**Interpopulation Variability in Dietary Traits of Invasive Bleak** *Alburnus alburnus* **(Actinopterygii, Cyprinidae) Across the Iberian Peninsula**

**Dani Latorre 1 , Guillem Masó 1,2 , Arlo Hinckley 3,4 , David Verdiell-Cubedo 1 , Gema Castillo-García 5 , Anni G. González-Rojas 5 , Erin N. Black-Barbour 5 , Anna Vila-Gispert 1 , Emili García-Berthou 1 , Rafael Miranda 6 , Francisco J. Oliva-Paterna 7 , Ana Ruiz-Navarro 7 , Eduardo da Silva 8 , Carlos Fernández-Delgado 9 , Julien Cucherousset 10 , José M. Serrano 3 and David Almeida 1,5, \***

Received: 13 June 2020; Accepted: 4 August 2020; Published: 5 August 2020

**Abstract:** The bleak *Alburnus alburnus* is native to most of Europe. This cyprinid fish is a successful invader in the Iberian Peninsula. No studies exist on its foraging strategies on a large scale for this ecoregion. The aim of the present study was to compare dietary traits of invasive bleak among the main Iberian rivers and a 'reference' native bleak population from France. Bleak were sampled during May–June 2019 from the Iberian Rivers Ebro, Tagus, Guadiana, Segura and Guadalquivir and the River Saône (France). Diptera larvae and zooplankton were common food categories in the River Saône. Insect nymphs were more important in the River Ebro. The intake of plant material was higher in the River Tagus. Flying insects were more consumed in the River Guadiana. Nektonic insects were important in the River Guadalquivir. Detritus was a frequent food category for all populations, in terms of occurrence and mass. Dietary parameters followed a unimodal response in relation to the latitudinal gradient, with the maximum values for the Tagus and Guadiana populations. Overall, results suggest that this wide interpopulation variability will contribute to the species' successful establishment throughout Mediterranean Europe, which poses a serious risk to its highly valuable native fish fauna.

**Keywords:** gut content analysis; Mediterranean rivers; prey richness; trophic niche breadth; Spain

#### **1. Introduction**

The disruptive effect of fish invasions is of particular conservation concern in the Iberian Peninsula, which is rich in endemisms. Indeed, >50% of native fish species are unique to this region [1,2], but the proportion of non-native fishes (>30%) continues to increase [2,3]. The bleak *Alburnus alburnus* (L., 1758) is a cyprinid species native to most of Europe, from the northern Pyrenees to the Urals. In its native area, bleak inhabit lakes or still-waters in medium/large rivers and feed chiefly on zooplankton [4–6]. In the Iberian Peninsula, this species was mainly introduced in reservoirs during the 1990s as a 'forage fish' for non-native piscivorous fishes, such as northern pike *Esox lucius* L., 1758, largemouth bass *Micropterus salmoides* (Lacepède, 1802) or pikeperch *Sander lucioperca* (L., 1758) [7]. Since its introduction, the bleak has displayed a strong invasive character throughout Spain and Portugal, taking advantage of anthropogenic disturbances on Mediterranean freshwater ecosystems [8–10]. Moreover, this species threatens several Iberian fishes, mainly through hybridization, trophic competition and behavioral interference (i.e., aggression) [3]. This is because Iberian fish communities have low diversity and are poorly adapted to a such invasion level of non-native fishes [1], which usually displace native species from available resources. As an example, direct observations by snorkeling have clearly shown that foraging behavior of endemic Ebro nase *Parachondrostoma miegii* (Steindachner, 1866) is disturbed by bleak [11]. All this information on bleak traits (e.g., spread capacity, environmental impacts) allows to consider this species as a clear invasive fish of high ecological risk in Iberia [2,12]. However, the available information on bleak dietary traits in the Iberian Peninsula corresponds to a few particular sites only [13,14], with no study comparing data at the scale of this entire ecoregion. This would be of particular interest to reveal macroecological patterns (e.g., latitudinal gradients). Indeed, dietary traits of non-native fish can show wide variability under contrasting ecological conditions [14–16] and this has implications from a conservation perspective. Thus, a better understanding of dietary data can be used by policy-makers and environmental managers to assist monitoring programs in identifying which other areas are likely to be colonized by this invasive species [2]. Therefore, the information in the present paper is highly relevant to understand invasion features of this fish species throughout Iberian freshwaters.

Consequently, the aim of the present study was to assess the interpopulation variability in dietary traits of invasive bleak across the Iberian Peninsula. For this purpose, bleak populations were compared among the main rivers within this region, along with a native bleak population from France. Specifically, diet composition was analyzed by means of two overall indices: percentages of occurrence and ingested mass; and four dietary parameters were examined: ingested mass, prey richness, trophic diversity and trophic niche breadth.

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

#### *2.1. Study Areas*

Five main Iberian rivers were sampled for bleak along a latitudinal gradient (from North to South): Ebro (41◦47′30" N–1◦05′24" W and 41◦26′39" N–0◦28′26" W), Tagus (39◦49′07" N–4◦20′25" W and 39◦58′12" N–4◦42′17" W), Guadiana (38◦59′58" N–5◦51′52" W and 38◦50′18" N–6◦13′53" W), Segura (38◦06′05" N–1◦17′50" W and 38◦04′54" N–0◦53′37" W) and Guadalquivir (37◦37′01" N–5◦35′45" W and 37◦30′46" N–5◦56′38" W). These geographic coordinates correspond to two sampling sites per river (first coordinates are for sites located upstream). For comparative purposes, we surveyed a main river within the bleak's native range close to the Pyrenees, i.e., historically a potential 'donor region' for non-native fish introductions to the Iberian Peninsula along the so-called 'Perpignan–Barcelona corridor' (see Clavero and García-Berthou [17] for details on invasion routes). Thus, the River Saône (Rhône drainage, eastern France) was selected as a 'reference' population, although only one site (47◦02′13" N–5◦06′52" E) was finally surveyed. In any case, a comparable number of bleak specimens (200 individuals, see below) were collected from the River Saône and data from the two sampling sites in Iberian rivers were pooled for statistical analyses, as no 'site effect' was found (see Data Analyses

below). All the study rivers were selected because they represent large water courses (i.e., >500 km river length), where bleak are widely distributed and reach relatively high abundances within the local fish assemblages. A final key point for selection of these particular Iberian rivers was that bleak were sequentially introduced into these different catchments and thus, their populations will potentially reflect specific 'invasion stages' on the examined dietary traits (e.g., Závorka et al. [18]). Specifically, bleak were introduced into the study rivers in the following years (first mention) [7]: Ebro in 1992, Guadiana in 1999, Segura in 2004, Tagus in 2005 and Guadalquivir in 2006. The climate in the study area of the River Saône is Temperate Oceanic (800–1000 mm of mean annual rainfall, 10–13 ◦C mean annual temperature) [19], the most common climatic conditions for the bleak's native range in Europe [6]. The climate regime for the study Iberian rivers is typical Mediterranean, with rainfall concentrated in autumn–winter (≈500 mm) and intense summer drought (<100 mm). The mean annual temperature ranges between 15–18 ◦C. The lowest temperatures occur in winter (down to −5 ◦C) and the highest in summer (>35 ◦C) [20]. The bed geomorphology was similar between rivers, mainly consisting of pebbles and gravel, although the fraction of silt was slightly higher in the River Saône.

#### *2.2. Field and Laboratory Work*

Fish were collected from May to June 2019, just before the spawning period of bleak for each study area (authors, pers. obs.), thereby avoiding any effect of the 'reproductive status' on foraging habits. Moreover, the year 2019 is considered to have been hydrologically 'average' in the study areas [19,20]. As a result, the effects of particular dry or wet years on dietary traits are avoided within our study. Sampling sites (*n* = 11, two per Iberian river plus one in the River Saône) were selected to encompass similar environmental conditions. Specifically, sites were located in well-regulated middle reaches of the main channel, where water level fluctuations are controlled throughout the year, avoiding the effects of strong increases/decreases in river discharge. Water velocity was registered in each site to check the values were similar among populations for a better comparability. These habitat conditions are representative of large rivers in both the native and Iberian ranges. Also, sampling sites were located in the vicinity of similar surroundings (e.g., land use for agricultural exploitation) and far from the influences of main tributaries and towns. Sites were separated >50 km within each Iberian river to ensure the data were more representative from the study areas and also to minimize data dependence among sampling sites per river. Given that water temperature has a strong influence on fish metabolism and food digestion, this parameter was measured per site to check that variability was in accordance with the study latitudinal gradient (i.e., colder waters to the North and warmer waters to the South). A variety of consistent sampling protocols was followed by wading and from boats according to the European legislation (CEN/ISO Standards, EC Directive 2014/101/UE [21]). This allowed obtaining a representative sample of bleak across the broadest possible body size range from each river. Catch methods consisted of following a zigzagging and upstream direction in both banks at each site (100-m river length) by electrofishing (2000 W pulse DC generator at 200–250 V, 2–3 A, 30 min per bank), dip nets (1.5-m-long pole, 30-cm-diameter net, 10-mm mesh size), seine nets (20 × 2 m, 10-mm mesh size) and gill nets (20 × 1 m, 10-mm mesh size, 50% hanging ratio, 1.5-m deep). All surveys followed the same sampling protocols (e.g., proportional effort in terms of people and time) to ensure comparability among the study rivers. Additionally, a professional fisherman sampled for bleak in the River Saône. To encompass the existing environmental variability, fish were collected from all meso-habitats present in the study rivers (e.g., runs, pools, shallows), as bleak can be found under contrasting river conditions in the Iberian Peninsula [10,14].

After each survey was concluded, bleak were immediately euthanized by immersion in an overdose solution of anesthetic (MS-222) for 15 min, followed by severance of the spinal cord. Fish were stored on ice during transport to the laboratory (<2 h within the same sampling date). Individuals of the remaining fish species were identified and counted. Native species were kept in a tank with supplied oxygen (two battery-operated aerators with portable pumps) until fully recovered before being released. Other non-native species were euthanized (see details above). All field procedures complied with animal use and care regulations of Europe (specific licenses were granted for scientific field research). Fishes were collected by trained personnel and thus, no adverse effects were caused on the wildlife/habitat in the sampling sites, with all native fish being fully recovered.

On arrival at the laboratory, bleak individuals (*n* = 1200, 100 ind. × 2 sites × 5 Iberian rivers + 200 ind. × 1 site from the River Saône) were measured for standard length (SL, ±1 mm). This particular fish length was selected because it avoids 'noise' given by variation of caudal fin length not related to body size (e.g., wounds and cuts in the fish skin and rays). Specifically, bleak size ranged between 40–195 mm SL. Fish were dissected to examine the sex and collect the digestive system. Only the anterior one-third of the intestinal tract was preserved in 4% formalin for subsequent examination to avoid food remains severely digested (see a similar procedure in Latorre et al. [13]). Food items were identified to the lowest possible taxonomic level (e.g., Tachet et al. [22]) using a dissecting microscope (40×) and weighed using an electronic balance (±0.1 mg).

#### *2.3. Data Analyses*

Preliminary analyses did not find any difference between the sexes for the examined dietary variables (all *p*-values > 0.05). Consequently, this categorical factor was not included in subsequent data analyses to simplify the analytical models and thus, increase the statistical power of the remaining sources of variation, which would otherwise be seriously compromised (see a similar procedure in Alcaraz and García-Berthou [23]). Given that percentages of empty stomachs were similar across populations (25–35%), this parameter was not statistically analyzed. In addition, data were pooled per Iberian river because the effect of 'sampling site' within each water course was not significant for any dietary variable (all *p*-values > 0.05), after performing Generalized Linear Mixed Models (GLMMs) with 'site' as the random factor (see a comprehensive review of this statistical technique in Johnson et al. [24]). This analytical approach (i.e., pooled data) considerably improved the clarity of results and also facilitated biologic interpretation.

Two overall dietary indices were calculated (omitting empty guts) and expressed as a percentage for each food category (e.g., Latorre et al. [13]): 'occurrence' (frequency of fish guts in which a particular food category occurred relative to the total number of fish individuals) and 'ingested mass' (frequency of the mass of a particular food category relative to the total ingested mass in all the examined intestinal tracts). Both of these percentages indicate whether a given food item is commonly eaten within the population and whether this food category is energetically important to the population. Furthermore, four dietary parameters were also calculated for each fish (e.g., see Almeida et al. [16] for details on the particular formulae): ingested mass (mg), prey richness (*S*), trophic diversity (Shannon index, *H'*) and trophic niche breadth (standardized Levin's index, *B*). These parameters were selected as different measures of trophic plasticity to avoid potential bias of using only one parameter. Consequently, any macroecological pattern (e.g., latitudinal gradient) in dietary traits will be much clearer revealed if profiles among rivers are similar for several parameters.

Given that previous studies revealed significant relationships between dietary parameters and bleak size (an approach of age for fish) in Iberian waters (e.g., Almeida et al. [14]), linear regressions were used, which showed similar slopes among the populations (all *p*-values > 0.05), with low and positive values. Consequently, analysis of covariance (ANCOVA) was used to reveal significant differences between populations (i.e., rivers) for the four dietary parameters. The effect of body size was controlled by using SL as the covariate. ANCOVAs were followed by a post hoc Tukey–Kramer honestly significant difference (HSD) test. Data were transformed by using ln (x + 1). Assumptions of normality of residuals and homogeneity of variances were verified through residual plots. Statistical analyses were performed with R version 3.1.3 [25]. The significance level was set at α = 0.05. Sequential Bonferroni corrections were performed for every set of multiple tests.

#### **3. Results**

Diptera larvae (e.g., Chironomidae and Simuliidae), zooplankton (e.g., Cladocera water fleas) and detritus were the most frequent food categories for the reference native population (i.e., the River Saône), both in terms of occurrence and ingested mass (Table 1). The Ebro population showed a similar diet composition as per native bleak, although Ephemeroptera and Plecoptera nymphs were also important prey items. For the rest of study rivers, percentages of food categories were highly variable (Table 1). More in detail to be highlighted per river: vegetation and other benthic invertebrates (e.g., freshwater snails) were frequent in the River Tagus; flying insects (e.g., wasps, mosquitoes or butterflies) and Diptera larvae were important in the Guadiana and Segura populations, respectively; benthic invertebrates (e.g., Diptera larvae, insect nymphs, Mollusca) and nektonic insects (e.g., water boatmen or predatory beetles) were very important in terms of ingested mass (>40% and ≈20%, respectively) for the River Guadalquivir. Detritus was an important food category for all bleak populations: 21–42% in occurrence and 13–27% as ingested mass (Table 1).

**Table 1.** Diet composition of bleak *Alburnus alburnus* in the study rivers. Percentages of occurrences (Oc., %) and ingested masses (Mass, %) are presented.

a Acari, Cladocera and Copepoda; b Mollusca, Oligocheta and Coleoptera larvae; c Corixidae, Gerridae and Dytiscidae adults; d Hymenoptera, Diptera and Lepidotera; e Araneae and Formicidae.

Regarding dietary parameters and after accounting for fish length, significant differences were found between bleak populations for ingested mass (F5,812 = 32.94, *p* < 0.001). Four distinct groups were found (from low to high adjusted mean): Saône/Segura (≈12 mg), Guadalquivir (≈25 mg), Guadiana (≈40 mg) and Tagus (almost 60 mg) (Figure 1a). Differences were also found for prey richness (F5,812 = 18.68, *p* < 0.001). Two river groups were found: Ebro/Saône (*S* ≈ 3–4 prey items) and Guadiana/Guadalquivir/Tagus (*S* ≈ 5 prey) (Figure 1b). Significant differences were found between populations for trophic diversity (F5,812 = 19.76, *p* < 0.001). Two river groups were found: Saône/Ebro/Guadalquivir (*H'* ≈ 1) and Guadiana/Tagus (*H'* > 1.5) (Figure 1c). Significant differences were also observed between rivers for trophic niche breadth (F5,812 = 20.49, *p* < 0.001). Two distinct groups were found: Saône (*B* ≈ 1.4) and Tagus/Guadiana (*B* > 2); while the remaining three rivers showed intermediate *B* values (1.6–1.7) (Figure 1d).

were found (from low to high adjusted mean): Saône/Segura (≈12 mg), Guadalquivir (≈25 mg), Guadiana (≈40 mg) and Tagus (almost 60 mg) (Figure 1a). Differences were also found for prey

≈

≈

≈

≈

**Figure 1.** *Cont.*

**Figure 1.** Comparison of dietary parameters for bleak *Alburnus alburnus* among the study rivers. Results are adjusted means ± SE (ANCOVA, covariate: SL). Letters above bars indicate significant differences between bleak populations (Tukey's HSD tests, *p* < critical *p*-value from Bonferroni correction). (**a**): ingested mass. (**b**): prey richness. (**c**): trophic diversity. (**d**): trophic niche breadth.

#### **4. Discussions**

et al. [4]; Vašek and Kubečka [5]), whereas bleak diet has received less attenti Given that the bleak mainly inhabits stillwaters in its native area, this species is well-adapted to play an ecological role as an openwater feeder, with diet being chiefly based on zooplankton [4,5]. Similarly, planktonic Crustacea were very important for native bleak in the River Saône. However, the main food categories were Diptera larvae and detritus (i.e., benthic food items) for this reference population. This discrepancy between scientific literature and the present results for the native area was probably because most studies on bleak have been performed in lakes and reservoirs (e.g., Vinni et al. [4]; Vašek and Kubeˇcka [5]), whereas bleak diet has received less attention in flowing waters.

However, even in lentic environments, bleak can modulate this zooplanktivorous strategy at each particular habitat patch to increase resource partitioning and consequently, reduce competition with coexisting cyprinid fishes, such as roach *Rutilus rutilus* (L., 1758) or freshwater bream *Abramis brama* (L., 1758) [4,5]. Dietary indices were more similar between the River Saône and the River Ebro, the (geographically) closest study area to the reference population. This was also observed by Latorre et al. [26]) for other biologic traits (i.e., growth and reproduction), which was explained in terms of climatic and hydrological 'proximity'. However, the intake of Diptera larvae and insect nymphs was different between the Saône and Ebro populations. In the River Saône, particle size was finer (see Methods section), with these bed conditions being very suitable for Diptera larvae (J. Cucherousset, pers. obs.), whereas substrate was coarser in the River Ebro (see Methods section), providing a high availability of nymphs (R. Miranda, pers. obs.). Other food items were prominent among the study populations, including a wide variety of trophic resources in terms of contrasting ecological features: vegetation, neuston, nekton and benthos. Thus, consumption of algae and plant material was higher in the River Tagus. In this respect, Vinni et al. [4] demonstrated that intra- and mainly inter-specific competition were key causes to understand this result (i.e., high use of vegetation) in the native area. In relation to this finding, Tagus River showed the highest value for ingested mass, resulting in only one 'statistical group'. This may compensate a higher proportion of vegetation in the diet (see Results above). As a support for this interpretation, Latorre et al. [26] did not find any effect of this 'low nutritious' feeding type (vegetation) on the growth rate in the River Tagus, with this population showing the highest values. Particularly for detritus, bleak showed a high consumption in the study Iberian rivers, which was also found elsewhere within this ecoregion, from streams to reservoirs [13,14]. As a potential explanation, bleak may accidentally ingest plant material and detritus while feeding on sheltered invertebrates (D. Verdiell-Cubedo, pers. obs.). In the Guadiana population, bleak took advantage of prey from the water surface, such as fallen flying arthropods (D. Almeida, pers. obs.), which results in a low effort in terms of energy investment. Bleak caught prey from the water column in the River Guadalquivir, with this population displaying a 'costlier' foraging alternative, as nektonic insects (e.g., adult Dytiscidae beetles) require higher pursuit and capture efforts. However, more important, bleak fed on a great variety of benthic prey, from insect nymphs and larvae to mollusks and annelids, in all river populations. This finding indicates an elevated capacity of this fish to use food resources apparently less suitable to its morphologic adaptations (e.g., conspicuous superior mouth). Overall, these results suggest that bleak could deeply change the foraging strategy, from a 'pelagic' to a 'benthic' feeder, even within the same population (see Results for benthos and nekton in the River Guadalquivir). This capacity may aid bleak to better thrive in contrasting Iberian rivers, where zooplankton availability may be a more limited trophic resource [14]. Such a significant dietary shift has been also observed in the Iberian Peninsula for other invasive fishes adapted to a more stable hydrological regime in their native ranges, although they are phylogenetically and geographically very 'distant' to the bleak (e.g., North American centrarchids, see Almeida et al. [16]).

Regarding dietary parameters, variation ranges for the four examined predictors were similar as per other study areas in the Iberian Peninsula (Latorre et al. [13] in the NE; Almeida et al. [14] in the SW). The present data were obtained from large rivers and the just mentioned studies included streams and reservoirs. Thus, these overall results indicate that Iberian bleak populations possess a high capacity of adapting to the particular habitat conditions where this invasive fish inhabits. No clear pattern was observed for any dietary parameter in relation to the year of introduction, which was in accordance with Latorre et al. [26] for growth and reproduction traits. This lack of relationship is probably because the establishment stage was reached in a few years after bleak introductions (e.g., Bøhn et al. [27]). As an example, a rapid and wide establishment of this species has been recently demonstrated in the River Segura [10]. Nevertheless, it must be clearly stated that data were limited (*n* = five Iberian rivers). However, unlike the year of introduction, the foraging strategy appeared to show a slight relationship with the latitudinal gradient, specifically a unimodal response [28], with Guadiana and mainly Tagus reaching the maximum values for all dietary parameters. In addition, the Tagus population showed the highest

backcalculated lengths at all ages and growth index among the same study rivers (see Latorre et al. [26]). A potential explanation is that bleak may be more favored by a moderate Mediterranean climate in central Iberia during the prespawning period (warmer) than temperate conditions in the native area (colder). Yet, bleak are affected by slightly more 'severe' ecological conditions in southern Iberia, e.g., oxygen level was lower because of higher temperature (physicochemical data not shown, but in accordance with the expected latitudinal gradient) [29]. More in detail, these 'benign' conditions in central Iberia may improve the physiological status (e.g., metabolic rate) of bleak, which facilitates this fish species to use alternative prey and widen its diet, with the corresponding increase in the trophic variability. In contrast to this 'generalist' strategy, more northerly and southerly populations (i.e., distant from the optimum in terms of habitat quality, see theoretical aspects in Oksanen and Minchin [28]) displayed a more 'specialist' strategy, reducing the complexity of diet composition. Nevertheless, these ecological interpretations must be understood with caution, as similarly to the previous statement, data were limited to a few locations (*n* = five Iberian rivers) and moreover, the assessed latitudinal amplitude was relatively narrow (only 10◦ of Latitude, 37–47◦ N).

Ecological responses such as dietary traits usually show wide variability in non-native species when invading new habitats, with this being particularly clear in freshwater fishes [13,15,16]. In the Iberian Peninsula, invasive fishes usually display wide trophic plasticity under contrasting environmental conditions, which contributes to improve their invasion process [3,13,14]. Accordingly, dietary traits were clearly variable among the study rivers, indicating a wide 'flexibility' in bleak foraging strategies dependent on particular river conditions (i.e., habitat heterogeneity, food supply). Studies on bleak diet exist across Iberia in which resource availability were not assessed [13,14]). However, other studies on feeding habits of invasive fishes truly estimated food supply as a measure of trophic resources in this ecoregion (e.g., Almeida et al. [16,30]), comparing this availability to the use (i.e., bleak diet), and specifically calculating electivity indices (i.e., selection). The present study did not assess resource availability because of technical restrictions (in terms of materials and personnel) to properly quantify the biomass of plant, benthos and plankton in large rivers, which poses a strong study limitation. Thus, abundance of contrasting food items is a key factor to be analyzed for future research on bleak invasion. Similarly—as per other biologic attributes (e.g., growth and reproduction)—the high interpopulation variability is considered a mechanism for bleak to successfully invade novel Mediterranean freshwater ecosystems, from streams to large rivers [8,10,26]. Indeed, Iberian endemic species are specialized to narrow trophic niches [1] or alternatively, these fishes may display high plasticity to the natural variability in Mediterranean rivers. Irrespective of this specific mechanism, the low level of coevolutionary competition is a potential cause why Iberian fish fauna is not adapted to strong invasive competitors, such as bleak. This is one of the main reasons to consider this fish species as a bioinvader of high risk in the Iberian Peninsula (see Table 1 in Almeida et al. [2]). Consequently, environmental managers should apply urgent conservation measures to control bleak populations across Iberian freshwaters. First, these measures should focus on the target species (i.e., fish culling). Second—and according to other studies on bleak in Iberian waters [9]—actions on aquatic habitats should be implemented to restore the 'Mediterranean conditions' of natural flow regimes that benefit native fish communities and hamper invasive species.

**Author Contributions:** Conceptualization, D.L., G.M., A.V.-G. and D.A.; methodology, D.V.-C., E.G.-B., J.C., E.d.S., C.F.-D., R.M., F.J.O.-P., A.R.-N., J.M.S.; software, D.L., G.M. and D.A.; validation, D.L., G.M., D.A.; formal analysis, D.L., G.M., A.H. and D.A.; investigation, D.L., G.M., A.H. and D.V.-C.; resources, D.V.-C., E.G.-B., J.C., E.d.S., C.F.-D., R.M., F.J.O.-P., A.R.-N., J.M.S.; data curation, D.L., G.M., G.C.-G., A.G.G.-R., E.N.B.-B.; writing—original draft preparation, D.L., G.M., A.V.-G. and D.A.; writing—review and editing, D.V.-C., G.C.-G., E.N.B.-B., A.G.G.-R., E.G.-B., J.C., E.d.S., C.F.-D., R.M., F.J.O.-P., A.R.-N., J.M.S.; visualization, D.L., G.M., G.C.-G., E.N.B.-B., A.G.G.-R., A.V.-G. and D.A.; supervision, A.V.-G. and D.A.; project administration, D.A.; funding acquisition, E.G.-B., J.C., E.d.S., C.F.-D., R.M., F.J.O.-P., A.R.-N., J.M.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was partly supported by the Spanish Ministry of Education (FPU 13/03291), the Spanish Ministry of Economy and Competitiveness (projects CGL2015-69311-REDT and CGL2016-80820-R) and the Government of Catalonia (ref. 2014 SGR 484).

**Acknowledgments:** Arlo Hinckley held a 'collaboration' grant from the Complutense University of Madrid (Spain). Gema Castillo-García held a 'pre-graduate' grant from the Universidad San Pablo-CEU (Spain). The anonymous reviewers improved the manuscript.

**Conflicts of Interest:** The authors declare no conflicts 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.

#### **References**

© 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 (http://creativecommons.org/licenses/by/4.0/).


**7. Energy consumption issues in wireless sensor network**

Energy consumption is the most important factor to determine the life of a sensor network because usually sensor nodes are driven by battery. Sometimes energy optimization is more complicated in sensor networks because it involved not only reduction of energy consumption but also prolonging the life of the network as much as possible. The optimization can be done by having energy awareness in every aspect of design and operation. This ensures that energy awareness is also incorporated into groups of communicating sensor nodes and the entire network and not only in the individual nodes (Bharathidasan et al. 2001).

A sensor node usually consists of four sub-systems (Bharathidasan et al. 2001):

 a power supply subsystem : It consists of a battery which supplies power to the node. It should be seen that the amount of power drawn from a battery is checked because if high current is drawn from a battery for a long time, the battery will die faster even though it could have gone on for a longer time. Usually the rated current capacity of a battery being used for a sensor node is less than the minimum energy consumption. The lifetime of a battery can be increased by reducing the current drastically or even turning it off often.

To minimize the overall energy consumption of the sensor network, different types of protocols and algorithms have been studied so far all over the world. The lifetime of a sensor network can be increased significantly if the operating system, the application layer and the network protocols are designed to be energy aware. These protocols and algorithms have to be aware of the hardware and able to use special features of the micro-processors and transceivers to minimize the sensor node's energy consumption. This may push toward a custom solution for different types of sensor node design. Different types of sensor nodes deployed also lead to different types of sensor networks. This may also lead to the different types of collaborative algorithms in wireless sensor networks arena.


Meet the editor

Nagehan Ersoy Tunalı received her Ph.D., M.Sc., and B.Sc. degrees in Molecular Biology and Genetics from the Boğaziçi University, İstanbul (TR). Her Ph.D. work involved "Molecular Analysis of Polyglutamine Diseases and Investigation of the Interaction Between Huntingtin and Nuclear Receptor Corepressor". She had the opportunity to gain experience in Huntington's Disease (HD) research at the University of Manchester (UK),

CNR-Istituto di Medicina Sperimentale e Biotecnologie (IT) and the University of Wales College of Medicine (UK). She served as the Editor-in-Chief of the Journal of Cell and Molecular Biology between 2006 and 2016. Nagehan ERSOY TUNALI is currently conducting research on genetic modifiers of HD, localization and interactions of huntingtin, molecular mechanisms of excitotoxicity in HD, diagnostic biomarker discovery in AD, and nanotechnology-based therapeutic approaches in HD and AD.

Contents

**Section 1**

*by Rajani Singh*

*by Anoop T. Chakrapani*

Mechanisms and Similarities

*and Oguz Osman Erdinc*

EEG Biomarker for Alzheimer's Disease

*by Eugenie Nepovimova and Kamil Kuca*

*by Demet Ilhan Algin, Demet Ozbabalık Adapinar*

**Section 2**

**Preface XI**

Brain and Its Neurodegeneration **1**

**Chapter 1 3**

**Chapter 2 17**

**Chapter 3 29**

and Treatment **55**

**Chapter 4 57**

**Chapter 5 69**

**Chapter 6 77**

Cerebellum: Its Anatomy, Functions and Diseases

of Addiction and Its Relation to Neurodegeneration *by Maria Uscinska, Nicolo' Gagliano and Frank Ho-Yin Lai*

The Brain Stress System in the Neurobiology of the "Dark Side"

Biomarkers of Diseases: Their Role in Emergency Medicine

Alzheimer's Disease: Molecular Mechanisms, Biomarkers

Alzheimer's Disease and Type 2 Diabetes Mellitus: Molecular

Multi-Target-Directed Ligands in Alzheimer's Disease Therapy

*by Karla Isabel Lira-De León, Alma Delia Bertadillo-Jilote, David Gustavo García-Gutiérrez and Marco Antonio Meraz-Ríos*

### Contents

Preface

Neurodegeneration is a broad term used to describe a range of processes that result in dysfunction and eventually death of the neurons. Neurodegenerative diseases represent a very large group of heterogeneous disorders affecting specific subtypes of neurons in the brain. Neurodegenerative diseases are listed among the leading causes of death and pose a serious public health challenge. Recently, the rising global burden of incurable neurodegenerative diseases has forced governments to allocate their financial and scientific resources to neurodegeneration research all

This book contributes both to the awareness of the anatomy and functions of our mysterious brain and its neurodegenerative states. In the first section of the book, our mini-brain, the cerebellum and its involvement in neurodegeneration are introduced first, since it has major roles in coordination, posture, balance, and important mental processes. In addition to that, the brain stress system and its contribution to neurodegeneration are discussed in a separate chapter. This section is finalized with a chapter involving biomarkers in emergency medicine and neurodegeneration, which will help clinicians and researchers search for certain emergency biomarkers in neurodegenerative conditions. The proceeding sections of the book include the presentation of three widely known and extensively studied neurodegenerative diseases; Alzheimer's Disease, Huntington's Disease, and Amyotrophic Lateral Sclerosis. The main molecular mechanisms, related biomarkers, and current modern therapy strategies are introduced. In addition to those, the characteristics of prion proteins in healthy cellular states and their involvement in prion diseases are discussed. In the last section of the book, molecular mechanisms of epilepsy are introduced, based on the fact that it is commonly associated with various neurode-

generative alterations in brain areas involved in electrographic seizures.

therapeutic interventions in neurodegenerative diseases.

mysterious neurodegenerative disorders.

The book is intended to serve as a source to aid clinicians and researchers in the field, and also life science readers to increase their understanding and awareness of the clinical correlations, genetic aspects, neuropathological findings, and current

I would like to thank all the authors for their close cooperation and valuable work. I believe that this book will enlighten the curiosity for neurodegeneration and also encourage researchers to work on potentially effective molecular therapies for still

**Nagehan Ersoy Tunali, Ph.D.**

İstanbul Medeniyet University,

İstanbul, Turkey

Faculty of Engineering and Natural Sciences, Department of Molecular Biology and Genetics,

around the world.


**Perspectives and Challenges in Doctoral Research—Selected Papers from the 10th Edition of the Scientific Conference of the Doctoral Schools of "Dun ˘area de Jos" University of Galati (SCDS-UDJG)**

Editors

**Eugen Rusu Gabriela Rapeanu**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editors* Eugen Rusu "Dunarea de Jos" University of Galati Galat,i Romania

Gabriela Rapeanu "Dunarea de Jos" University of Galati Galat,i Romania

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Inventions* (ISSN 2411-5134) (available at: https://www.mdpi.com/journal/inventions/special\_issues/10th\_ Doctoral\_Research).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. *Journal Name* **Year**, *Volume Number*, Page Range.

**ISBN 978-3-0365-7892-7 (Hbk) ISBN 978-3-0365-7893-4 (PDF)**

Cover image courtesy of Gabriela Rapeanu

© 2023 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications.

The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND.


**7. Conclusions**

Although there is a higher and more favorable medicinal value in traditional medicines such as *Cordyceps sinensis*, the limitations should be considered as well in order to reveal its undiscovered potential of treating lipid-related diseases. However, as the availability of *Cordyceps sinensis* is gradually being decreased in the West, care should be taken when meeting the consumer demands, especially when it comes to the cultivation and growth of the herb. Moreover, as the natural *Cordyceps sinensis* is becoming rare, methods are in the process of being developed for the artificial production of this fungus with the same chemical constituents or even better. The world is now awakening to the importance of this fungus, and the price factor appears to play an important role in the marketplace. A lot of research is still pending on this fungus especially from the medical side. With the present research nevertheless, it is noteworthy that *Cordyceps sinensis* has been identified

**179**

**Author details**

Kanchana Samarasinghe and Viduranga Y. Waisundara\*

\*Address all correspondence to<EMAIL_ADDRESS>
provided the original work is properly cited.

Australian College of Business and Technology—Kandy Campus, Kandy, Sri Lanka

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

*Therapeutic Properties and Anti-Lipidemic Activity of* Cordyceps sinensis

as a traditional medicine which is able to regulate lipid levels and demonstrated anti-lipidemic activity. Cordycepin appears to be an important bioactive compound

elucidate more bioactive compounds existing in *Cordyceps sinensis* which are able to collectively involve in lipid metabolism and regulation. The potency of *Cordyceps sinensis* would highly depend on its chemical constituents, viz., the cordycepin and polysaccharides that make up the fruiting body, mycelium, or spores. However, further experimental, epidemiological, and clinical studies are needed to identify other molecular targets, resolve the relationships between *Cordyceps sinensis* intake and anti-lipidemic activity, and explore the optimum dosing, efficacy, and safety alone and in combination with existing anti-cholesterolemic and anti-

The authors wish to thank the Australian College of Business & Technology,

Kandy Campus, for the facilities provided to put this write-up together.

The authors declare no conflicts of interest, financial, or otherwise.

in this aspect, and it is without a doubt that further research will be able to

*DOI: http://dx.doi.org/10.5772/intechopen.92616*

triglyceridemic pharmaceutical therapies.

**Acknowledgements**

**Conflict of interest**

*Therapeutic Properties and Anti-Lipidemic Activity of* Cordyceps sinensis *DOI: http://dx.doi.org/10.5772/intechopen.92616*

as a traditional medicine which is able to regulate lipid levels and demonstrated anti-lipidemic activity. Cordycepin appears to be an important bioactive compound in this aspect, and it is without a doubt that further research will be able to elucidate more bioactive compounds existing in *Cordyceps sinensis* which are able to collectively involve in lipid metabolism and regulation. The potency of *Cordyceps sinensis* would highly depend on its chemical constituents, viz., the cordycepin and polysaccharides that make up the fruiting body, mycelium, or spores. However, further experimental, epidemiological, and clinical studies are needed to identify other molecular targets, resolve the relationships between *Cordyceps sinensis* intake and anti-lipidemic activity, and explore the optimum dosing, efficacy, and safety alone and in combination with existing anti-cholesterolemic and antitriglyceridemic pharmaceutical therapies.


Raman Frequnecy (THz)

frequency (THz)

R

110

115

Fig. 4. CARS spectrum of GaSe showing the optical phonon band near 0.6THz

$$|\left.\chi^{(3)}\right|^2 = |\left.\chi\_N^{(3)} + \left.\chi\_R^{(3)}\right|^2$$

$$= |\left.\chi\_N^{(3)}\right|^2 + \left.\chi\_N^{(3)}\right|\frac{a(o\_d - o\_R)}{\left(o\_d - o\_R\right)^2 + \Gamma^2} + \frac{a^2}{\left(o\_d - o\_R\right)^2 + \Gamma^2} \tag{7}$$

Here, (3) *N* is the non-resonant third order susceptibility, which is assumed to be a real constant, while the resonant third order susceptibility (3) *R* is given in the following form:

$$\mathcal{Z}\_{\mathbb{R}}{}^{\langle 3 \rangle} \{ \alpha\_{d} = \alpha\_{p} - \alpha\_{\mathbb{S}} \} = \frac{a \{ \alpha\_{d} - \alpha\_{\mathbb{R}} + i \Gamma \}}{\left( \alpha\_{d} - \alpha\_{\mathbb{R}} \right)^{2} + \Gamma^{2}} \tag{8}$$

Here, 0 *d pS* is the difference frequency between the pump (Pump1) and Stokes (Pump2), *R* is the Raman resonance frequency, is the Raman linewidth, and *a* is the constant associated with the magnitude of the resonance. When the non-resonant contribution is large compared to the resonant one, we can neglect the last term in Eq.(7). Then we obtain

$$\text{CARS} \propto \left| \, \mathbb{Z}\_N^{(3)} \right|^2 + 2 \mathbb{Z}\_N^{(3)} \frac{a(a\_d - a\_{\mathbb{R}})}{\left(a\_d - a\_{\mathbb{R}}\right)^2 + \Gamma^2} = \left| \, \mathbb{Z}\_N^{(3)} \right|^2 + 2 \mathbb{Z}\_N^{(3)} \frac{a \Delta a o}{\Delta a^2 + \Gamma^2} \tag{9}$$

Here, *d R* is the detuning from the resonant Raman frequency. The schematic illustration of the CARS signal interfering with the resonant and non-resonant components is shown in Fig. 5 The curve fit using eq.(3) is shown in Fig. 4 as the dashed line. We have estimated the Raman linewidth = 0.02 THz (=0.67 cm-1) and the ratio of the resonant to the non-resonant contribution, given by the parameter (3) *a* /( ) *N* , to be 12%. The discrepancy between the theoretical curve fit and the observed spectrum in the low-frequency side is due to the drift of the non-resonant background signal caused by the fluctuation of the laser intensity.

Fig. 5. The structure of CARS spectrum resulting from interference between the nonresonant and resonant components

#### **3.2 Time-domain inverse Raman spectroscopy (IRS) for GaSe**

Figure 6 shows the inverse Raman spectrum of the GaSe sample. The total pump power incident on the sample was about 1 mW. The signal loss of Pump1 was detected using lockin techniques by modulating Pump2 with a mechanical chopper. The IRS spectrum was taken by scanning the Pump2, Δ, with the probe optical delay set at the maximum SFG signal . As can be seen from Fig. 6, there are resonance peaks around 0.6 and 4.2 THz due to the E2g and A1g optical phonon modes, respectively. The resonance peak expected at 1.8 THz corresponding to an optical phonon (E1g-mode) in GaSe was not observed, probably due to the weak Raman scattering cross section. The inverse Raman signal is one order stronger than the CARS signal. Accordingly, the SNR of the inverse Raman signal is also larger. The reason for this is not clear at present.

Fig. 6. Inverse Raman spectra of GaSe

160 Vibrational Spectroscopy

28 26 24 22 20 18 16 14 Optical probe delay

0.8 0.7 0.6 0.5 0.4

*dR dR*

*N* is the non-resonant third order susceptibility, which is assumed to be a real

( ) ( ) ( )

constant associated with the magnitude of the resonance. When the non-resonant contribution is large compared to the resonant one, we can neglect the last term in Eq.(7).

*d R*

illustration of the CARS signal interfering with the resonant and non-resonant components is shown in Fig. 5 The curve fit using eq.(3) is shown in Fig. 4 as the dashed line. We have estimated the Raman linewidth = 0.02 THz (=0.67 cm-1) and the ratio of the resonant to the

between the theoretical curve fit and the observed spectrum in the low-frequency side is due to the drift of the non-resonant background signal caused by the fluctuation of the laser

*d R*

*a*

2 2

*d R* is the detuning from the resonant Raman frequency. The schematic

*a*

*d R*

is the difference frequency between the pump (Pump1) and Stokes

*d R a i*

*R* is the Raman resonance frequency, is the Raman linewidth, and *a* is the

 CARS signal Theoretical fit

2

*R* is given in the following form:

*a*

*N* , to be 12%. The discrepancy

(9)

22 22

2 2

(8)

(3) (3) 2 2 2 | |2 *N N*

(7)

 (ps)

Raman frequency (THz)

(3) (3) (3) 2 2 | || |

 *N R*

( ) | | () () *d R*

*a*

Fig. 4. CARS spectrum of GaSe showing the optical phonon band near 0.6THz

(3) (3) 2

(3)

(3) (3) 2

( ) CARS | | 2 ( )

non-resonant contribution, given by the parameter (3) *a* /( )

*N N*

*R d pS*

*N N*

constant, while the resonant third order susceptibility (3)

Resonant E2g-mode

0

Here, (3)

(Pump2),

Then we obtain

Here,

intensity.

Here, 0

*d pS*

50

CARS signal (mV)

100

One of the advantages of the time-domain IRS (and SRGS) in comparison with the timedomain CARS (or CSRS) is that it is not necessary to scan the optical delay of the probe light simultaneously with . In addition, the IRS spectrum is symmetric about the

Low-Frequency Coherent Raman Spectroscopy Using Spectral-Focusing of Chirped Laser Pulses 163

\* \* 2112 1 2 2 3 cos sin *R R P EEE y*

> 2112 1111

*NR*

\* \*2 1111 1 2 2 3 cos / cos *NR NR P EEE*

The non-resonant background can be removed by placing an analyzer in front of the detector, with its polarization perpendicular to *P*NR. The total projection of the two components of *P*R [Eq. (11)] along the direction perpendicular to *P*NR can be written as

1111 1 2 2 3 (cos sin cos sin cos ) *R R P EEE*

2112 1111

*R*

*R*

is the depolarization ratio of the resonant IRS field, and is equal to the spontaneous Raman

0.4 0.5 0.6 0.7 0.8 0.9 1.0

Even as this technique causes a slight reduction of the resonant signal, a large amount of the non-resonant signal is suppressed. Figure 8 shows the inverse Raman spectrum using the polarization technique. As can be seen from the figure, the non-resonant component is well suppressed and the resonance peak is clearly seen at the optical phonon mode frequency

Raman Frequency (THz)

*R*

*NR*

1 3

 (14)

Polarizetion Inverse Raman GaSe t=1mm

Polarized

In the absence of electronic resonance, NR is a real quantity and independent of frequency.

1111 1 2 2 3 cos *R R P EEE x*

> > *NR*

*P*NR is therefore linearly polarized with an angle relative to the x axis,

where the angle is related to by tan =NR tan

depolarization ratio in the absence of electronic resonance.

Fig. 8. Polarized inverse Raman signal of GaSe from 0.4 to 1.0 THz

0

5

10

Lock-in Out (mV)

15

20

Here,

around 0.6 THz.

\* \*2

In this case the depolarization ratio of the non-resonant IRS field is

\* \*2

,

(12)

(15)

(13)

(11)

resonance frequency while that of CARS is not symmetric (dispersive shape). This is because the signal in an IRS spectrum corresponds to the imaginary part of the 3rd order nonlinear susceptibility (the same as the spontaneous Raman signal) while that of CARS corresponds to the real part, and this is the reason why it interferes with the signal from the real nonresonant nonlinear susceptibility. The non-resonant background signal is also dominant as in CARS and is probably due to other non-resonant four-wave mixing processes. Therefore, it is necessary to suppress this non-resonant background signal when performing spectroscopy of biomolecules and imaging where a higher SNR is required.

Fig. 7. Polarization vectors of Pump1 (*E***1**) and Pump2 (*E***2**), the non-resonant IRS signal (*P***NR**) and the analyser polarizer.

The polarized CARS (P-CARS) technique (Oudar et al., 1979; Cheng et al., 2001) can be applied to the inverse Raman measurement in order to suppress the non-resonant background signal. This is achieved by controlling the relative polarization angle between Pump1 and Pump2; that is, adjusting the polarization direction of the non-resonant and resonant signals. Owing to the polarization difference, it is possible to remove the polarization component of the non-resonant signal by placing a polarizing filter in front of the detector. The principle of polarization IRS method (P-IRS) is outlined as follows. Consider that Pump1 is linearly polarized along the x-axis and Pump2 is polarized along a direction, an angle relative to the x-axis as shown Fig.7. When the beat frequency generated by the two pump beams is resonant with a molecular vibration or an optical phonon vibration, the interaction of the pump beam in the sample induces a third order nonlinear polarization that contains a non-resonant part, *PNR*, and a resonant part, *PR*. The x and y components of the non-resonant part contributing to IRS can be written as

$$P\_x^{NR} = 3\,\chi\_{1111}^{NR} E\_1^\* E\_2 \, E\_2^\* \cos^2 \phi,$$

$$P\_y^{NR} = 3\,\chi\_{2112}^{NR} E\_1^\* E\_2 \, E\_2^\* \cos \phi \sin \phi \tag{10}$$

Similarly, the x and y components of the resonant part can be written as

$$P\_{\mathbf{x}}^{R} = \mathbf{3} \,\mathcal{Y}\_{1111}^{R} E\_1 \,^\* E\_2 \,^\* E\_2 \,^\* \cos^2 \phi$$

$$P\_{\mathbf{y}}^{R} = \mathbf{3} \,\mathcal{Y}\_{2112}^{R} E\_1 \,^\* E\_2 \,^\* E\_2 \,^\* \cos \phi \sin \phi \tag{11}$$

In the absence of electronic resonance, NR is a real quantity and independent of frequency. In this case the depolarization ratio of the non-resonant IRS field is

$$\boldsymbol{\rho}^{\rm NR} = \frac{\mathbb{Z}\_{2112}^{\rm NR}}{\mathbb{Z}\_{1111}^{\rm NR}} = \frac{1}{3} \tag{12}$$

*P*NR is therefore linearly polarized with an angle relative to the x axis,

$$P^{\rm NR} = \Im \chi\_{1111}^{\rm NR} E\_1 \, ^\ast E\_2 \, ^\ast E\_2 \, ^\ast \cos^2 \phi / \cos a \tag{13}$$

where the angle is related to by tan =NR tan

The non-resonant background can be removed by placing an analyzer in front of the detector, with its polarization perpendicular to *P*NR. The total projection of the two components of *P*R [Eq. (11)] along the direction perpendicular to *P*NR can be written as

$$P\_{\perp} = 3\,\chi\_{1111}^R E\_1 \,^\* E\_2 \,^\* E\_2 \,^\* \text{(}\cos^2\phi\sin a - \rho^R\cos\phi\sin\phi\cos a\text{)}\tag{14}$$

Here,

162 Vibrational Spectroscopy

resonance frequency while that of CARS is not symmetric (dispersive shape). This is because the signal in an IRS spectrum corresponds to the imaginary part of the 3rd order nonlinear susceptibility (the same as the spontaneous Raman signal) while that of CARS corresponds to the real part, and this is the reason why it interferes with the signal from the real nonresonant nonlinear susceptibility. The non-resonant background signal is also dominant as in CARS and is probably due to other non-resonant four-wave mixing processes. Therefore, it is necessary to suppress this non-resonant background signal when performing

*x*

*E***1**

*PNR*

analyzer

Fig. 7. Polarization vectors of Pump1 (*E***1**) and Pump2 (*E***2**), the non-resonant IRS signal (*P***NR**)

The polarized CARS (P-CARS) technique (Oudar et al., 1979; Cheng et al., 2001) can be applied to the inverse Raman measurement in order to suppress the non-resonant background signal. This is achieved by controlling the relative polarization angle between Pump1 and Pump2; that is, adjusting the polarization direction of the non-resonant and resonant signals. Owing to the polarization difference, it is possible to remove the polarization component of the non-resonant signal by placing a polarizing filter in front of the detector. The principle of polarization IRS method (P-IRS) is outlined as follows. Consider that Pump1 is linearly polarized along the x-axis and Pump2 is polarized along a direction, an angle relative to the x-axis as shown Fig.7. When the beat frequency generated by the two pump beams is resonant with a molecular vibration or an optical phonon vibration, the interaction of the pump beam in the sample induces a third order nonlinear polarization that contains a non-resonant part, *PNR*, and a resonant part, *PR*. The x and y components of

\* \*2

,

(10)

1111 1 2 2 3 cos *NR NR P EEE x*

2112 1 2 2 3 cos sin *NR NR P EEE y*

Similarly, the x and y components of the resonant part can be written as

spectroscopy of biomolecules and imaging where a higher SNR is required.

*E***2**

*y*

the non-resonant part contributing to IRS can be written as

\* \*

and the analyser polarizer.

$$\boldsymbol{\rho}^{\boldsymbol{R}} = \frac{\mathcal{Z}\_{2112}^{\boldsymbol{R}}}{\mathcal{Z}\_{1111}^{\boldsymbol{R}}} \tag{15}$$

is the depolarization ratio of the resonant IRS field, and is equal to the spontaneous Raman depolarization ratio in the absence of electronic resonance.

Fig. 8. Polarized inverse Raman signal of GaSe from 0.4 to 1.0 THz

Even as this technique causes a slight reduction of the resonant signal, a large amount of the non-resonant signal is suppressed. Figure 8 shows the inverse Raman spectrum using the polarization technique. As can be seen from the figure, the non-resonant component is well suppressed and the resonance peak is clearly seen at the optical phonon mode frequency around 0.6 THz.

Low-Frequency Coherent Raman Spectroscopy Using Spectral-Focusing of Chirped Laser Pulses 165

4 THz (A1g)

**0 5 10 15 20 25 30 35**

A crucial aspect of the coherent Raman spectroscopy system is its utilization of a femtosecond laser in conjunction with a novel time-domain detection scheme to acquire the sub-THz to THz coherent Raman spectrum. However, low signal strength from samples like biomolecules necessitates improving the SNR of the system. The efficiency of coherent Raman scattering and the SFG process in the signal detection is proportional to the cube and square of the excitation intensity, respectively. Therefore, the coherent Raman scattering signal detected in time-domain is proportional to the 4th power of the excitation intensity. Laser intensity fluctuations in turn, cause large fluctuations in the signal. This deteriorates the SNR, even if the signal itself is large. Moreover, the large fluctuation of the non-resonant signal compared to the resonant signal due to vibrational modes also results in a low SNR. In order to address these issues, the following points may be considered: (1) use a high repetition rate laser, (2) use balanced detection or obtain the difference between the signal and the reference, and (3) establish ways of measuring the differential signal in the time delay of Pump1 and Pump2 (equivalent to the differential signal in the frequency domain) through the modulation of their optical path lengths. If the repetition rate of the pump laser is too high, as in the case of a mode-locked Ti:sapphire laser oscillator (having typical repetition frequencies of 50~100 MHz), the energy per pulse is too small and the coherent Raman signal strength is too weak resulting to a low SNR. For this reason, the optimum pump source might be a femtosecond laser amplifier system with a repetition rate of ~100 kHz and with an energy per pulse of a few J, in order to maintain sufficient excitation

THz time-domain coherent Raman spectroscopy system using "spectral focusing" of a broadband femtosecond laser source was introduced. Although the system is still in its

Fig. 10. Inverse Raman signal from GaSe (*c*-cut, t = 1 mm) measured using a 40 fs pulse

**Time delay (ps)**

9.3 THz (A1g)

**0.0**

**5. Problem and future plan**

width laser

intensity.

**6. Conclusion**

**0.5**

**Loss signal (arb. unit)**

**1.0**

burst

0.6THz (E2g)

#### **4. Extension of measurement bandwidth**

The measurement bandwidth is dependent on the spectral bandwidth and thus the associated pulse width of the femtosecond laser. Thus, using a narrow pulse width femtosecond laser will give a broad measurement bandwidth. However, when using very short pulses (less than 100 fs), special care must be taken to suppress higher order dispersion in the stretcher configuration. With a grating pair such as the one shown in Fig.2 (the compressor part), optical pulses with negative chirp can be obtained. When a lens pair is inserted between the grating pair, the spatial image of the optical beam is inverted and a negative chirp is then reverted to a positive chirp. However, the insertion of a lens pair introduces higher order dispersion and spherical aberration, which cannot be compensated by the grating pair compressor. As a result, the pulse width after compression is broadened with the higher order dispersion and the time-resolution in the detection system deteriorates. To avoid dispersive optics in the stretcher system we adopted Öffner configuration (Öffner, 1971; Cheriaux et al., 1996), which consists of a grating and a combination of concave, convex and roof mirrors as shown in Fig. 9. The optical pulses hit the grating and the aspheric mirrors four times. By changing the offset distance Z1 of the grating position we can control the positive dispersion of the optical pulses.

Fig. 9. The Öffner configuration used as the stretcher. The point *O* is the center of curvature of both mirrors. The point *C* is the incident position of the optical pulses on the grating. The point C lies on the incidence plane of the optical pulses on the grating.

Figure 10 shows the measured inverse Raman spectrum from the GaSe using femtosecond laser pulses with a pulse width of 40 fs in the transform-limited condition. The vertical axis of the graph is the optical power loss in Pump1, corresponding to the inverse Raman signal, while the horizontal axis is the relative time-delay between the two pump pulses, which determines the beat frequency. The sharp peak at the zero time-delay is the burst signal of the overlapping pump pulses, which corresponds to zero-beat frequency. The small and sharp peak at 1.1 ps (~0.6 THz) is the optical phonon mode (*E2g* mode), corresponding sliding motion of layers with its plane perpendicular to the *c*-axis of GaSe. The apparent peak at 7.9 ps (4 THz) is the fully symmetric phonon mode (*A1g* mode) and the peak at 18.7 ps (9.3 THz) is another Raman active mode (*A1g* mode) (Wieting & Verble, 1972). Based on these bands and the associated non-resonant background signal, the frequency bandwidth of this system is estimated to be 15 THz (corresponding to ~30 ps time delay).

Fig. 10. Inverse Raman signal from GaSe (*c*-cut, t = 1 mm) measured using a 40 fs pulse width laser

#### **5. Problem and future plan**

164 Vibrational Spectroscopy

The measurement bandwidth is dependent on the spectral bandwidth and thus the associated pulse width of the femtosecond laser. Thus, using a narrow pulse width femtosecond laser will give a broad measurement bandwidth. However, when using very short pulses (less than 100 fs), special care must be taken to suppress higher order dispersion in the stretcher configuration. With a grating pair such as the one shown in Fig.2 (the compressor part), optical pulses with negative chirp can be obtained. When a lens pair is inserted between the grating pair, the spatial image of the optical beam is inverted and a negative chirp is then reverted to a positive chirp. However, the insertion of a lens pair introduces higher order dispersion and spherical aberration, which cannot be compensated by the grating pair compressor. As a result, the pulse width after compression is broadened with the higher order dispersion and the time-resolution in the detection system deteriorates. To avoid dispersive optics in the stretcher system we adopted Öffner configuration (Öffner, 1971; Cheriaux et al., 1996), which consists of a grating and a combination of concave, convex and roof mirrors as shown in Fig. 9. The optical pulses hit the grating and the aspheric mirrors four times. By changing the offset distance Z1 of the

grating position we can control the positive dispersion of the optical pulses.

Z1

Grating Grating image

point C lies on the incidence plane of the optical pulses on the grating.

O

C

Convex Mirror

Roof Mirror

Fig. 9. The Öffner configuration used as the stretcher. The point *O* is the center of curvature of both mirrors. The point *C* is the incident position of the optical pulses on the grating. The

Figure 10 shows the measured inverse Raman spectrum from the GaSe using femtosecond laser pulses with a pulse width of 40 fs in the transform-limited condition. The vertical axis of the graph is the optical power loss in Pump1, corresponding to the inverse Raman signal, while the horizontal axis is the relative time-delay between the two pump pulses, which determines the beat frequency. The sharp peak at the zero time-delay is the burst signal of the overlapping pump pulses, which corresponds to zero-beat frequency. The small and sharp peak at 1.1 ps (~0.6 THz) is the optical phonon mode (*E2g* mode), corresponding sliding motion of layers with its plane perpendicular to the *c*-axis of GaSe. The apparent peak at 7.9 ps (4 THz) is the fully symmetric phonon mode (*A1g* mode) and the peak at 18.7 ps (9.3 THz) is another Raman active mode (*A1g* mode) (Wieting & Verble, 1972). Based on these bands and the associated non-resonant background signal, the frequency bandwidth of this system is estimated to be 15 THz (corresponding to ~30 ps time delay).

Concave Mirror

**4. Extension of measurement bandwidth**

A crucial aspect of the coherent Raman spectroscopy system is its utilization of a femtosecond laser in conjunction with a novel time-domain detection scheme to acquire the sub-THz to THz coherent Raman spectrum. However, low signal strength from samples like biomolecules necessitates improving the SNR of the system. The efficiency of coherent Raman scattering and the SFG process in the signal detection is proportional to the cube and square of the excitation intensity, respectively. Therefore, the coherent Raman scattering signal detected in time-domain is proportional to the 4th power of the excitation intensity. Laser intensity fluctuations in turn, cause large fluctuations in the signal. This deteriorates the SNR, even if the signal itself is large. Moreover, the large fluctuation of the non-resonant signal compared to the resonant signal due to vibrational modes also results in a low SNR. In order to address these issues, the following points may be considered: (1) use a high repetition rate laser, (2) use balanced detection or obtain the difference between the signal and the reference, and (3) establish ways of measuring the differential signal in the time delay of Pump1 and Pump2 (equivalent to the differential signal in the frequency domain) through the modulation of their optical path lengths. If the repetition rate of the pump laser is too high, as in the case of a mode-locked Ti:sapphire laser oscillator (having typical repetition frequencies of 50~100 MHz), the energy per pulse is too small and the coherent Raman signal strength is too weak resulting to a low SNR. For this reason, the optimum pump source might be a femtosecond laser amplifier system with a repetition rate of ~100 kHz and with an energy per pulse of a few J, in order to maintain sufficient excitation intensity.

#### **6. Conclusion**

THz time-domain coherent Raman spectroscopy system using "spectral focusing" of a broadband femtosecond laser source was introduced. Although the system is still in its

Low-Frequency Coherent Raman Spectroscopy Using Spectral-Focusing of Chirped Laser Pulses 167

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#### **7. Acknowledgment**

The Authors are grateful to the "SAKIGAKE (PREST)" Grant in the research area "Life Phenomena and Measurement Analysis" by the Japan Science and Technology Agency, and the Grants-in-Aid for Scientific Research (KAKENHI) (B) program by the Japan Society for the Promotion of Science (JSPS).

#### **8. References**

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166 Vibrational Spectroscopy

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### *Edited by Dominique de Caro*

The infrared and Raman spectroscopy have applications in numerous fields, namely chemistry, physics, astronomy, biology, medicine, geology, mineralogy etc. This book provides some examples of the use of vibrational spectroscopy in supramolecular chemistry, inorganic chemistry, solid state physics, but also in the fields of moleculebased materials or organic-inorganic interfaces.

Vibrational Spectroscopy

Vibrational Spectroscopy

*Edited by Dominique de Caro*

Photo by joruba / iStock


**4. Freshwater wetlands**

Wetlands are defined as permanently or intermittently wet areas, shallow water and land water margins that support a natural ecosystem of plants and animals that are adapted to wet conditions. They support a wide range of plants and animals. In New Zealand, wetland plants include 47 species of rush and 72 species of native sedge (Johnson & Brooke, 1998). Many of these plants have very specific environmental needs, with a number of plants species adapted to wet and oxygen deprived conditions. Wetlands support a high proportion of native birds, with 30% of native birds compared with less than 7% worldwide (Te Ara – the Encyclopedia of New Zealand, 2009). For instance, the australasian bittern (*Botaurus poiciloptilus*), brown teal (A*nas chlorotis*), fernbird (*Bowdleria punctata*), marsh crake (*Porzana pusilla)*, and white heron (*Egretta alba*) rely on New Zealand's remnant wetlands. Migratory species also depend on chains of suitable wetlands. Wetlands are also an essential habitat for native fish, with eight of 27 native fish species found in wetlands (McDowall, 1975). Among those are shortfin eel (*anguilla australis*) and inanga (*galaxias maculatus*), the major species in the whitebait catch, and species from the Galaxiid family like the giant kokopu (*galaxias argenteus*), which is usually found in swamps (Sorrell & Gerbeaux, 2004). Apart from provision of habitat for biodiversity, wetlands offer other valuable ecosystem services such as flood protection, nutrient retention for water quality, recreational services (Mitsch & Gosselink, 2000), and important cultural services for Māori, including food harvesting and weaving materials. The importance of wetland ecosystems is recognised internationally, and New Zealand is a signatory to the Ramsar Convention on Wetlands of International Significance. Six sites are currently designated as Wetlands of International Importance, with a total area of 55 thousand hectares.

In less than two centuries, New Zealand wetlands have been severely reduced in extent, particularly with the conversion to pastoral agriculture from the mid 19th century. The loss is attributed to human activities through fires, deforestation, draining wetlands, and ploughing (Sorrell et al., 2004; McGlone, 2009). Further degradation of the habitat has occurred since the introduction of livestock with consequent increases in nutrient flows, changing the fragile equilibrium in the wetlands and altering species composition (Sorrell & Gerbeaux, 2004). The loss of local fauna and flora has also been dramatic. Fifteen wetland birds species have become extinct (with 8 out of 15 being waterfowl species) (Williams, 2004), and ten species are on the list of threatened bird species (Miskelly et al., 2008). Among the plants, 52 wetland taxa species have been classified as threatened (de Lange et al., 2004). The decline in many native freshwater fish is also attributed to the loss and degradation of wetlands (Sorrell & Gerbeaux, 2004).

Ausseil et al. (2011) estimated that the pre-human extent of wetlands was about 2.4 million ha, that is, about 10% of the New Zealand mainland. The latest extent (mapped in 2003) was estimated at 250,000 ha or 10% of the original coverage.

Continued impacts and reduced indigenous biodiversity are expected over the next century. In grazed areas, plant community composition should continue to alter gradually depending on stocking rates and variability in climate and disturbance regimes. As for areas that are completely converted to new land cover types, changes should be much more immediate. These conversions are likely to have significant impacts on the ecosystem

Wetlands are defined as permanently or intermittently wet areas, shallow water and land water margins that support a natural ecosystem of plants and animals that are adapted to wet conditions. They support a wide range of plants and animals. In New Zealand, wetland plants include 47 species of rush and 72 species of native sedge (Johnson & Brooke, 1998). Many of these plants have very specific environmental needs, with a number of plants species adapted to wet and oxygen deprived conditions. Wetlands support a high proportion of native birds, with 30% of native birds compared with less than 7% worldwide (Te Ara – the Encyclopedia of New Zealand, 2009). For instance, the australasian bittern (*Botaurus poiciloptilus*), brown teal (A*nas chlorotis*), fernbird (*Bowdleria punctata*), marsh crake (*Porzana pusilla)*, and white heron (*Egretta alba*) rely on New Zealand's remnant wetlands. Migratory species also depend on chains of suitable wetlands. Wetlands are also an essential habitat for native fish, with eight of 27 native fish species found in wetlands (McDowall, 1975). Among those are shortfin eel (*anguilla australis*) and inanga (*galaxias maculatus*), the major species in the whitebait catch, and species from the Galaxiid family like the giant kokopu (*galaxias argenteus*), which is usually found in swamps (Sorrell & Gerbeaux, 2004). Apart from provision of habitat for biodiversity, wetlands offer other valuable ecosystem services such as flood protection, nutrient retention for water quality, recreational services (Mitsch & Gosselink, 2000), and important cultural services for Māori, including food harvesting and weaving materials. The importance of wetland ecosystems is recognised internationally, and New Zealand is a signatory to the Ramsar Convention on Wetlands of International Significance. Six sites are currently designated as Wetlands of International

In less than two centuries, New Zealand wetlands have been severely reduced in extent, particularly with the conversion to pastoral agriculture from the mid 19th century. The loss is attributed to human activities through fires, deforestation, draining wetlands, and ploughing (Sorrell et al., 2004; McGlone, 2009). Further degradation of the habitat has occurred since the introduction of livestock with consequent increases in nutrient flows, changing the fragile equilibrium in the wetlands and altering species composition (Sorrell & Gerbeaux, 2004). The loss of local fauna and flora has also been dramatic. Fifteen wetland birds species have become extinct (with 8 out of 15 being waterfowl species) (Williams, 2004), and ten species are on the list of threatened bird species (Miskelly et al., 2008). Among the plants, 52 wetland taxa species have been classified as threatened (de Lange et al., 2004). The decline in many native freshwater fish is also attributed to the loss and degradation of

Ausseil et al. (2011) estimated that the pre-human extent of wetlands was about 2.4 million ha, that is, about 10% of the New Zealand mainland. The latest extent (mapped in 2003) was

structure and provision of ecosystem services.

Importance, with a total area of 55 thousand hectares.

estimated at 250,000 ha or 10% of the original coverage.

wetlands (Sorrell & Gerbeaux, 2004).

**4. Freshwater wetlands**

Figure 4 compares the current extent of freshwater wetlands with its historic extent. The greatest losses occurred in the North Island where only 5% of historic wetlands remain compared with 16% in the South Island. The South and Stewart Islands contain 75% of all remaining wetland area, with the highest proportions persisting on the West Coast of the South Island and on Stewart Island. The remaining wetland sites are highly fragmented. Most sites (74%) are less than 10 ha in size, accounting for only 6% of national wetland area. Only 77 wetland sites are over 500 ha, accounting for over half of the national wetland area.

Fig. 4. Map comparison of current and historic extent of freshwater wetlands (blue areas) in New Zealand.

Classification of wetlands can be a challenge as they are dynamic environments, constantly responding to changes in water flow, nutrients, and substrate. Johnson & Gerbeaux (2004) clarified the definitions of wetland classes of New Zealand such as bog, pakihi, gumland, seepage, inland saline, marsh, swamps, and fens. By using GIS rules, it was possible to classify wetlands into their types and follow the trend of extent since historical times (Ausseil et al., 2011). Swamps and pakihi/gumland are the most common wetland types found in New Zealand. However, swamps have undergone the most extensive loss since European settlement, with only 6% of their original extent remaining (Figure 5). This is due to swamps sitting mainly in the lowland areas where conversion to productive land has been occurring. Unlike indigenous forest and indigenous grasslands, there is no national study describing recent loss over the last ten to twenty years for wetlands in New Zealand. However, some

Provision of Natural Habitat for Biodiversity: Quantifying Recent Trends in New Zealand 211

We used LENZ at level II (suitable for national to regional scale) and the most recent land cover (2008) to characterise historic and present habitats. The measure of habitat provision

*i i*

*ai* is the area of natural habitat remaining in land environment *i,*

*Pi* is the biodiversity value of the *i*th land environment when fully natural.

*bij* is the area of of the *j*th habitat site in the *i*th land environment, and

*n* is the number of habitats in the *i*th land environment.

0.5. All other landcovers have a condition of 0.0.

habitat measure (i.e. each pixel represents

**5.2 Freshwater wetlands**

*a H P*

0.5 *i*

0.5

(1)

(2)

*i*

*A*

The 0.5 power index is used to produce a function monotonically increasing from zero to one with a decreasing derivative in order to represent the higher biodiversity value of rare habitat. In the absence of comprehensive and detailed biodiversity information, Dymond et al. (2008) suggested using species-area relationships (Connor & McCoy, 2001) to estimate *Pi* as the land environment area to the power of 0.4. The varying condition, or degree of naturalness, of individual sites also needs to be taken into account in the habitat measure:

> 1 *n ij ij j i i*

The condition of indigenous forest, subalpine shrublands, alpine habitats, and tussock grasslands above the treeline, are assumed to have a condition of 1.0. Tussock grasslands below the treeline have a condition of 0.8 and indigenous shrublands have a condition of

Figure 6 shows the input layers (current land cover and land environments at level II) and the resulting habitat provision map. This map shows the contribution per hectare to the

> *ij ij n i ij ij j*

*c b*

Wetlands are at the interface between water and terrestrial dry environments. They have been categorised with freshwater environments in the past, and as such require a different definition of biogeographic units than the terrestrial environments. We replaced land environments data with biogeographic units defined by climatic and river basin characteristics (Leathwick et al., 2007). This framework was used to define priority conservation for rivers (Chadderton et al. 2004) and wetlands (Ausseil et al., 2011). A condition index for wetlands, similar to *ci* in equation (1), was calculated for all current wetland sites in New Zealand (Ausseil et al., 2011). This condition index reflects the major

*H*

).

*c b*

1

*A H P*

*i c b*

**5.1 Indigenous forest and grasslands**

for a land environment is defined as:

*Ai* the area of land environment *i*, and

where

where

*cij* is the condition,

Fig. 5. Current and historic extent of wetland per class.

regional analyses suggest that wetland extent continues to decline, although at a slower rate, as land drainage and agricultural development continue (Grove, 2010; Newsome & Heke, 2010). Wetland mapping is a challenging task as wetlands are sometimes too small in area to be identified using common satellite resolution. Their extent can vary seasonally (e.g., dryness, wetness) and therefore can change markedly at the time of imagery acquisition. While satellite images are useful for providing information at national scale, automatic classification is not possible as vegetation types in wetlands are so variable, making them difficult to characterise through spectral signature. Thus wetlands have been mapped on a manual or semi-automated basis (Ausseil et al., 2007), and this requires a significant amount of effort for all of New Zealand.


*5.1. EVCSs' Demand for IEEE 33 Bus*

The IEEE 33 bus distribution system was used to investigate the model under study. The power system load flow results, represented as node voltages, line currents, and losses, were obtained for the different demands of the EVCS connected to the system. The results were assessed by the performance indices shown in Table 6. For all cases, the integration of the EVCS into the network system was limited by the capacity of the incoming feeder of the main bus 1, as shown in Figure 5.

**Table 6.** Maximum EVCS penetration-demand obtained by load flow.

**Figure 5.** Current in the incomer buses of 33 bus systems.

The limitation factor is defined by the incomer capacity, which should not exceed 1 p.u. Another limiting parameter is the voltage profile, which was taken into consideration while obtaining the EVCSs' demand. The voltage profile at each bus was checked for all the scenarios, and also plotted in Figure 6.

**Figure 6.** Voltage profile for 33 bus distribution systems.

For the base case, the minimum voltage of 0.913 p.u. occurs at bus 18. This result was benchmarked and validated with the same bus voltage values published in [39,40]. The worst-case scenario was obtained for the maximum overloading of EVCSs, for which a voltage drop violation occurred.

For the IEEE 33 bus system with 33 integrated EVCSs, the voltage drop is approximately 1% compared only to the base case. In all cases, there is no voltage violation while integrating the maximum EV charger demand.

Figure 7 compares the line losses in the incoming lines (incomers) to the buses for all studied cases. It is observed that the incomers with most line losses are those for buses 2–6. It is also observed that the fewer the line losses, the greater the EVCSs' installed capacity.

**Figure 7.** Losses in the incoming lines of the buses in 33 bus systems.

The charging station's maximum penetration for this IEEE 33 bus was obtained for the cases without PV. According to the results in Table 6, the capacity of the charging station is related to the number of total EVCSs distributed in the network.

For the case of four EVCSs in the network, the max capacity is 119 kW per charger station. This results in a higher total charging capacity of 476 kW in the overall system and lower losses of approximately 238 kW. Installing 33 EVCSs limits each charger to 14 kW. This case shows higher losses

(244 kW) than the previous. The limitation factor in all cases is the incomer line capacity. For the IEEE 33-bus system, installing fewer EVCSs is more efficient than having higher numbers in the distribution network.