**1.  Despite the encouraging progresses in cancer treatment, one of the core dilemmas is the current limitation in classical therapeutic modalities such as surgery, radiation and chemotherapy [7, 8]. For instance, surgery in combination with radiation treatment and chemotherapy drugs has been proved to be effective for localized cancers without metastasis and diffusion [9, 10]. Chemoradiotherapy has been considered as a synergistic anticancer remedy for locally advanced solid tumors whereas with increased damage to normal tissues and microbiota resistance [11–13]. Distinguish from the conventional cancer treatment (e.g., surgery, radiotherapy and chemotherapy), noncellular immunotherapy such as checkpoint

inhibitors (e.g., PD-1, PD-L1, CTLA4), lymphocyte-promoting cytokines (e.g., IFN-γ, GM-CSF, G-CSF), and cancer vaccines (e.g., mRNAs) has been continuously developed to fulfill the goals for cancer administration as well [14–17]. Additionally, current progress has also highlighted the feasibility of nanomaterials (e.g., organic nanomaterials, inorganic nanomaterials, organic–inorganic hybrid nanomaterials) as promising agents for cancer therapy based on the knowledge of nanobiotechnology and clinical biomedicine [18–20]. However, the significant disadvantages of the aforementioned strategies are apparent and should not be ignored including drug delivery barriers, graft-versushost disease, off-target effects and severe toxicity [5, 21, 22].

Therewith, the assumption of eradicating tumors by utilizing the human immune system has been successfully and extensively practiced for the past decades by pioneering investigators and clinicians [23]. To date, a series of cellular Immunotherapy has been identified to promote the outcomes and reduce adverse reactions of cancer patients, and in particular, those with late-stage patients with various treatment-refractory cancers [14–17]. Therefore, in this chapter, we mainly focus on the progress as well as the prospective and challenges in cellular immunotherapy for cancer treatment including the patient-specific immune cells (e.g., tumor infiltrating lymphocytes, cytokine induced killer cells), innate immunocytes (e.g., natural killer cells, dendritic cells, macrophage), adaptive immunocytes (T lymphocytes, B lymphocytes), engineered immunocytes (e.g., chimeric antigen receptor-transduced T cells, T cell receptor-transduced T cells, CAR-NK, CAR-M) [21, 24–27]. Collectively, the immune cell-mediated cancer immunotherapy has constituted a promising area of cancer biotherapy.


The LandTrendr results did not reveal any substantial differences in the types and timing of changes between treatment and control areas (Figure 5); hence did not provide evidence for an improved development of treatment than control areas. However, it should be considered that this result may be tied to the applied methodology, particularly the use of the maximum NDVI which may not fully capture effects of interventions. This may be the case if the impact of interventions show an increase in crop yield that might be reflected better as an increase in the integral of the phenological crop cycle curve (integrated NDVI) or if the impact is the reduction of eroded soil in the end of the rainy season which could lead to higher NDVI values in the end of the phenological cycle, rather than causing an increase in maximum NDVI. Future research should, provided that the data availability will be sufficient (e.g., Sentinel-2), therefore include seasonal metrics when examining changes in vegetation condition related to SLM interventions.

#### *4.2. Visual Inspections of Trends Using Google Earth*

Whereas the previously discussed results based on the analysis conducted and reported at the level of watersheds did not indicate strong differences in vegetation development between treatment and control areas, closer visual examination of the trend maps showed that human-induced land improvements can be detected from the Landsat-based approach developed, though at localised scales rather than consistently spread throughout entire watersheds. For a few major watersheds, particularly for Banja and Sekela in Amahara region, the VHR images supported that RUE increases between 2012 and 2014 were due to the establishment of tree plantations (Figure 6). Following Mekonnen et al., communal and private lands in rural areas in Amhara region have been extensively used for the expansion of Eucalyptus and Acia plantations due to the demand for wood resource [61]. Furthermore, the government has been promoting tree planting widely through the introduction of campaigns. Consequently, woodlots, home gardens, trees on cropland and farm boundary plantations have become common agroforestry practices [61] which may explain the general emergence of small tree patches in various watersheds.

Furthermore, from visual inspection it was observed that notable changes occurred outside cultivated fields as patches of increasing trends in vegetation cover, particularly at hillside locations and along streams and gullies (Figure 7). These clusters of pixels were characterised by a negative-positive trend shift (MK) and were in agreement with LandTrendr recoveries of which the largest were detected after 2010. Since in these instances VHR images could verify the regrowth of vegetation, these negative-positive trend shifts can be linked to anthropogenic land improvement where degradation previously occurred. Moreover, findings of positive development could be confirmed by results from the GFA Consulting Group performance assessment (Section 3.2).

#### *4.3. Effect of SWC Measures on Vegetation Trends*

The OLS regression results demonstrated that positive changes in NDVI could be attributed to the impact of SLMP infrastructure. Visual inspection showed that this was visible in the spatial patterns for several locations at check dams and terraces, as shown in the case examples (Figures 8 and 9). This coincides with results from a study by Ali et al. who used NDVI and other satellite derived indices to evaluate the impact of SWC measures on different land use (i.e., on cultivated land and non-cultivated land such as degraded hillsides), and found that biophysical measures had a particular high impact on non-cultivated land [13].

In general, the most significant positive changes in NDVI were observed within the smallest buffer zone; i.e., the decrease in the medium change rate or density of change occurrence was mainly observed within 250 m distance from the location, in few cases within 500 m (Tables 1 and 2). At distances larger than this and up to 1500 m, trends levelled off or even increased again. Field visits showed that gullies typically pass through cultivated fields and in these cases revegetation efforts are conducted mainly directly at the gully only. Hence, for land restoration of gullies dense vegetation regrowth does not often take place across larger distances from the restoration activities. Land improvement in proximity to check dams could therefore also occur as a line type pattern in the trend map. In this case, circular buffer zones will not help explaining vegetation trends. Apart from this, it should be considered that rehabilitation activities can occur at a smaller scale than Landsat's spatial resolution of 30 × 30 m, in which cases changes would not easily be detected. The spatial scale of the impact of SWC interventions as implemented in Ethiopia therefore underlines the challenge of detecting changes related to improved land management in developing countries based on the use of traditional remote sensing methods for change detection.

#### **5. Conclusions**

The aim of this study was to examine vegetation dynamics between 2002 to 2018 in degraded areas in the Ethiopian highlands and assess the impact of SLMP interventions. To examine vegetation dynamics in complex landscapes in Ethiopia on a detailed spatial scale, we investigated the potential of combining remote sensing data from different Landsat sensors using cloud-based geospatial processing supporting a high-resolution time series analysis. The vegetation dynamics in the study areas showed a shift from

browning (2002–2010) to greening (2011–2018) along with an overall greening trend over the full period (2002–2018). From the spatiotemporal patterns of NDVI and rainfall it could be concluded that the browning trend was not explained by long-term changes in rainfall. In contrast, the greening trend over the full period could—for 14% of the study area—be explained by increases in rainfall. Overall, no clear patterns of anthropogenic induced changes in vegetation were found when aggregating results at the catchment scale, as NDVI median trends did not clearly indicate better development in SLMP intervention areas than in control areas. Visual inspection based on multi-temporal Google Earth imagery showed that the changes in NDVI and rain-use efficiency did spatially overlap areas of small-scale land improvements related to human management, however, on a smaller scale than a micro-watershed (the smallest aggregation level). The OLS regression results provided evidence of land recovery that could be attributed particularly to SLMP infrastructure (check dams and terraces). Positive impacts on vegetation were found to be contributing to improving the rehabilitation of degraded hillside areas and gullies. These findings underline that the little differences found between treatment and control areas when aggregated to the level of (micro-)watersheds are rooted in a scale issue, and

highlight the need for per-pixel trend analysis using sensor systems like Landsat, or higher spatial resolution, to be able to remotely capture the effect of SLMP interventions.

The ecological improvements through SLMP, identified here at the per-pixel level from the use of Landsat time series, are an important contribution to restore terrestrial ecosystems as targeted in the Sustainable Development Goals. Continuous efforts in developing means for improved monitoring of human-induced vegetation restoration of degraded lands will be essential to maintain rehabilitated land, prevent further land degradation and support environmental sustainability.

**Author Contributions:** Conceptualization, E.B. and R.F.; methodology, E.B. and R.F.; formal analysis, E.B.; writing—original draft preparation, E.B.; writing—review and editing, E.B. and R.F.; visualization, E.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** R.F. acknowledges support by the Villum Foundation through the project 'Deep Learning and Remote Sensing for Unlocking Global Ecosystem Resource Dynamics' (DeReEco).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Acknowledgments:** The authors would like to thank GFA Consulting Group GmbH who provided the geolocation data on soil and water conservation measures and the German Agency for International Cooporation (GIZ) GmbH who provided the data on treatment and control areas.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **Appendix A**

**Table A1.** Description of soil and water conservation (SWC) measure data.

**Figure A1.** Landsat cross-calibration. (**a**) Distributions of ETM+ and OLI NDVI values; (**b**) OLS regression of ETM+ NDVI against OLI NDVI values. For a given OLI NDVI value, the corresponding ETM+ value is usually lower.

**Figure A2.** Interpolation. (**a**) Interpolation of a pixel's annual maximum NDVI time series (blue) through a three-year maximum moving window (green). The data gap in 2003 is interpolated; (**b**) Annual maximum value composite (MVC) with gaps produced by Landsat-7 ETM+ SLC error and clouds; (**c**) Interpolated MVC through applying a three-year maximum moving window.

**Figure A3.** Interpretation of the nature of changes in ecosystem functioning based on the spatial agreement of vegetation (NDVI) and rainfall trends. A decrease in NDVI despite an increase in precipitation or vice versa is likely to be caused by human management. Trend combinations with the same slope direction are likely to be caused by climate change [26].

**Figure A4.** LandTrendr. Fitted segments into an annual maximum NDVI time series.

**Figure A5.** Example of buffer zones used for the OLS regression analysis to examine trends at soil and water conservation measure locations.

**Table A2.** Mann-Whitney U results with the sample size N (number of significant pixels), the median NDVI trend, and U indicating whether treatment areas have significantly 1 larger or smaller trends than control areas. Watersheds that did not include any control micro-watersheds are not included.

1 Differences exist with different significance levels (\* *p* < 0.05, \*\* *p* < 0.01, \*\*\* *p* < 0.001).

**Figure A6.** Regional OLS regression models for check dams with (**a**) the median of all trend differences and (**b**) the proportion of significant positive trends during 2011–2018 within the zones as dependent variable. The red line shows the linear slope when including zones up to 250 m distance, the blue line when including zones up to 500 m. \*\* *p* < 0.01, \*\*\* *p* < 0.001.

#### **References**

### *Article* **Social-Ecological Archetypes of Land Degradation in the Nigerian Guinea Savannah: Insights for Sustainable Land Management**

**Ademola A. Adenle 1,2,\* and Chinwe Ifejika Speranza 1**

**Abstract:** The Nigerian Guinea Savannah is the most extensive ecoregion in Nigeria, a major food production area, and contains many biodiversity protection areas. However, there is limited understanding of the social-ecological features of its degraded lands and potential insights for sustainable land management and governance. To fill this gap, the self-organizing map method was applied to identify the archetypes of both proximal and underlying drivers of land degradation in this region. Using 12 freely available spatial datasets of drivers of land degradation—4 environmental; 3 socio-economic; and 5 land-use management practices, the identified archetypes were intersected with the Moderate-Resolution Imaging Spectroradiometer (MODIS)-derived land-degradation status of the region, and the state administrative boundaries. Nine archetypes were identified. Archetypes are dominated by: (1) protected areas; (2) very high-density population; (3) moderately high information/knowledge access; (4) low literacy levels and moderate–high poverty levels; (5) rural remoteness; (6) remoteness from a major road; (7) very high livestock density; (8) moderate poverty level and nearly level terrain; and (9) very rugged terrain and remote from a major road. Four archetypes characterized by very high-density population, moderate–high information/knowledge access, and moderate–high poverty level, as well as remoteness from a major town, were associated with 61.3% large-area degradation; and the other five archetypes, covering 38.7% of the area, were responsible for small-area degradation. While different combinations of archetypes exist in all the states, the five states of Niger (40.5%), Oyo (29.6%), Kwara (24.4%), Nassarawa (18.6%), and Ekiti (17.6%), have the largest shares of the archetypes. To deal with these archetypical features, policies and practices that address increasing population in combination with poverty reduction; and that create awareness about land degradation and promote sustainable practices and various forms of land restoration, such as tree planting, are necessary for progressing towards land-degradation neutrality in the Nigerian Guinea Savannah.

**Keywords:** archetypes; self-organizing maps; land degradation; drivers; savannah; Nigeria

### **1. Introduction**

With increasing global population, environmental change, and competing claims on land, the need to maintain land productivity and reduce land degradation has become even more critical. Various global initiatives reflecting this urgency include the United Nations Convention to Combat Desertification (UNCCD) goal of achieving a Land-Degradation Neutral (LDN) World [1]; the African Forest Landscape Restoration Initiative (AFR100) aiming to restore 100 million hectares of land in Africa by 2030 [2]; and the Great Green Wall Initiative across the Sahel [3]. Yet, land degradation (LD), i.e., the persistent reduction (negative trend) or loss of the biological productivity or ecological integrity of land or its value to humans, remains a diverse and complex issue [4,5].

The African Savannah is among the globally threatened landscapes [6], where climatic and edaphic conditions, as well as human activities, constrain vegetation regeneration [7,8].

**Citation:** Adenle, A.A.; Ifejika Speranza, C. Social-Ecological Archetypes of Land Degradation in the Nigerian Guinea Savannah: Insights for Sustainable Land Management. *Remote Sens.* **2021**, *13*, 32. https://dx.doi.org/ 10.3390/rs13010032

Received: 17 November 2020 Accepted: 23 December 2020 Published: 23 December 2020

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/).

In West Africa, the savannah ecozones are prone to LD and experience both anthropogenic and non-anthropogenic pressures [7,9]. These expose its 353 million inhabitants and their livelihoods to various impacts such as decline in ecosystem services, food insecurity, migration, and civil conflict [8]. In Nigeria, LD cases across its agroecological zones are mostly triggered by a singular factor or a combination of factors, which include: desertification, deforestation leading to biomass loss, land pollution caused by oil spillage and illegal mining, as well as extensive soil erosion [10]. Thus, agroecological zones like the Nigerian Guinean Savannah (NGS) are experiencing pressures from urbanization, agricultural expansion and an increasing population that is largely dependent on land resources for livelihoods [11,12]. The NGS, covering 49% of Nigeria, is widely degraded, its ecosystem services continue to decline, and livelihoods remain precarious [11,12]. Unsustainable land use and climate stress have been implicated in this widespread degradation [11,13–15]. While the indicators of LD drivers, their interplay, and implications at scale are generally acknowledged [14,16], there is a lack of knowledge of the constellation of factors characterizing specific degraded landscapes, such as the NGS, and their interplay [11]. The explanations of several studies in the NGS on LD drivers are often without an integrative perspective capable of exposing the interactions between potential LD drivers [16,17].

Recent studies thus stress the need for an integrative approach to enable a better understanding of the constellation and interplay of LD drivers in land systems [14,17]. One such integrative approach is archetypical clustering for identifying recurrent patterns in land conditions [18,19]. Archetypes, i.e., patterns or processes that occur repeatedly across space and time [19,20], have been found to provide a more holistic understanding of land system processes [20]. This understanding enables comparability across cases and helps identify strategic policy options to address land management across scales [18–20]. Archetype studies have been conducted on food security in the Peruvian Altiplano [21]; institutional analysis and climate change in the Peruvian Andes [22]; national analysis of ecosystem services in Germany [23]; water governance of river basins [24], and global land resources management [25]. An archetype approach thus helps to illuminate the associative patterns and influence of the complex drivers of global changes (such as LD) that have often been treated in isolation [19,20,25].

This paper thus aims to identify the characteristic patterns of social-ecological factors associated with LD in the NGS and to analyze the implications of the identified LD archetypes for land governance and sustainable land management (SLM) in the region. In the subsections, the description of the social-ecological conditions in the NGS, the study methods and hypothesis, the archetypical patterns of drivers, their thematic, and spatial characterization, including the links to different levels of LD, and the implications of the archetypes for land governance and SLM in the NGS are presented.

#### **2. Materials and Methods**

#### *2.1. Study Area*

The Nigerian Guinea Savannah (NGS) (Figures 1 and 2) is an ecological region found between 6.50◦N and 9.62◦N, 2.77◦E and 13.18◦E. Occupying central Nigeria, it is the country's most extensive ecological zone, referred to as the Middle Belt of Nigeria. The zone consists of parkland savannah, gallery forests, and derived savannah, including distinctive montane vegetation [26], with tropical dry and wet seasons. Rainfall in the wet season (April to October) is about 1240–1440 mm. The dry season lasts from November to March [27]. Average maximum annual temperature varies from 31 ◦C to 35 ◦C while the average minimum ranges from 20 ◦C to 23 ◦C [27]. The region is broadly divided into two sub-regions based on distinctive vegetation types, namely the Southern and Northern Guinea Savannahs. The Southern Guinea Savannah has taller trees, such as locust bean tree (*Parkia biglobosa*), and grasses such as Gamba grass (*Andropogon gayanus*); while the Northern Guinea Savannah is characterized by bush with some trees (e.g., *Isoberlinia* spp.) and relatively shorter grasses (e.g., *Hyparrhenia* spp.) [27]. The NGS has a high level of fauna and flora and hosts major perennial rivers such as River Niger and River Benue. Sub-

sequent years of uncontrolled deforestation and poor land management has transformed the zone largely into a degraded landscape (Figure 2) [12].

**Figure 1.** Map of the Nigerian Guinea Savannah including the administrative boundaries of states (Adapted from [26]).

**Figure 2.** (**a**) Landscape showing a degraded patch of the Nigerian savannah vegetation in Shiroro local government area, Niger state, Nigeria; (**b**) a mix of degraded land with few bushes and trees interspersed in Borgu local government area, Niger state, Nigeria. (Source: own fieldwork, 2019).

#### *2.2. Framing Land-Degradation Drivers*

In this study, LD drivers were understood as determinants, i.e., reasons, factors, or actions, shaping the rate of human activities, leading to a decision to remove or reduce vegetation cover thereby causing a decline or loss of land resources' productivity [14,28]. Factors prompting LD are broadly categorized either as proximal or underlying drivers [16,17,29]. Proximate drivers are human activities or immediate actions, including the decision to directly use or alter the land cover [14]. Underlying drivers include indirect or underpinning factors, such as socio-economic factors (e.g., poverty) and biophysical factors (e.g., topographic variables) that trigger proximate causes of LD [14]. Based on literature [17,28,29] and a report on the LDN target for Nigeria in 2018 [30], three main categories of LD drivers of the Nigerian Guinea Savannah Archetypes (NGSA) namely: environmental, socioeconomic, and land-use management practices were identified. Based on the available spatial and remote sensing data, 12 drivers comprising three environmental, four socioeconomic, and five land-use management practice categories were selected (Table 1a–c). The processes through which the variables (whether as proximate or underlying drivers), drive the LD are explained in Table 1a–c.

#### *2.3. Datasets Selection*

For this study, factors were considered based on their influence on land-use decisions and LD [16,29,31] (see Table 1 for details). Climatic variables such as rainfall were not included because human-induced activities are the prevailing cause of LD, especially in the NGS [11,12]. However, human-induced drivers are better managed than climatic drivers of LD [11,32].

#### 2.3.1. Land-Use Management Practices

Land-use management practices have been linked to degraded land in Nigeria through land clearing and conversion including intensification [13]. Other unsustainable land-use management practices include rapid agricultural expansion, uncontrolled bush burning, deforestation, excessive wood extraction, unplanned infrastructure extension, and urban development, including overgrazing [12,13,33]. For this study and based on spatial data availability, data on land-use management practices were acquired and processed, respectively. The fire-occurrence density was derived from the National Aeronautics and Space Administration (NASA) active fire hotspot data of 2018 (firms.modaps.eosdis.nasa.gov) after running a spatial point-density analysis of the active fire spots at 250 m resolution. The livestock grazing intensity data for 2005, developed by Harvest Choice in 2018 (www.ifpri.org/project/harvestchoice) was used. The generated distance to the major road in 2016 for Nigeria, at a resolution of 3 arc-second (approximately 100 m at the equator) was acquired from (www.worldpop.org/project/categories?id=14) [34]. The Euclidean distance analysis of the extracted major town polygons from Google Earth (www.google.com/earth/) was used to determine the distance to major towns at 250 m resolution. Using the International Union for Conservation of Nature (IUCN) recognized protected area polygon for Nigeria (www.protectedplanet.net/country/NGA), the density of the protected area at 250 m resolution across the NGS was generated through point-density analysis.

#### 2.3.2. Socio-Economic Drivers

Demographic and socio-economic factors such as population density, income, poverty, and illiteracy are drivers of LD in certain contexts [16,29]. In other contexts, they may even be drivers of improved land conditions [35]. However, in the case of Nigeria, its over 200 million inhabitants and population density of 226 km2 place a huge demand on land resources [33,36]. Thus, gridded human population density constructed for 2018 in 2020, from random forest-based dasymetric redistribution at 3 arc-second (approximately 100 m at the equator) spatial resolution, was downloaded from www.worldpop.org [34]. Male and female literacy layers of high resolution at 1 km × 1 km gridded cells developed for 2003 in 2017, based on a geostatistics approach [37], were acquired [34]. These are necessary as they are proxies for access to agricultural extension information, as previous studies show that limited information on SLM drives LD [16,38]. The poverty headcount in percentages for Nigeria at 1 km for 2013 mapped through Bayesian model-based geostatistics analysis was downloaded from www.worldpop.org [34], because poverty can foster practices that cause LD, while LD can foster a poverty trap [38]. These drivers were selected in that they are known factors that influence decisions on land and SLM LD [28,29,38].

#### 2.3.3. Environmental Drivers

Land can be sensitive to degradation due to its environmental and physiographic characteristics, influencing human decisions to use land [31]. Such influential characteristics include soil bulk density (BD), elevation, and slope [13,39]. For this study, the alreadyprocessed NASA Shuttle Radar Topography Mission (SRTM) based slope and elevation for Nigeria by [40] at a resolution of 3 arc-second (approximately 100 m at the equator)

produced for 2020 was acquired from www.worldpop.org [34]. The spatial mapping of the bulk density (BD; soil compaction) in 2018 at 250 m resolution from 0 cm to 30 cm depth were downloaded from www.soilgrids.org and then averaged to give the overview of the BD for the study area [41–43].

#### *2.4. Methods*

#### 2.4.1. Conceptual Framework

The workflow in Figure 3 was applied to the 12 drivers (Table 1a–c), which served as inputs for identifying archetypes. The intermediate outputs from the framework, such as correlation between drivers including cluster features, can be found in supplementary material Figures S1–S5.

**Figure 3.** The conceptual framework.

2.4.2. Identifying Archetypes of Land-Degradation Drivers Using Self-Organizing Maps

To develop the Nigerian Guinea Savannah Archetypes (NGSA) of LD drivers, the 12 driver datasets were clipped to the boundary outline and resampled to a 250 m pixel size, using the nearest neighbor technique (Figure 3, Box 2), and then projected to a uniform coordinate system (i.e., Minna/UTM zone 31N). To enhance the clustering of the datasets, data were normalized (Z-score) to reformate into a common scale (Figure 3, Box 2). Correlation was calculated to identify relationships between the drivers (Figures S5 and S6) and the extent of interdependencies among the dataset that might limit the analysis. Then the Kohonen Self-Organizing Map (SOM) technique was applied to generate a singlelayer map of the archetypes of LD drivers [54]. The SOM approach involves organizing data (in this case, the 12 spatial datasets) into patterns based on their inherent similarities and dissimilarities into different groups [54,55]. By testing the different combinations of clusters and using a performance analysis involving both the Davies–Bouldin index and the mean distance of the classified grid cells [23,25], nine clusters of LD drivers were identified as archetypes (see supplementary material, Figures S2–S5). The Z-score, i.e., standardized score from the SOM, was used to examine values of each driver in terms of distance from the mean and to explain the clusters. A Z-score that is equal to the mean is zero, while positive and negative Z-score, i.e., greater than or less than zero, depict distance way above or below the mean. Through an ordinal scaling, the relative standing of the Z-score from the mean was ranked. If the Z-scores = 0, this implies mean/low influence of a driver; Z-score ≤ ±1 = moderate influence; ±1 < Z-score < ±2 = high influence; and Z-score ≥ ±2 = very high influence, respectively (Figure 3, Box 3). From the absolute values of the Z-scores of each cluster, the percentage dominance categories in the clusters were determined [23] (Figure 3, Box 3).


*Article* **Power Spectral Density Analysis of Nanowire-Anchored Fluctuating Microbead Reveals a Double Lorentzian Distribution**

**Gregor Bánó 1, Jana Kubacková 2, Andrej Hovan 1, Alena Strejˇcková 3, Gergely T. Iványi 4, Gaszton Vizsnyiczai 5, Lóránd Kelemen 5, Gabriel Žoldák 6, Zoltán Tomori 2 and Denis Horvath 6,\***

**Abstract:** In this work, we investigate the properties of a stochastic model, in which two coupled degrees of freedom are subordinated to viscous, elastic, and also additive random forces. Our model, which builds on previous progress in Brownian motion theory, is designed to describe waterimmersed microparticles connected to a cantilever nanowire prepared by polymerization using two-photon direct laser writing (TPP-DLW). The model focuses on insights into nanowires exhibiting viscoelastic behavior, which defines the specific conditions of the microbead. The nanowire bending is described by a three-parameter linear model. The theoretical model is studied from the point of view of the power spectrum density of Brownian fluctuations. Our approach also focuses on the potential energy equipartition, which determines random forcing parametrization. Analytical calculations are provided that result in a double-Lorentzian power density spectrum with two corner frequencies. The proposed model explained our preliminary experimental findings as a result of the use of regression analysis. Furthermore, an a posteriori form of regression efficiency evaluation was designed and applied to three typical spectral regions. The agreement of respective moments obtained by integration of regressed dependences as well as by summing experimental data was confirmed.

**Keywords:** nanowire cantilever; stochastic model; double Lorentzian spectrum


*4.9. Growth Factors xMAP® Technology*

Cytokine concentrations were determined from the non-pigmented lymph node part, using analyte-specific capture beads coated with target-specific capture antibodies according to the manufacturer's specifications. The analytes were detected by biotinylated analytespecific antibodies. Following binding of the fluorescent detection label, the reporter fluorescent signal was measured with the Bio-Plex 200 multiplex suspension array system (Bio-Rad, Hercules, CA, USA) and detected with Bio-Plex 5.0 Software (Bio-Rad, Hercules, CA, USA). The sensitivity for the respective cytokines was as followed: CCL1/I-309 1.73–1260 pg/mL; CCL5/RANTES 8.27–6030 pg/mL; CCL11/Eotaxin 20.23–14,750 pg/mL, CXCL1/GRO alpha 15.30–11,160 pg/mL; IL-8/CXCL8 1.44–1050 pg/mL; CCL2/JE/MCP-1 11.87–8650 pg/mL; CCL8/MCP-2 4.66–3400 pg/mL; CCL17/TARC 30.62–22,320 pg/mL; CXCL10/IP 0.43–310 pg/mL. Analyte levels of complete growth medium mixed with the additives FBS or hPL served as background controls (Supplementary Figure S2).

#### *4.10. DNA Extraction*

Genomic DNA of the primary tumor and all four MUG Mel3 cell lines was isolated on a Maxwell MDxResearch System (Promega).

#### *4.11. Copy Number Profiling*

Genome-wide copy number alterations (CNA) were established using shallow wholegenome sequencing (sWGS). Shotgun libraries were prepared using the TruSeq DNA LT Sample preparation Kit (Illumina, San Diego, CA, USA). Briefly, 380 ng, 144 ng and 360 ng input DNA from all four MUG Mel3 cell lines and both parts of the lymph node were fragmented in 130 µL using the Covaris System (Covaris, Woburn, MA, USA). After concentrating the volume to 50 µL, end repair, A-tailing and adapter ligation were performed following the manufacturer's instructions. For selective amplification of the library fragments that have adapter molecules on both ends, 15 PCR cycles were used for higher concentration samples. Libraries were quality checked on an Agilent Bioanalyzer using a DNA 7500 Chip (Agilent Technologies, Santa Clara, CA, USA) and quantified using qPCR with a commercially available PhiX library (Illumina, San Diego, CA, USA) as a standard. Libraries were pooled equimolarily and sequenced on an Illumina MiSeq in a 150 bp single read run. On completion of the run data were base-call demultiplexed on the instrument (provided as Illumina). Genome-wide copy number calling was performed as previously described [50].

#### *4.12. Mutation Analysis Using AmpliSeq (BRAF)*

Highly multiplexed PCR was used to generate amplicon libraries to amplify 207 amplicons, covering approximately 2800 COSMIC mutations from 50 oncogenes and tumor suppressor genes. (Cancer Hotspot Panel v2, Cat. Nr. 4475346; Thermo Fisher Scientific, Waltham, MA, USA). All analyses were performed in duplicate. Libraries were prepared using the Ion AmpliSeq Library Kit 2.0 and sequencing was performed on an Ion Proton Sequencer (Thermo Fisher Scientific, Waltham, MA, USA). Emulsion PCR and sequencing runs were performed with the appropriate kits (Ion One Touch Template Kit version 2 and Ion Proton 200 Sequencing Kit; Thermo Fisher Scientific) using Ion PI chips. Sequencing length was set to 520 flows and yielded reads ranging from 70 to 150 bp, consistent with the expected amplicon size range. Initial data analysis was performed using the Ion Torrent Suite Software version 4.1 Plug-ins (Thermo Fisher Scientific).

#### *4.13. Tumorigenicity Study*

CR ATH HO mice (Crl:NU(NCr)-Foxn1nu, Charles River Laboratories, Kent, UK) and NXG mice (NOD-Prkdcscid-IL2rgTm1/Rj, Janvier Labs, Saint Berthevin Cedex, France) were maintained in-house (4–5 weeks of age, weight between 15 and 20 g). In accordance with a protocol approved by the committee for institutional animal care and use at the Austrian Federal Ministry of Science and Research (BMWFW); vote 66.010/0046- WF/V/3b/2016) animal work was carefully carried out. All mice were maintained under specific pathogen free (SPF) conditions in individually ventilated cages with ad libitum access to food and water. For cell injection and ultrasound imaging, mice were anaesthetized with constant administration of 2% isoflurane in a constant airflow of 2.5 L per minute. Animals were sacrificed 25 days post-injection.

Mice were divided into two groups (*n* = 5) according to strain. Animals were injected into each flank subcutaneously with 2.5 × 106 cells in 100 µL PBS cell suspension using a 27G needle. Injection was performed as described in the following: front right flank received MUG Mel3 NPh, back right flank received MUG Mel3 NPF, front left flank received MUG Mel3 Ph, back left flank received MUG Mel3 PF. Inoculation of the tumor was monitored daily and ultrasound imaging started on day six post-injection once a week. After sacrifice, histopathological examination was performed. The mice were dissected, and tumors extracted. All other organs were checked visually for structural changes. The

tissue was fixed in 4% paraformaldehyde solution for 24 h, and were further embedded in paraffin.

#### *4.14. Ultrasound Imaging*

High-frequency ultrasound (HF-US) was performed using a Vevo3100 HF-US system with a 50 MHz (MX700) or a 40 MHz (MX550D) transducer (Fujifilm VisualSonics, Inc., Toronto, ON, Canada) reaching a spatial resolution of about 30–40 µm. Images in sagittal and transverse planes were obtained of the region of interest. Calculation of tumor volume was performed using VevoLab Software (Version 5.5.1, Fujifilm VisualSonics, Inc.) using the formula displayed in Equation (1):

$$\text{V}\left[\text{mm}^3\right] = \frac{\text{3.141} \times \text{length}\left[\text{mm}\right] \times \text{width}\left[\text{mm}\right] \times \text{depth}\left[\text{mm}\right]}{6} \tag{1}$$

#### *4.15. PS Exposure*

PS exposure was measured using the RealTime-Glo™ Annexin V Apoptosis Assay (Promega). Briefly, cells were seeded at 105 cells/100 µL medium in a white 96-multiwell plate with clear bottom and incubated O/N at 5% CO2 and 37 ◦C. The reagent stock solution (1000×) was diluted in respective medium according to the manufacturer's protocol. Before the measurement 100 µL of the reagent solution was added to the cell suspension. Luminescence was measured using the Glomax Multi+ detection system (Promega, Madison, WI, USA).

#### *4.16. Peptides*

C-terminally amidated peptides R-DIM-P-LF11-322 (PFWRIRIRRPRRIRIRWFP-NH2, M = 2677.4 g/mol) and R-DIM-P-LF11-334 (PWRIRIRRPRRIRIRWP-NH2, M = 2382.4 g/mol) derived from Lactoferricin were purchased from PolyPeptide Group (San Diego, CA, USA). A purity of higher than 96% for all peptides had been determined by RP-HPLC. Peptide stock solutions were prepared in acetic acid (0.1%, *v*/*v*) to an approximate concentration of 3 mg/mL and treated by ultrasonication at 15 min for better solubility. Peptide concentration was determined by measurement of UV absorbance of tryptophan at a wavelength of 280 nm using a NanoDrop photometer (ND 1000, Peqlab, VWR International, Inc., Erlangen, Germany). All peptide stocks were stored at 4 ◦C until use.

#### *4.17. PI Uptake Toxicity Assay*

To determine peptide-induced cell death, cells were collected, resuspended in respective media and diluted to a concentration of 1 × 106 cells/mL. 100 µL aliquots (105 cells) were incubated with different peptide concentrations (5 µM to 100 µM) for up to 8 h in the presence of propidium iodide (PI; 2 µL/105 cells of 50 µg/mL, Molecular Probes Inc., Eugene, OR, USA) at RT in black 96-well plates. PI uptake was measured using the GloMax® Multi+ Detection System (Promega, Madison, WI, USA). Cytotoxicity was calculated from the percentage of PI-positive cells in media alone (P0) and in the presence of peptide (PX; Equation (2)). Triton-X-100 was used to determine 100% of PI positive cells (P100).

$$\% \text{ PI} - \text{uptake} = \frac{100 \times (\text{P}\_{\text{x}} - \text{P}\_{0})}{(\text{P}\_{100} - \text{P}\_{0})} \tag{2}$$

Excitation and emission wavelengths were 536 nm and 617 nm, respectively. Each experiment was repeated at least 3 times.

#### *4.18. Statistical Analysis*

Statistical analyses were conducted in GraphPad Prism 9.2.0 (GraphPad Software, San Diego, CA, USA) using two-tailed student's *t*-tests to compare culture conditions (FBS or hPL) within cell line origins (pigmented or non-pigmented) or to compare the origins of cells within the same cultivation method, and two-tailed unpaired student's *t*-test to

compare tumor weight and tumor growth of the four culture conditions on the day of sacrifice. A *p*-value ≤ 0.05 was considered statistically significant.

#### **5. Conclusions**

Melanoma in pregnancy is a vast, debated and complex field. Further research is urgently needed to provide evidence-based treatment and improve the management of melanoma in pregnancy. We have successfully established four cell lines from a melanoma metastasis from a pregnant woman. By using different tumor fractions and cultivation conditions, we were able to better mimic intratumoral heterogeneity as the four cell lines differ morphologically, genetically and phenotypically. In addition, we were able to demonstrate different cytotoxic effects by antitumor peptide treatments for all four cell lines, and thus may also consider them for new innovative treatment strategies.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/ijms222111318/s1.

**Author Contributions:** B.R. and S.S. designed the draft for this manuscript. S.S. performed main cell culture parts, Luminex Technology, collected and analyzed the data and wrote the manuscript with B.R. T.H. performed qPCR, S.A.W. and I.A. conducted the animal experiments. S.R. and D.Z. were responsible for the peptides. E.R. was responsible for the patient and patient history and acquired together with G.R. the tumor material. C.B. and P.S. prepared and supplied the hPL. A.A. provided the human specimen and was responsible for IHC stainings. W.B., B.H. and C.W. performed ICC stainings. M.G. and N.K. contributed to in vitro experiments. K.K. and E.H. were responsible for the genetic section of this manuscript. G.R. helped organizing the study. B.R. managed, supervised, and provided funding for this study. All authors contributed to the interpretation of the data, discussed the results, contributed to the manuscript, and approved the final version. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** We thank the DocSchool 'Molecular Medicine and Inflammation' at the Medical University of Graz for funding the publication costs.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**Abstract**

Waste management is a social, economic, and environmental problem facing all African countries. If the 2030 Agenda for sustainable development is to be achieved, sustainable waste management approaches must be an environmental and public health imperative deserving political priority. Current reasons for the poor management of waste in Africa, include, amongst others, weak organizational structures; lack of appropriate skills; inadequate budgets; weak legislation; lack of enforcement; low public awareness; corruption, conflict; political instability; and lack of political will. At the heart of the problem, is a failure in governance. However, through these gaps, many social and technological innovations have emerged. Innovations that recognize the opportunity that waste provides as a secondary resource. Diverting waste away from dumpsites and landfills towards reuse, recycling and recovery can improve the livelihoods of thousands of informal waste reclaimers, while also creating new jobs and business opportunities for the continent. Reintroducing secondary resources such as polymer, fiber, metals and nutrients back into local value chains has the potential to strengthen manufacturing economies and reduce the economic burden on product imports. Bringing waste under control in Africa and unlocking the opportunities that "waste" provides as "resource" will require immediate intervention by government, business and civil society.

**Keywords:** solid waste, infrastructure services, green jobs, recycling


**5. Acknowledgement**

We are thankful to El Triunfo Biosphere Reserve personnel for granting permits to work in the reserve. This research is part of the Cloud Forest Restoration Project funded by CONACyT–FOMIX-CHIAPAS: CHIS-2007-07-77710**.**


**Paul A. Crowther**

*Review*

Department of Physics & Astronomy, University of Sheffield, Sheffield S3 7RH, UK<EMAIL_ADDRESS>
Received: 3 September 2019; Accepted: 6 November 2019; Published: 8 November 2019

**Abstract:** A review of the properties of the Tarantula Nebula (30 Doradus) in the Large Magellanic Cloud is presented, primarily from the perspective of its massive star content. The proximity of the Tarantula and its accessibility to X-ray through radio observations permit it to serve as a Rosetta Stone amongst extragalactic supergiant HII regions since one can consider both its integrated characteristics and the individual properties of individual massive stars. Recent surveys of its high mass stellar content, notably the VLT FLAMES Tarantula Survey (VFTS), are reviewed, together with VLT/MUSE observations of the central ionizing region NGC 2070 and HST/STIS spectroscopy of the young dense cluster R136, provide a near complete Hertzsprung-Russell diagram of the region, and cumulative ionizing output. Several high mass binaries are highlighted, some of which have been identified from a recent X-ray survey. Brief comparisons with the stellar content of giant HII regions in the Milky Way (NGC 3372) and Small Magellanic Cloud (NGC 346) are also made, together with Green Pea galaxies and star forming knots in high-*z* galaxies. Finally, the prospect of studying massive stars in metal poor galaxies is evaluated.

**Keywords:** galaxies; star formation–galaxies; star clusters; general–open clusters and associations; individual; 30 Doradus–stars; massive–stars; early-type


**4. Components of beef cow efficiency**

An efficient cow herd is sexually precocious, with a high reproductive rate, low dystocia and has longevity with minimum maintenance requirements [24]. A herd's ability to reproduce in a given nutritional environment is the most important contributing factor to efficiency. Selection goals for efficiency in the cow-calf production systems include early sexual maturity

> with lean growth and minimal increase in mature weight [24]. The most efficient beef cow is therefore the one with the highest milk production that can yearly wean a calf with the

> **Figure 5.** Estimated breeding values (EBV) for reproductive traits in Bonsmara cows from 1990 to 2010 (source: ARC-API)

Effect of Bioregion on the Size and Production Efficiency of Bonsmara Cattle in Semi-Arid Parts…

http://dx.doi.org/10.5772/intechopen.72713

47

Genetic trends for growth and maternal traits in the Bonsmara breed studied by [22] are shown in **Figure 4**. In this study, the genetic trends are presented for a 20 year period (1990–2010), which illustrates the consistent improvements in estimated breeding values for weaning weights (direct and maternal), 12- and 18-month weights, while estimated breeding values for birth and mature weights remained nearly constant. These improvements were obtained in well-managed cattle herds, which generally exhibit regional differentiation in mature size

Genetic trends for reproduction traits of the 20 years of Bonsmara breed data studied, are presented in **Figure 5**. Estimated breeding values for both age at first calving and inter-calving period increased since 1990–2010. It follows that the reproductive ability of cows decreased marginally during the same period during which marked progress was made in terms of growth traits, possibly since cow size still exceeded to production potential of the main beef

Reproduction and calf survival rates are the most important factors that determine the efficiency of a beef herd [10, 24]. In spite of the importance of reproduction it is generally

growth and carcass characteristics required by the market [20].

(*ICP ~ EBV of Inter-calving period; AFC ~ EBV for age at first calving*).

(e.g., optimum body size relative to bioregion, as illustrated in **Figure 1**).

cattle production regions in Southern Africa.

**5. Reproduction of extensive beef cattle**

**Figure 4.** Estimated breeding values (EBV) for growth traits in the Bonsmara cattle breed from 1990 to 2010 (source: ARC-API) (*Birth\_Dir ~ EBV for birth weight direct; Wean\_Dir ~ EBV for weaning weight direct; Wean\_Mat ~ EBV for weaning weight maternal; 12\_Month ~ EBV for 12-month weight; 18\_Month ~ EBV for 18 month weight; Mature\_weight ~ EBV for mature weight*).

Effect of Bioregion on the Size and Production Efficiency of Bonsmara Cattle in Semi-Arid Parts… http://dx.doi.org/10.5772/intechopen.72713 47

**Figure 5.** Estimated breeding values (EBV) for reproductive traits in Bonsmara cows from 1990 to 2010 (source: ARC-API) (*ICP ~ EBV of Inter-calving period; AFC ~ EBV for age at first calving*).

with lean growth and minimal increase in mature weight [24]. The most efficient beef cow is therefore the one with the highest milk production that can yearly wean a calf with the growth and carcass characteristics required by the market [20].

Genetic trends for growth and maternal traits in the Bonsmara breed studied by [22] are shown in **Figure 4**. In this study, the genetic trends are presented for a 20 year period (1990–2010), which illustrates the consistent improvements in estimated breeding values for weaning weights (direct and maternal), 12- and 18-month weights, while estimated breeding values for birth and mature weights remained nearly constant. These improvements were obtained in well-managed cattle herds, which generally exhibit regional differentiation in mature size (e.g., optimum body size relative to bioregion, as illustrated in **Figure 1**).

Genetic trends for reproduction traits of the 20 years of Bonsmara breed data studied, are presented in **Figure 5**. Estimated breeding values for both age at first calving and inter-calving period increased since 1990–2010. It follows that the reproductive ability of cows decreased marginally during the same period during which marked progress was made in terms of growth traits, possibly since cow size still exceeded to production potential of the main beef cattle production regions in Southern Africa.


Contents

Foreword on Shock

Shock, defined as a state of insufficient perfusion and impaired tissue oxygen delivery, is a life-threatening condition of varied etiologies that requires prompt recognition, diagnosis, and resuscitation [1, 2]. Prehospital shock is associated with a 33–52% in-hospital mortality rate [1]. While trends in frequency and mortality of undifferentiated shock in the emergency department (ED) are under-explored, in-hospital mortality rates of 23–24% have been reported in Danish [1] and US ED settings [3, 4], with 90-day mortality approaching 41% [1]. Moreover, mortality rates for some subtypes of shock may be higher (e.g., trauma patients with hemorrhagic shock) [5]. Mortality further increases with any associated end-organ dysfunction or failure [6], as well as with increased comorbidity burden as measured by the

Despite technical improvement in diagnostics and advances in treatment, shock remains a critical finding in acute medical care. Reducing time to recognition and targeted treatment are critical aspects of patient care. Shock's clinical recognition is traditionally based on vital sign abnormalities (blood pressure, heart rate), and it may be defined as the presence of hypotension (systolic blood pressure ≤ 100 mmHg) and ≥ 1 organ failures [1]. The Shock Index (SI; systolic blood pressure/heart rate)

Traditionally shock has been classified into four categories: cardiogenic, distributive, hypovolemic, and obstructive. However, although the circulatory system is complex and depends on a multitude of variables, some find it helpful to simplify it to three main components: cardiac function ("the pump"), intravascular volume ("the tank"), and systemic vascular resistance ("the pipes") [2]. In shock, dysfunction occurs within one or more of the three components – the pump, tank, or pipes – such that tissue perfusion and oxygen delivery are impaired [2]. Acute pump malfunction can be caused by arrhythmias, conditions that result in a sudden decrease in cardiac contractility (e.g., myocardial infarction, myocarditis, valvular insufficiency) or by extracardiac conditions that obstruct cardiac output (e.g., pericardial tamponade, pulmonary embolus). Acute tank malfunction primarily results from a decrease in intravascular volume due to hemorrhage, volume loss, or impaired venous return and impaired left ventricular preload (e.g., tension pneumothorax). Pipe malfunction may be observed with processes that alter vascular tone including anaphylaxis, neurogenic (e,g., spinal cord injury), sepsis, and processes that disrupt pipe integrity (e.g., aortic dissection, abdominal aortic aneurysm). Importantly, some entities (e.g., burns, trauma, etc.) may compromise more than one system (pump, tank, pipes). Regardless of the underlying mechanism of shock, if impaired perfusion and oxygen delivery are not recognized and reversed, organ dysfunction, tissue necrosis, and

may further be used as a measure of cardiovascular failure (≥ 1) [7].

Significant regional disparities in evidence-based care have been reported, and in-hospital mortality remains high. This book goes beyond the basics of epidemiology, pathophysiology, and recognition, and targets the difficult, and at times confusing, management decisions that clinicians treating shock are faced with including resuscitation fluid (or blood product) selection, mechanical ventilation,

Charlson comorbidity index [1].

death may ensue.

*Ranjan Matthias and Muna Al Musalmani*


**5.1 Diseases transmission via vector**

Plant diseases can be caused by various factors. Such factors could be abiotic such as nutrient deficiencies, soil compaction, salt injury or sun scorch. Biotic causes of plant disease transmission are caused by living organisms and they are collectively named as pathogens. Understanding pathogens life cycle, living requirements, movements and disease they carry can allow effective implementation of management regime to intervene the cyclative transmission.

*a NPV, nonpersistent stylet borne viruses.*

*b SPV, semipersistent foregut-borne viruses.*

*c PCV, persistent circulative (mostly nonpropagative) viruses.*

*d PPV, persistent propagative viruses.*

*e Includes 110 virus species of the genus Potyvirus, family Potyviridae.*

*f Virus species of the genus Begomovirus, family Geminiviridae.*

#### **Table 2.**

*The table illustrates the major group of insect that cause the virus spread through vector. The viruses have also been categorised to persistency in the environment [11].*

#### *5.1.1 Insects*

Diseases in plants can also be transmitted via vectors. Aphids (28%) and whiteflies (18%) have been studied extensively over the years as they have been identified as common pathogenic vectors alongside with beetles (7%) and nematode (7%). However, the dense forests and every different area of land would always lead to a new discovery of a new species. In recent years, there is a new species of wasp, *Allorhogas gallifolia*. This wasp would make use of other wasp's gall as nests. Larvae that hatch would then feed on caterpillars that consume gall tissues. A caddisfly, *Potamophylax coronavirus*, has also been discovered. This moth has eggs and larvae that thrive in the environment near rivers and lakes. With new insects emerging, there are various study opportunities apart from just their life cycle. They can potentially be a reservoir host for all kinds of diseases to humans, animals and plants. The type of insects would often carry similar viroid.

Insects as vectors are relatively tricky to have a proper management to fully eradicate the population. This small, hardy population has found itself thriving through different ages of the earth by having the capability to populate through laying multiple eggs. These eggs laid by one are then sufficient to replace more than one adult in the population. With the rapid replication ability, insects are able to adapt to the changing environment through different mechanisms. Insects can transmit in a cyclative manner where the pathogen is ingested before passing on to the new plant host. Calculative plant pathogens will often induce physiological changes in their plant hosts. Vectors who then feed on the infected plant will then have behavioural changes to optimise the spread of pathogens to other plants. Their ability to invade a new area is dependent on the insects' ability to adapt to the environment besides the food availability.

#### *5.1.2 Herbivore as vector*

Herbivores are essential to the plant communities as grazing removes substantial quantities of biomass and promote plant species diversity [16]. In addition to herbivore vertebrate, insects have been shown to promote species richness by feeding competitive dominants. An instance to display such phenomena would be molluscs. They are a major group of invertebrate in temperate grasslands. These principal forb feeders would contribute negative impacts on species richness. These invertebrates are more well studied compared to soil-borne fungus. The complexity of the pathogen physiology is hard to comprehend as the environment will cause compensatory and additive interactions. Apart from infecting plants. Pathogens have displayed that they have the ability to increase plant susceptibility to herbivores to feed on the plant. Pathogens could also decrease it's susceptibility as it makes the plant less palatable to grazing animals.

#### **5.2 Soil-borne pathogens**

Besides insects as vectors for pathogens, they could be soil-borne as well. They are capable of spreading via swimming spores of primitive and soil-inhibiting pathogenic fungi. Fungal pathogens have been categorised into three functional groups: biological controllers, ecosystem regulators and species participating in organic matter decomposition and compound transformations. Fungal pathogens are dispersed by spores. Their successful inhabitants of soil is accredited to its high plasticity and their ability to adopt various forms in response to adverse or unfavourable conditions [5]. Biological controllers can improve soil health by regulating *Earth's Energy Budget Impact on Grassland Diseases DOI: http://dx.doi.org/10.5772/intechopen.99971*

diseases, pests, and the growth of other organisms. Fungi as ecosystem regulators are responsible for soil structure formation and medication. This will enhance the habitat of other organisms through the regulation of the dynamic aspect of the physiological processes in the soil environment.

Infected grasslands were observed to have increased species richness. However, This would have a negative impact on the dominant species in the grassland. In addition, the affected grassland will have decreased biomass. In a study by Allan et al. (2010), the biomass of the grassland will increase in an increasing exponential manner over the years. Fungal pathogens are not harmful to all. Despite the harm the bring to some of the species, fungi actively participate in nitrogen fixation, biological control of root pathogens, production of hormone and protection against drought. For instance, fungi can be beneficial to some leguminous crop via the improvement of plant uptake of nutrients and provide some form of protection to pathogens.

Nevertheless, bacteria that cause diseases in plants. Bacteria could be transmitted via the similar route as viruses by having physical contact on the health leaves or introducing plant materials that have bacteria. The simple transmission mode carries a huge load of impact on the environment as it alters the plants' physiology.