tree species in other modules are needed (Ruggera et al., 2016). For example, in analyzing the nest web of an Argentinian tropical forest, Ruggera et al. (2016) found that woodpeckers and nonexcavator birds formed distinct modules because the former interacts with both living and standing dead trees. In contrast, the nonexcavator birds use only decay-formed cavities in living trees. Consequently, they suggested that conservation efforts for cavity-nesting birds should focus on standing dead trees as much as on certain alive tree species. In species interaction networks and in spatial networks, a certain degree of modularity is beneficial to the system's stability or resilience because it impedes the negative cascading effects of species' extinction or prevents disturbances from rapidly propagating across the network of forest patches (Messier et al., 2019; Stouffer & Bascompte, 2011).

*Nestedness* is a characteristic of bipartite networks in which specialist species interact with a subset of the group of species with which generalists interact (Almeida-Neto et al., 2008; Delmas et al., 2019). Devoto et al. (2011) found strong nestedness in the moth pollination network of a boreal pine forest, which was associated with the dominance of a small core of generalist species that also interacted with the more specialized species. This asymmetric pattern made this hub of species, as well as the pollination service they provided, vulnerable to poor weather conditions.

Networks can also be analyzed by measuring the frequency with which different *motifs* appear in their architecture (Delmas et al., 2019). Motifs are smaller subsets of interacting nodes that are viewed as the building blocks of networks (Table 28.3; Milo et al., 2002). By simulating random networks that conserve some key properties of the observed network, e.g., order, size, and connectance, one can determine whether a particular motif occurs more frequently in the network than what would be expected by chance (Bodin & Tengö, 2012; Robins et al., 2007). In species interaction networks, motifs can be used to derive the different roles that individual species play in a network from their position in motifs (Stouffer et al., 2012). For example, Baker et al. (2015) found that despite variability in species composition in a host– parasitoid community with time and along a gradient of forest fragmentation, the role of species remained largely stable. In social webs, the analysis of motifs can be used to understand the relationships between structuring interactions and the ability of the system to adapt their management of natural resources. For instance, Fischer and Jasny (2017) found that homophily was a strong structuring pattern in the network formed by organizations concerned about increased wildfire risk. In this example, homophily may insulate organizations from being exposed to a diversity of ideas, thereby impeding their capacity to develop novel management strategies. In a socialecological network that combined fire transmission (ecological links, see Table 28.1) and coordination of fire risk mitigation (social links), Hamilton et al. (2019) found **Table 28.3** Examples of possible motifs for different categories of network. Species interaction networks: **a** apparent competition of two consumers for a single resource; **b** a linear three-level food chain; and **c** omnivory in a three-level food chain. Social networks: **d** reciprocal interactions between three actors; **e** an actor acts as an intermediate between two actors that are otherwise disconnected; and **f** two disconnected actors report to a third that exerts a leadership role. Motifs of natural resource access in social-ecological networks (from Bodin & Tengö, 2012): **g** each actor manages their own resource independently even if their resources are ecologically linked, e.g., a spreading disturbance; **h** one actor depends on the other actor for access to the resource; **i** both actors compete for access to a single resource and are not engaged in any dialogue for co-management

that actors favored interactions with their immediate geographic neighbors, which constitutes an important challenge for the large-scale governance of wildfire risk.

### **28.4 Discussion**

### *28.4.1 Challenges*

The use of networks in forest ecology and management presents multiple challenges, the most important being the difficulty in identifying appropriate nodes and links. Creating species interaction networks requires intensive sampling to obtain highresolution data. For instance, nest webs are constructed by identifying cavity-bearing trees and inferring excavator species by relying on the correlation between their body size and the diameter of cavity entrances. Interaction between tree and bird species is then determined by routinely inspecting in the field cavities using a camera system to observe signs of breeding or roosting (eggs, feathers, nestlings, etc.), which is labor intensive (Cockle & Martin, 2015; Ouellet-Lapointe et al., 2012; Ruggera et al., 2016). Investigating mycorrhizal networks requires sampling needles and cambium tissue from study trees, as well as an intensive sampling of the forest floor to collect tuberculate mycorrhizae (Beiler et al., 2010). Moreover, the identification of pollinator–plant interactions requires field observations of flower visits (Gómez-Martínez et al., 2020; Memmott, 1999) or the capture of pollinator organisms to identify pollen on their body and quantification of the interaction by counting pollen grains (Devoto et al., 2011). Similarly, host–parasitoid interactions are determined by collecting host organisms in the field and then rearing parasitoids in the lab (Cagnolo et al., 2011; Van Veen et al., 2008). Accurate identification of species may rely on DNA barcoding, especially in species-rich systems where morphologically similar species abound (Smith et al., 2011). Obviously, reconstructing species interaction networks is sensitive to sampling efforts such that abundant species may receive more attention than rare ones (Cagnolo et al., 2011; Van Veen et al., 2008).

In spatial networks, nodes are identified from raster images, such as remotesensing data, by aggregating adjacent cells that satisfy environmental criteria to be considered as forest or habitat patches, e.g., forest cover type and age, tree density (Bunn et al., 2000). Patches may be easily identified in landscapes presenting a dichotomous vegetation cover, such as fragmented forests in agricultural landscapes or urban settings. However, this task is more difficult in heterogeneous and continuous forest landscapes and for wildlife species whose habitat includes a diversity of cover types with varying preferences, e.g., the woodland caribou (*Rangifer tarandus caribou*; Galpern et al., 2011; O'Brien et al., 2006). In these cases, edge detection methods can be used to delineate patches from the matrix (Fortin, 1994). Moreover, field observations and expert opinions may be necessary to make certain assumptions regarding how organisms interact with their environment, such that cover types and patches can be ranked according to their quality or relative use by the species of interest (O'Brien et al., 2006; Pascual-Hortal & Saura, 2007; Saura & Pascual-Hortal, 2007).

Links in spatial networks are generally identified by least-cost paths between nodes. This approach assumes that organisms travel between nodes using the most risk-free and efficient route, which may not always be the case for organisms characterized by anisotropic or passive dispersal. Moreover, in networks where links denote the movement of wind-dispersed seeds or certain bird and insect species, least-cost paths can be estimated using Euclidean distances between patches. However, for most wildlife species, links will correspond to nonlinear paths that consider the environmental heterogeneity of the matrix and the biological traits that influence their dispersal ability (Fall et al., 2007). Therefore, the determination of least-cost paths can be sensitive to the values and resolution of the resistance surface (Etherington, 2016; Rayfield et al., 2010), which, in turn, requires intensive parameterization efforts; thus, the results may be prone to bias (Etherington, 2016).

Defining and identifying nodes and links is also a pervasive challenge when translating social and social-ecological systems into networks. Not unlike ecological data, collecting social data to identify entities and their interconnections involves substantial investment and is prone to errors. For example, a common approach to identify nodes in social networks is snowball sampling, an approach based on multiple steps (Doreian & Woodard, 1992; Fischer & Jasny, 2017; Hamilton et al., 2019; Knoot & Rickenbach, 2014). In the first step, a subset of key actors (single individuals or organizations) is selected and interviewed to obtain the names of other actors with whom they interact. In the second step, these additional actors are then interviewed to obtain yet again other names. The process continues until no new actors are identified (Fischer & Jasny, 2017). Depending on the number of sampling waves or the depth of the interviews, this approach can be time-consuming and subject to selection bias, e.g., well-connected individuals being identified more easily, and bias in reporting (or not) certain conflicting relationships, e.g., between opposing individuals or organizations (Doreian & Woodard, 1992).

Additionally, the construction of social-ecological networks often requires a certain level of aggregation of the ecological or social units determined by the system and the question under study. For example, studying coordination between forest management organizations may require that ecological nodes be scaled up to represent forests within the jurisdictional boundaries over which these organizations interact, thereby losing the local environmental specificity and limiting the utility of the network approach for managers working at local scales (Hamilton et al., 2019; Sayles et al., 2019). Bodin et al. (2019) proposed that a starting point in conceptualizing nodes and links is defining the social-ecological interdependencies central to the investigated management issue. Focusing on these connections will facilitate identifying the most relevant nodes and choosing the appropriate level of aggregation. Likewise, creating a network necessarily requires bounding the system under study. This bounding imposes an artificial frontier with the implicit assumption that connections beyond its limit have negligible impacts on the system's structure and function (Sayles et al., 2019). Given that any network analyses are sensitive to the number of nodes and links, which are themselves the result of the bounding choice, Sayles et al. (2019) suggested that studying the effects of different bounding approaches is needed to advance the field of social-ecological networks.

Common natural resource governance challenges occur in a variety of ecosystems, contexts, and scales (Ostrom, 2009). One goal of social-ecological network research is to understand the causal pathways between network structures and how these challenges emerge or are solved (Bodin & Tengö, 2012; Groce et al., 2019; Guerrero et al., 2020; Janssen et al., 2006). However, because of the numerous methodological choices involved in translating a social-ecological system into a network, the resulting network analysis tends to be specific to the studied system, limiting the ability to compare studies (Bodin et al., 2019; Young et al., 2006). Recently, Bodin et al. (2019) emphasized the need to develop a set of research design guidelines, applicable across contexts and scales, to facilitate synthesis and gain insights from diverse studies. These authors also suggested that advancing social-ecological network research requires a systematic classification of different basic causal relationships between simple patterns of network structure and environmental outcomes to help researchers make clearer assumptions about causality when more complex pathways are operating in their system (Bodin et al., 2019; Groce et al., 2019).

### *28.4.2 Benefits and Potential of the Network Approach in Forest Management*

Managing for forest resilience has become imperative in a changing environment (Gauthier et al., 2015; Trumbore et al., 2015). Many symptoms of climate effects, invasive insects and diseases, and expanding land use are already evident within forest ecosystems (Hansen et al., 2013; Pr˘av˘alie, 2018). Moreover, due to global change, boundary conditions are shifting for many ecological processes, including disturbance regimes, species ranges, phenology, and carbon flux dynamics (Ramsfield et al., 2016; Seidl et al., 2017; Vose et al., 2019). Transition zones and locations where species exist at the limits of their current ecological tolerances, such as portions of the hemiboreal ecotone of eastern North America, may be particularly sensitive to these shifts (Thom et al., 2019). Management decisions that we make today must account for the uncertainty in future environmental threats, and they must anticipate uncertainty related to the rapidly changing economic and social context affecting demand for forest services and products. Network theory could help forest managers identify sensitivities and vulnerabilities linked with these changes and mitigate their effects accordingly, for instance through adaptive forest management (Gauthier et al., 2008; Millar et al., 2007). Moreover, the application of network theory could also likely be a key for monitoring biodiversity and projecting the future state of biodiversity in managed forests (Mina et al., 2021).

Recent applications of network theory have been proposed for evaluating and managing the resilience of large tracts of forests to global change stressors (Box 28.2; Aquilué et al., 2020, 2021; Mina et al., 2021). In these approaches, resilience is viewed as a multidimensional concept combining biodiversity and network topology measures likely to positively influence the capacity of spatial forest networks to cope with future disturbances (Messier et al., 2019). More precisely, resilience accounts for functional redundancy, the functional response diversity of forest metacommunities (Mori et al., 2013), their network connectivity, mean centrality, and modularity (Gonzalès & Parrott, 2012). Management strategies that modify one or more of these resilience-based properties can then be tested against scenarios of climate change and disturbance, e.g., drought, insect outbreak (Aquilué et al., 2020, 2021; Mina et al., 2021). For example, this approach can determine whether establishing plantations of functionally rare species or enriching forest stands to increase the variety of response traits, at locations that also improve forest network connectivity, provide forest ecosystems the ability to resist or adapt to future environmental conditions. One can then use this approach to identify the management strategy that best conserves the forest landscape under a range of possible but uncertain disturbances.

Network theory is useful for managing ecological recovery from natural disturbances, especially in the context of climate change where many disturbances are expected to increase in occurrence, severity, and size (Pr˘av˘alie, 2018). For example, following large forest fires, seeds, fungal spores, and organisms often disperse from natural fire refugia (nodes in spatial forest networks) and then interact demographically as the landscapes recover through succession (Keeton & Franklin, 2004; Krawchuk et al., 2020). In landscapes where fire management is used to mitigate fire risk, network theory can assist in designing strategies, e.g., location and size of prescribed burns, that preserve habitat connectivity for wildlife species (Sitters & Di Stefano, 2020).

Spatiotemporal and spatial networks employed together with simulation models could help predict shifting conditions in forest ecosystems to adapt management practices accordingly. For example, Huang et al. (2020) used spatiotemporal networks together with species distribution models to determine how future climates will affect habitat availability for terrestrial mammals experiencing range shifts in North America. Future applications of networks are, therefore, expected to be used in conjunction with other models to better integrate changing environmental conditions and ecological processes occurring at different spatial or temporal scales. For example, Mina et al. (2021) coupled a spatial network approach with a spatially explicit simulation model of forest dynamics (LANDIS-II, Mladenoff, 2004) to determine how climate-induced changes in forest cover influence landscape connectivity. Wildemeersch et al. (2019) used a network-of-networks approach to simulate forest pest outbreaks. Their model included a landscape-scale network of forest patches as well as a stand-scale network within each patch. The small scale captured the local pest pressure, whereas the large scale captured the influence of landscape connectivity on the spreading behavior of the pest.

Networks are promising tools for multifunctional forest management because they effectively integrate the interactions between social and ecological elements. Spatial networks can help assess trade-offs between conflicting management goals. For example, they can be used to determine management strategies that account for ecological connectivity to satisfy conservation and economic targets (Ruppert et al., 2016) or optimize the provision of multiple ecosystem services (Vogdrup-Schmidt et al., 2019). Moreover, social-ecological networks can be used to identify linkages that would foster coordinated efforts in the management of natural disturbance risks (Hamilton et al., 2019), such as reducing fire risk hazards within the wildland–urban interface (Keeton et al., 2007; Vilà-Vilardell et al., 2020).

To summarize, this chapter has demonstrated the richness and flexibility of the network framework for forest management. Further applications of network theory to forest management will necessitate an adaptive approach, accounting for shifting dynamics and interactions among nodes, be they ecological or social. Network analysis is a powerful tool for identifying sensitivities and vulnerabilities within networks. It may prove invaluable in helping forest managers to better anticipate and adapt to global change.

### **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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### **Chapter 29 Land and Freshwater Complex Interactions in Boreal Forests: A Neglected Topic in Forest Management**

### **Guillaume Grosbois, Danny Chun Pong Lau, Martin Berggren, Miguel Montoro Girona, Willem Goedkoop, Christian Messier, Joakim Hjältén, and Paul del Giorgio**

**Abstract** Aquatic and terrestrial habitats are interdependent components of the boreal forest landscape involving multiple dynamic interactions; these are manifested particularly in riparian areas, which are key components in the forest landscape. However, this interdependence between aquatic and terrestrial habitats is not adequately accounted for in the current management of forest ecosystems. Here we review the impacts of land disturbances on the optical and physicochemical properties of water bodies, aquatic food web health, and the ecological functioning of these freshwaters. We also describe how freshwaters influence the adjacent terrestrial ecosystems. A better understanding of these dynamic biotic and abiotic interactions between land and freshwater of the boreal forest is a first step toward including these freshwaters in the sustainable management of the boreal forest.

G. Grosbois (B) · M. M. Girona

M. M. Girona e-mail<EMAIL_ADDRESS>
G. Grosbois · D. C. P. Lau · W. Goedkoop Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, P.O. Box 7050, SE-75007 Uppsala, Sweden e-mail<EMAIL_ADDRESS>
W. Goedkoop e-mail<EMAIL_ADDRESS>
D. C. P. Lau Department of Ecology and Environmental Science, Umeå University, Linnaeus väg 6, 90187 Umeå, Sweden

© The Author(s) 2023 M. M. Girona et al. (eds.), *Boreal Forests in the Face of Climate Change*, Advances in Global Change Research 74, https://doi.org/10.1007/978-3-031-15988-6\_29

Groupe de Recherche en Écologie de la MRC-Abitibi, Institut de recherche sur les forêts, Université du Québec en Abitibi-Témiscamingue, Amos Campus, 341, rue Principale Nord, Amos, QC J9T 2L8, Canada e-mail<EMAIL_ADDRESS>
### **29.1 Introduction**

The boreal forest, the world's largest land biome, is characterized by a high density and diversity of freshwater environments. These water bodies form a complex aquatic network that interacts dynamically with the surrounding terrestrial environment. However, scientists have traditionally kept to their respective areas of interest, and research in the respective fields of terrestrial and aquatic ecology has remained separate. Consequently, there is a knowledge gap in our understanding of the interactions between aquatic and terrestrial habitats as well as those processes specific to the aquatic–terrestrial ecotones (Hjältén et al., 2016). Nevertheless, environments such as the littoral zone in lakes and the shoreline area in forests are habitats rich in biological diversity and are sites where essential processes occur, e.g., the primary production of macrophytes and benthic algae or tree species associated with wet areas. Land– water interactions, comprising energy and matter fluxes, occur mainly in this ecotone (Fig. 29.1). The largest fluxes from land toward freshwater are the dissolved and particulate organic and inorganic matter carried by surface runoff, groundwater, and wind (Vander Zanden & Gratton, 2011). The freshwater to land fluxes are smaller in volume but greater in energy and are higher in nutritional quality. These fluxes follow animal movements, such as insect emergence or terrestrial predation on aquatic prey.

Both types of fluxes are intimately entangled, connecting the land and water environments in the boreal forest (Baxter et al., 2005); however, these habitats are commonly viewed as separate ecosystems and are thus managed by distinct environmental institutions or agencies. The aquatic habitats are therefore usually not directly included in sustainable forest management. This view may pose risks of underestimating the complexity of the structure and functioning of the forest and

M. Berggren

M. M. Girona · J. Hjältén

Restoration Ecology Group, Department of Wildlife, Fish, and Environmental Studies, Swedish University of Agricultural Sciences, 901 83 Umeå, Sweden e-mail<EMAIL_ADDRESS>
M. M. Girona · C. Messier Centre d'étude de la forêt/Centre for Forest Research, Université du Québec à Montréal, P.O. Box 8888, Stn. Centre-Ville, Montréal, QC H3C 3P8, Canada e-mail<EMAIL_ADDRESS>
C. Messier

P. del Giorgio

Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, S-223 62 Lund, Sweden e-mail<EMAIL_ADDRESS>
Département des Sciences Naturelles, Institut des Sciences de la Forêt Tempérée, Université du Québec en Outaouais, 58 Rue Principale, Ripon, QC J0V 1V0, Canada

Department of Biological Sciences, Université du Québec à Montréal, P.O. Box 8888, Stn. Centre-Ville, Montréal, QC H3C 3P8, Canada e-mail<EMAIL_ADDRESS>
**Fig. 29.1 a** Connectivity between the aquatic and terrestrial habitats of the boreal forest; **b** forest harvesting in a lake watershed. *Photo credits* Miguel Montoro Girona and Janie Lavoie

hinder successful management (Gauthier et al., 2009; Messier et al., 2013). For an improved representation of reality, we highlight in this chapter that terrestrial and freshwater habitats are integral parts of the same boreal forest ecosystem, and, as such, we will no longer refer to terrestrial and aquatic ecosystems as separate entities.

One way to consider land–water interactions is by implementing riparian buffer strips, which are commonly included in forest management. Although the direct and indirect effects of these buffer strips on aquatic habitats have been studied extensively (Lidman et al., 2017a, b; Peterjohn & Correll, 1984), there remains an absence of information of the site-specific role of riparian forests, e.g., on different types of forest soil, with different slopes or different stand compositions (Kuglerová et al., 2014); this knowledge gap impedes the advancement of best practices in boreal forest management (Kuglerová et al., 2017). Furthermore, the effectiveness of riparian forests in moderating the impacts of harvesting on the terrestrial part of the boreal forest has rarely been tested. The construction of roads related to forestry practices and the type and timing of harvesting may affect how forestry machinery modifies stream habitats, both directly and indirectly, by increasing turbidity (Reiter et al., 2009). Extensive cutting may raise the water table and increase the amount of organic matter exported to aquatic habitats (Laudon et al., 2009; Sun et al., 2000). In addition, increases in the number of water pools in the tracks of the machines may increase mercury methylation and the related contamination of the landscape (de Wit et al., 2014; Sørensen et al., 2009). Fertilization, liming, and fire control may also affect the leaching of nutrients and other constituents into the water (Bisson et al., 1992; Degerman et al., 1995). This chapter therefore aims to describe the existing natural and anthropogenic interactions between the aquatic and terrestrial habitats of the boreal forest and highlight the importance of integrating the aquatic environments into the sustainable management of the forest. We demonstrate that revising the existing forest management paradigm, which currently considers land and freshwater habitats as isolated ecosystems, is essential to acknowledge the interdependence between these components and enhance forest management success.

### **29.2 The Browning of Boreal Freshwaters**

A portion of the brown color of boreal lakes and rivers is caused by dissolved organic matter (DOM) leaching from the soil and litter of forests (Roulet & Moore, 2006). The DOM concentration, including the dissolved organic carbon (DOC) component, has increased in freshwaters during the last decades to shift the water color of boreal water bodies toward brown, a phenomenon called *browning* or *brownification*  (Fig. 29.2; Lennon et al., 2013). The browning of freshwater is a serious concern for many ecosystem services, including drinking water supply (Haaland et al., 2010), as treatment of brown water rich in DOM is costly and may produce chlorinated carcinogenic compounds (Richardson et al., 2007). Whereas the extent and intensity of browning remain poorly studied at the global scale, the most dramatic changes in DOC concentrations (>0.15 mg·L−1·yr−1) have been reported in temperate and boreal regions (de Wit et al., 2016; Monteith et al., 2007).

Browning is associated with natural and anthropogenic changes in land cover as well as other factors that modify the amount and nature of organic matter exported from the watershed into freshwater habitats (Kritzberg, 2017). In boreal forests, increasing DOC concentrations in lakes is a direct consequence of forest harvesting. Harvesting raises the water table, thereby increasing the hydrological connectivity between shallow DOC-rich soils and the recipient freshwaters (Glaz et al., 2015). Organic matter export may also increase because of greater terrestrial primary production in the forest (Larsen et al., 2011), enhanced by increased atmospheric carbon dioxide levels (Campbell et al., 2017). Other factors include increased nitrogen deposition (Rowe et al., 2014), higher precipitation (Hongve et al.,

**Fig. 29.2** Lake water with low (l*eft*) and high (*right*) concentrations of dissolved organic matter. *Photo credit* Guillaume Grosbois

2004), and greater export of iron, which can form stable, highly colored complexes with DOM (Weyhenmeyer et al., 2014). Several recent studies have attributed the DOC increase to recovery from atmospheric acid deposition, which enhances organic matter solubility and, therefore, the mobility of DOC from forest to aquatic environments (Meyer-Jacob et al., 2019). These researchers questioned whether the current browning of freshwaters reflected a re-browning to reach past levels of water color or whether this represented a new trend toward unprecedented browning (Meyer-Jacob et al., 2020). Both trends have been observed, and whereas re-browning is associated with regions having received moderate to high levels of acid deposition, higher than preindustrial DOC concentrations have been recorded in regions characterized by low levels of acid deposition (Meyer-Jacob et al., 2019). Thus, recovery from acidification is insufficient to explain browning in all lakes, and the trends in water color may be more related to changes in land use, e.g., from agriculture to modern forestry (Kritzberg, 2017).

An increase in DOM in freshwaters can drastically change the structure and functioning of inland waters having a darker water color by increasing the surface water temperature and modifying thermal stratification (Williamson et al., 2015). Although higher temperatures can potentially enhance algal production, browner waters also attenuate the penetration of photosynthetically active radiation and therefore diminish a water body's suitability for aquatic primary production (Creed et al., 2018). Increasing DOM in freshwater strongly affects microbial processes by shifting basal pelagic production from phytoplankton to bacteria (Jansson et al., 2000). Although the biodegradation coefficient per unit of DOM is reduced in brown lakes and rivers (Berggren & Al-Kharusi, 2020; Berggren et al., 2020), the absolute amount of organic carbon that is assimilated and metabolized by microorganisms increases with greater DOM concentrations (Lennon, 2004). This has consequences for higher trophic levels, affecting zooplankton production and fish survival and growth (Hedström et al., 2017; Taipale et al., 2018). Browning therefore radically alters the functioning and subsidies of aquatic food webs (Hayden et al., 2019).

In the near future, green (eutrophication) and brown (browning) lakes are expected to replace blue lakes in the boreal forest (Leech et al., 2018). The increase in DOM export in some regions is clear, particularly in northern ecosystems where higher temperatures and shorter winters will allow a greater water movement through litter and soil and thus increase DOM export to freshwaters (Laudon et al., 2012). These major changes in DOC could modify the overall C budget of the boreal forest, and future research must focus on this issue to better understand how these new conditions will influence the future C sinks and sources.

### **29.3 Nutrients in Freshwaters: Eutrophication and Oligotrophication**

Lake and river environments depend strongly on nutrient export from their associated watersheds (Hynes, 1975). In boreal forests, nutrients are fixed via atmospheric deposition, N-fixation by cyanobacteria, or weathering, and a portion is exported to aquatic ecosystems through runoff (Hall, 2003). Nutrients are not only transported by freshwaters but are also physically, chemically, and biologically processed. One way of estimating these biotic contributions is to experimentally manipulate the watershed, for example by preventing acid rain (Hultberg & Skeffington, 1998) or harvesting the trees (Likens et al., 1970). During the Hubbard Brook Experiment, the cutting of all watershed vegetation demonstrated the key role of terrestrial vegetation in retaining nutrients within the watershed (Likens et al., 1970). Moreover, nutrient export to streams and lakes drastically increased after tree harvesting; this highlighted the terrestrial origin of most nutrients. Rivers and lakes are, therefore, highly dependent on watershed sources of nutrients. The Global Nutrient Export from Watersheds (NEWS) model estimated that, at the global scale, the export of dissolved inorganic nitrogen (N) and phosphorus (P) is dominated by anthropogenic sources, whereas 80% of the dissolved organic N and P is dominated by natural sources (Seitzinger et al., 2005). Bernhardt et al. (2003) also demonstrated that rivers react to major forest disturbances, reducing nitrogen concentrations downstream of the disturbance. Thus, aquatic environments are strongly dependent on the export of forest nutrients and are biogeochemically reactive habitats where algae and grazers/predators have a high capacity for assimilating and storing nutrients.

Increased exploitation of natural resources, e.g., forestry and mining, and associated infrastructure development in boreal regions has increased the leakage of carbon, nitrogen, and phosphorus from catchments into watercourses (Payette et al., 2001). This increase of biologically reactive dissolved inorganic nitrogen has been measured over large areas globally and has been found to affect nutrient ratios in freshwaters and lead to lake eutrophication (Hessen, 2013). Such an increase in nutrient inputs may shift an initial N-limitation to P-limitation for primary producers and consumers; this alteration causes major changes to the base of food webs in lakes (Elser et al., 2009, 2010) and rivers (Chen et al., 2013). Additionally, terrestrial organic matter is associated with DOM, nitrogen, and phosphorus (Aitkenhead-Peterson et al., 2003), and their respective inputs increase with browning (see Sect. 29.2). Many boreal freshwater ecosystems will experience greater nutrient inputs in the future; however, the effect on the functioning of food webs remains relatively unknown. Prepas et al. (2001) demonstrated that harvesting around the headwater lakes of Alberta's boreal plain increased total phosphorus concentration in the lakes, triggering an increase in cyanobacteria and cyanotoxins. Consequently, zooplankton biomass and abundance decreased in these boreal lakes impacted by forest harvesting. Given that N and P export to freshwaters is predicted to increase over the next decades, terrestrial– aquatic interactions must be accounted for in the future management of the boreal forest.

In already-nutrient-poor systems in many boreal regions of the world, e.g., Sweden, Canada, and Finland, lakes have experienced a substantial, long-term decline in total phosphorus concentrations (2.1%·yr−1 since the 1980s), reaching values close to the detection limit; this process is referred to as lake oligotrophication (Arvola et al., 2011; Eimers et al., 2009; Huser et al., 2018). These declines are linked to the recovery from acidification, the increased climate change–induced trapping of nutrients by plant roots (Elmendorf et al., 2012), changes in watershed processes related to soil properties (Gustafsson et al., 2012), or a combination thereof. Cyanobacterial blooms are often triggered by an excess of nutrients in lakes; however, they can also be enhanced by N-limitation, as cyanobacteria can fix environmental N and are therefore more competitive than other algae (Berman-Frank et al., 2007). Diehl et al. (2018) report that cyanobacteria make up more than 50% of the biomass in shallow, epilithic biofilms in northern Swedish lakes. Cyanobacteria-dominated communities in oligotrophic waters negatively impact lake food webs through a lower trophic transfer efficiency from primary producers to consumers (Brett & Muller-Navarra, 1997). Declining total phosphorus concentrations could therefore act in synchrony with increasing DOM and suppress aquatic productivity.

When harvesting a significant area of a watershed as part of a management strategy in boreal forests, it is essential to consider the effect on the already fragile balance of nutrients resulting from browning, eutrophication, or oligotrophication.

### **29.4 Metabolism in Freshwaters: Heterotrophy Versus Autotrophy**

Inland waters process, store, and outgas most of the carbon (C) exported from land, thereby playing a significant role in the global C cycle (Battin et al., 2009; Raymond et al., 2013). These external carbon inputs can vary from being a primary energy source in freshwater metabolism (*heterotrophy*) to being less important relative to aquatic photosynthetic production (*autotrophy*). This reliance depends on many factors that determine the interactions between the aquatic and forest components of the boreal landscape, such as ecosystem size. For example, small streams connected to the landscape mainly emit terrestrially derived carbon dioxide (CO2), whereas larger rivers mostly re-emit CO2 previously fixed by aquatic primary producers (Hotchkiss et al., 2015). Inland waters are classified as net heterotrophic when metabolism based on external watershed inputs causes the CO2 emissions from aquatic organisms, i.e., respiration *R*, to be greater than aquatic gross primary production (GPP). In contrast, waters are classified as net autotrophic when the metabolism is predominantly based on aquatic production and, therefore, R is less than GPP (Jansson et al., 2000). New inputs of organic matter from the boreal forest are also a source of nutrients to the water and may increase GPP even though they may not change the net ecosystem production (NEP, i.e., GPP–R), as it may also increase R (Cole et al., 2000). New inputs from forests, such as those following forest harvesting, thus affect the basal production of aquatic ecosystems, potentially increasing heterotrophic bacterial metabolism as well as photosynthetic production. Clapcott and Barmuta (2010) demonstrated that forest harvesting increases metabolism and organic matter processes in small headwater streams; however, it remains unclear whether this is the case in larger ecosystems (Klaus et al., 2018). The impact of forest harvesting on aquatic metabolism is, therefore, very difficult to predict because organic matter inputs from the watershed are susceptible to switching from an equilibrated ecosystem, i.e., R = GPP, toward net heterotrophy when bacterial metabolism is favored but also toward net autotrophy when the associated nutrients favor photosynthetic algal production.

Inland waters are classified in terms of their trophic state as eutrophic (very productive), mesotrophic (moderately productive), or oligotrophic (unproductive) environments. The impact of forest harvesting is expected to differ according to the trophic state of lakes and rivers. Extra watershed inputs of organic matter to eutrophic lakes may increase an already high GPP but still trigger a switch of an ecosystem from a sink to a source of greenhouse gases because of increased production and diffusion of methane (Grasset et al., 2020). Greater nutrient and organic matter inputs from the catchment can unbalance autotrophy versus heterotrophy processes and lead to an accumulation of organic matter in the sediments. The degradation of this extra t-OM under anoxic conditions in the sediments is likely to produce methane (Donis et al., 2017). Mesotrophic and oligotrophic lakes can alternate from autotrophic to heterotrophic states, in different seasons, from one year to another, or after meteorological events such as storms that uncouple R and GPP (Richardson et al., 2017; Vachon & del Giorgio, 2014; Vachon et al., 2017). Given that even within the same ecosystem, C from the forest and C synthesized by algae can be used differently by bacteria—forest C for biomass and algal C for respiration (Guillemette et al., 2016)—the impact of forest harvesting on aquatic bacterial communities is difficult to predict.

Limnologists often consider terrestrial DOM (t-DOM) to be homogeneous; in reality, however, t-DOM represents a mix of material of very different origins and differing states of degradation (Berggren et al., 2010b). It includes low molecular weight compounds, such as carboxylic acids, amino acids, and carbohydrates, which bacteria easily utilize and eventually transfer to higher trophic levels (Berggren et al., 2010a). Molecules are referred to as labile, semi-labile, or recalcitrant according to how easily they can be degraded by bacteria (Kragh & Sondergaard, 2004). The terrestrial share of DOM is composed of compounds that originate from terrestrial plant tissues, and soil microorganism communities often modify these compounds before entering inland waters (Solomon et al., 2015). Generally, t-DOM is composed of humic and fulvic acids that contain aromatic hydrocarbons, including phenols, carboxylic acids, quinones, and catechol, and a nonhumic fraction characterized by lipids, carbohydrates, polysaccharides, amino acids, proteins, waxes, and resins (McDonald et al., 2004). Among these compounds, low molecular weight carboxylic acids, amino acids, and carbohydrates can potentially support all bacterial production in boreal ecosystems (Berggren et al., 2010a). The remaining incoming terrestrial organic matter is highly concentrated in tannins and represents organic compounds not degraded by microbial fauna in the soil (Daniel, 2005). Degradation processes preferentially remove oxidized, aromatic compounds, whereas reduced, aliphatic, and N-containing compounds are either resistant to degradation or tightly cycled; they therefore persist in aquatic systems (Kellerman et al., 2015). The role of allochthonous carbon in aquatic ecosystems is closely related to bacterial abundance, biomass, and production (Azam et al., 1983; Roiha et al., 2012), as most DOM decomposition is undertaken by planktonic bacteria (Daniel, 2005; Wetzel, 1975). Depending on the quality of the organic carbon, bacterial productivity might change, whereas the quantity of the organic carbon appears to define community composition (Roiha et al., 2012). Glaz et al. (2015) demonstrated that, in contrast to DOM quantity, the nature of t-DOM in lakes affected by forest harvesting did not change. Therefore, forest harvesting triggers extra inputs of DOM into aquatic environments that can directly influence and foster bacterial communities and modify the entire-lake metabolism.

### **29.5 Allochthony in Boreal Aquatic Consumers**

In aquatic habitats within forests, organic matter (OM) inputs from the adjoining terrestrial counterparts are *allochthonous*, whereas aquatic primary production is referred to as *autochthonous*. Terrestrial OM eventually enters the aquatic food webs, and its use by aquatic organisms for biomass production is referred to as *allochthony*. The significance of allochthony in supporting aquatic food webs has been shown recently for bacteria (Berggren et al., 2010a; Guillemette et al., 2016), zooplankton (Berggren et al., 2014; Grosbois et al., 2017a), invertebrates (Hayden et al., 2016), and fish (Glaz et al., 2012; Tanentzap et al., 2014).

Although all aquatic organisms may play a role in processing terrestrial OM directly or indirectly, zooplankton occupy a strategic position in aquatic food webs. Zooplankton can consume both autochthonous and allochthonous OM for biomass production and are key organisms responsible for transferring OM to higher trophic levels (Grosbois et al., 2020). Whereas earlier studies found significant trophic transfer of terrestrial particulate OM to zooplankton (Cole et al., 2006), recent studies demonstrate that this direct use of terrestrial particulate OM by pelagic food webs is rather limited (Mehner et al., 2016; Wenzel et al., 2012). However, the assimilation of terrestrial OM in aquatic food webs can follow different pathways and may begin at the lower tropic levels, i.e., through the microbial degradation of terrestrial OM. The terrestrial OM is then transferred to the trophic levels comprising ciliates, flagellates, or rotifers through their consumption of the microbes (Jansson et al., 2007; Masclaux et al., 2013). The allochthonous OM assimilated by these species is then consumed by larger zooplankton taxa that are, in turn, available for zooplanktivores, including numerous fish species, e.g., *Perca flavescens*, and invertebrates, e.g., *Chaoborus obscuripes* and *Leptodora kindtii* (Tanentzap et al., 2014). Thus, zooplankton can serve as indicators of allochthony in aquatic food webs and have been the focus of multiple studies of allochthony (Cole et al., 2011; Lee et al., 2013; Perga et al., 2006). Allochthony in zooplankton varies widely, from less than 5% (Francis et al., 2011) to 100% (Rautio et al., 2011). Although there remains some debate about the significance of the terrestrial OM contribution for zooplankton in different lake types, e.g., large clear-water lakes versus small humic lakes, it is now increasingly accepted that allochthony can often be very significant for many zooplankton taxa, especially in lakes that receive large terrestrial OM inputs, which limit light availability and aquatic primary production (Cole et al., 2011; Emery et al., 2015; Wilkinson et al., 2013).

The allochthony of benthic invertebrates in rivers and lakes has often been well defined because of the remarkable feeding adaptations of these taxa (grazers, filterers, shredders, and predators). Therefore, we can observe an allochthony gradient in these organisms, from the least allochthonous grazers to the most allochthonous shredders (Rasmussen, 2010). However, the diet of each feeding group can include a mix of food sources with organisms feeding mainly on autochthonous material and also assimilating allochthonous OM. For example, grazers can consume and assimilate terrestrial OM deposited on benthic algal mats, and filter-feeders can obtain suspended terrestrial OM. Inversely, animals feeding on allochthonous material may ingest autochthonous material, e.g., shredders can consume plant litter together with periphytic algae growing on the litter surface. The use of allochthonous or autochthonous OM by benthic invertebrates also depends on the riparian vegetation and the aquatic habitat size. The river continuum concept (RCC) (Vannote et al., 1980) describes a decreasing allochthony gradient from small forested headwater streams to large autochthonous rivers having minimal canopy cover. However, recent studies have also shown strong autochthony rather than allochthony in headwater streams (Lau et al., 2009a, b; Torres-Ruiz et al., 2007). Erdozain et al. (2019) demonstrated, contrary to RCC predictions, that allochthony in aquatic food webs is low in forest headwaters and increases with greater harvesting intensity and delivery of terrestrial OM. It is, therefore, very likely that silvicultural practices can strongly impact autochthony and allochthony in aquatic food webs.