Agradecimentos
A realização desta tese de mestrado contou com o apoio e incentivo de várias pessoas, sem as quais não teria sido possível, e que portanto, merecem o devido reconhecimento e agradecimento.
Antes de mais, gostaria de agradecer ao Hugo Rebelo, pela sua dedicação e apoio como orientador, devo um enorme obrigada por se ter tornado disponível sempre que possível para me auxiliar na resolução de qualquer problema, e a tornar este trabalho no melhor possível. À minha coorientadora, Vanessa Mata, foi também uma peça indispensável, tendo-se mostrado sempre pronta para me ajudar com qualquer dúvida, técnica ou existêncial, ao logo deste ano. Quero agradecer também à Helena Santos, Francisco Amorim, Helena Raposeira, Pedro Horta, Raquel Godinho e Orly Razgour, pela ajuda e conhecimentos transmitidos ao longo deste trabalho, quer a nível teórico, quer a nível prático.
Também agradeço às instituições de acolhimento CIBIO/InBIO – Centro de Investigação em Biodiversidade e Recursos Genéticos da Universidade do Porto/Rede de investigação em Biodiversidade e Biologia Evolutiva e FCUP – Faculdade de Ciências da Universidade do Porto por providenciarem as condições para conduzir a investigação necessária para executar este Mestrado.
E por fim, agradeço também aos meus pais, amigos, namorado e todos aqueles mais próximos de mim, por toda a paciência e apoio fundamentais que me deram nos últimos tempos de realização desta tese de mestrado.
Sumário
A utilização da paisagem e as pressões que acarreta, causadas principalmente por atividades antropogénicas, foram já reconhecidas como sendo a maior causa da alteração da biodiversidade terrestre. As espécies adaptaram-se ao ambiente, contudo, algumas tem tido dificuldades em sobreviver, devido a grandes alterações nos ecossistemas, principalmente perda de habitat. Compreender o papel da conectividade do habitat e os movimentos das espécies deveria ser uma prioridade, apesar das dificuldades em ser quantificado.
De modo a compreender os efeitos de alterações climáticas e da perda de habitat, é importante identificar um bioindicador que demonstre respostas a estas alterações. No caso dos morcegos, estes demonstram uma forte dependência de habitats florestais e são sensíveis a uma vasta gama de perturbações que também afetam outros taxa. Este estudo foca-se no morcego Plecotus auritus begognae, uma linhagem recentemente descoberta na Península Ibérica, associada a ambientes florestais. A Península Ibérica é considerada uma importante reserva de biodiversidade, no entanto, tem sido alvo de alterações climáticas e de paisagem ao longo do século XX.
O principal objetivo deste trabalho reside na avaliação, através de análises da genética da paisagem, o efeito de alterações históricas na paisagem na estrutura da população de P. a. begognae. Mais de 300 amostras desta espécie foram recolhidas na Península Ibérica durante a última década. 23 microssatélites autossomais foram submetidos a análises genéticas e estatísticas, com o objetivo do cálculo de índices genéticos representativos da riqueza alélica dos indivíduos. Esta informação foi então correlacionada com dados espaciais, obtidos a partir de mapas históricos de habitat e do modelo de uso de solo, HILDA. Posteriormente, foram criadas uma série de friction layers, modelos de genética da paisagem e de distribuição de espécies, de modo a compreender a relação entre a espécie em estudo e o ambiente. Os resultados sugerem que, de um modo geral, a área florestal aumenta na Península Ibérica ao longo do século XX, sendo que a área florestal das primeiras décadas foram as que mais contribuíram para a estruturação da atual população de Plecotus. Florestas mais antigas providenciam um melhor habitat para esta espécie, devido à sua ecologia, e portanto esta é mais frequente em áreas onde árvores antigas são mais comuns.
Este estudo realça a necessidade da inclusão de dados históricos em estudos de estruturação populacional e sugere a preservação das florestas antigas actuais,
assim como a promoção da sua expansão, invés de investir na criação de áreas florestais novas, as quais se revelam menos adequadas a esta espécie de morcego.
Summary
Land use and related pressures are acknowledged to be the main drivers of terrestrial biodiversity change, usually resulting from anthropogenic activities. Species have adapted to their environments, although, there are many that have had difficulty in surviving in current times due to major changes in the ecosystems, especially loss of habitat. Comprehending the role of habitat connectivity providers and its part for species movement should be a priority, despite being difficult to quantify and measure connectivity.
In order to understand the effects of climate change and habitat loss it is important to identify bioindicator taxa that show measurable responses to these changes. Particularly, insectivorous bats, show a strong dependence of forested habitats and are sensitive to a wide range of disturbances that also affect many other taxa (Jones et al., 2009). My research focused on Plecotus auritus begognae, a recently discovered lineage in the Iberian Peninsula, associated to forested environments. The Iberian Peninsula has been considered an important reservoir of biodiversity. However, it has also been a target of climate and land-use change over the XXth century, leading to spatial changes in the landscape.
The main goal of my thesis is to evaluate through landscape genetics analyses the effect of historical land use changes on the population structure of P. a. begognae. Over 300 Plecotus auritus begognae samples were obtained from mist netting and roost trapping sessions in the Iberian Peninsula over the last decade. 23 autosomal microsatellite loci were analyzed and submitted to a number of genetic and statistical analyses, aiming towards the calculation of different genetic indices. This information was related to spatial environmental data, made available by historical maps and the HILDA (HIstoric Land Dynamics Assessment) model, and later facilitated the creation of friction layers, landscape genetic models (through Multiple Regression Distance Matrices) and species distribution models, in order to better understand the relation between the studied species and its environment. Results suggest that the overall forest area has increased in Iberia during the XXth century, while.the forest extent
during the earlier decades of the XXth century were strongly associated to the current
Plecotus population structure. Also, older forests seem to provide a better habitat for
this species, given its ecology, and therefore, it is more abundantly found in areas were older roosts can be found.
This study highlights the need to include historical data when studying the current structuring of species populations and provides de evidence that we should urge to preserve the existing ancient forests, as well as aid in their expansion, rather than promoting the development of new spurious forests that may not provide the necessary resources for the species survival.
Keywords: Plecotus auritus begognae, bats, land use change, population structure,
Table of contents
List of figures and tables………...………...10
1. Introduction……….………..………….11
1.1 Land use change and biodiversity...………..………..……….…….11
1.2 Landscape connectivity………..……….12
1.2.1 Challenges in studying population connectivity………..…………14
1.2.2 Landscape genetics………...16
1.3 Land use change in Europe during the XXth century……….….……17
1.4 Bats as indicators of environmental change...18
1.4.1 Relevance of forests for bats……….……19
1.4.2 Plecotus auritus begognae as a forest dependent species……….…….20
1.4.3 The Iberian Peninsula as a case study……….……...21
2. Objectives………..22
3. Methods……….……….23
3.1 Study area………..……...23
3.2 Fieldwork and sample collection………..……..24
3.3 Molecular analyses and genotyping………..…………25
3.4 Land use variables………..……….26
3.4.1 Calculation of friction layers………..……….28
3.5 Landscape genetics………..………...29
3.6 Relationship between individual allelic richness and forest age………..……….30
4. Results……….…………...32
4.1 Genetic structure of the populations………..…………32
4.2 Land use change in Iberia……….……….……….32
4.3 The structuring of populations due to historical land use……….…...33
4.4 Influence of forest age in the presence and allelic richness of individuals…..…34
5. Discussion……….……….36
5.1 Plecotus a. begognae’s dependence of forests……….……….….36
5.2 Plecotus a. begognae and the structuring of genetic diversity………...…..37
5.3 Caveats and limitations………...…………..…..38
5.4 Future studies………..….39
5.5 Final remarks………...….39
7. Annexes……….53
List of figures and tables
Figure 1. A Plecotus auritus begognae individual observed during the fieldwork done for this study……….………….….21 Figure 2 – The study area in which red symbols represent samples collected over that last decade………...………..24 Figure 3. One of the captured specimens, and an example of the small biopsy punch in the wing membrane, for tissue collection………...……25 Figure 4 – An example of a friction layers calculated using Circuitscape software…....29 Figure 5 – Graphic of the Iberian population structure………..…………..32 Figure 6 – The variation in the total area occupied by tree major types of land use in Iberia throughout the XXth century………...….………..33
Figure 7 – The genetic variability of the P. a. begognae species, in Iberia, according to HL genetic index……….………35
Table 1 – Set of variables used in the final species distribution models………..31 Table 2 – Table with the 10 best MRDM models, based on the highest R2 values……34
1. Introduction
1.1. Land use change and biodiversity
The rapid changes occurring at a landscape level have been responsible for altering various ecological systems around the globe. Land use and related pressures are acknowledged to be the main drivers of terrestrial biodiversity change. Biodiversity has already experienced widespread losses, potentially compromising ecosystem functions and services, such as biomass production and pollination that underpin human well-being (Newbold et al., 2016). Anthropogenic activities modify the quality, amount, and spatial configuration of habitats; the degradation of habitat quality or quantity can reduce the population size of species’, its growths rates and increase the chance of local extinction events (Pulliam, 1988). If habitat loss is significant, it can reduce genetic diversity, and the ability of species to evolve adaptations to new environments (Gilpin, 1987). Yet, humankind depends mainly on land use for food, fiber, and bioenergy, however the environmental trade-offs of land use, – greenhouse gas emissions, water use and pollution, biodiversity loss or soil erosion – are substantial from local to global scales (Foley et al., 2005; Tilman et al., 2011). Understanding where, how, and why land use is changing is thus important for mitigating environmental trade-offs, and for developing policies to transition to more sustainable forms of land management (Turner et al., 2007, Rounsevell et al., 2012).
Land use alters habitats’ spatial pattern, increasing the distances among patches. An important consequence of this fragmentation is a reduction in connectivity, which can constrain the ability of many species to move across the landscape in response to disturbances (Primack & Miao, 1992; Iverson et al., 1999). These changes in land cover over the globe have resulted in the conversion of native habitats, previously supporting a diverse species assemblage, to intensive land uses that support simplified and low-diversity communities (Rapport et al., 1985). To be noted that, species’ responses to climate change can be influenced by changes in available habitat, alongside vital population processes, species interactions and interactions between demographic and landscape dynamics (Keith et al., 2008), all of which are highly related to and affected by land use change. The species associated with a human-dominated landscape have greatly expanded in recent years, in contrast, several native community types, biomes and various species have been reduced greatly by human activity (Hansen et al., 2001). The impacts of land use change and,
consequently of eroding biodiversity, could include reductions in resilience of various local species, resistance to invasion, and ecological services provided to humans. However, assessing the spatial patterns of change intensity depends largely on adequate spatial data representing management intensity for large areas (Verburg et
al., 2011; Kuemmerle et al., 2013).
1.2. Landscape connectivity
Species have adapted to their environments and hence, the distribution of species' abundances in space should reflect the match between the environment and the species' ecological requirements, with an appropriate capacity for maintaining ecological interactions, such as competition and predation and resilience to recent perturbations caused by human activities. Despite this, many species have had difficulty in surviving in current times, due to major changes in the ecosystems, especially loss of habitat. The relative importance of habitat loss, patch size and population isolation are expected to differ with different degrees of habitat fragmentation, hence, depending on the degree of habitat fragmentation the proportion of suitable habitat decreases in the landscape. The reduction of suitable area and isolation effects strongly influence the population size of the species (Andrén, 1994), while habitat connectivity plays an important role in enabling dispersal and gene flow within and among populations (Saura et al., 2014).
The viability of many species depends on connectivity between their populations through dispersal across broad landscapes (Mateo-Sánchez et al., 2014). Climate and land-use changes will inevitably make large habitat areas inhospitable for many species, and require these species to move large distances following shifts in their suitable habitats. Long-term persistence of species will therefore rely upon their capacity to respond to these changes, which will frequently obligate passing intensively human-modified landscapes. Connectivity emerges from the dispersal across landscapes, or the movement of individuals and genes between resource patches. These linkages are influenced by population dynamics through a variety of mechanisms, such as demographic rescue, inbreeding avoidance, colonization of unoccupied habitat, and spread of diseases. In recent decades, landscape and metapopulation ecology studies have corroborated that a population’s extinction probability is affected by the levels of population connectivity mediated through dispersal, migration and gene flow (Hanski, 1991; O’Grady et al., 2006). Well
connected populations should generally have a lowered extinction risk, given that locally extinct habitats can be rescued through recolonization (Koen et al., 2012). The connectedness of populations is often referred to as landscape connectivity (Koen et
al., 2012). This concept has been defined as the degree to which a landscape
facilitates or impedes movement (Taylor et al., 1993). Being linked to species extinction risk, landscape connectivity is considered an important aspect of landscape structure and one that is invoked as a conservation target for planning. Consequently, the knowledge of these relations can enhance the understanding of species current and potential distribution patterns, population demography, genetic variability, evolutionary processes, and overall viability of species in heterogeneous landscapes, as well as provide insights into the dynamics of metacommunities (Vasudev et al., 2015).
The creation and protection of movement corridors is a commonly proposed strategy to promote population connectivity (Mateo-Sánchez et al., 2014). However, the traditional concept of a corridor as a narrow strip of appropriate habitat which facilitates movements of organisms between habitat patches is somewhat controversial. This results from an inappropriate characterization of connections and ecological flows between habitat patches, given the limited evidence of the effectiveness and issues related to scale and delineation. This concept assumes that it is more likely that organisms experience their surroundings as gradients of differential habitat quality (Mateo-Sánchez et al., 2014), instead of exploring the landscapes as categorical mosaics. Assessing present connectivity among populations is equally difficult, and assessments are often undertaken only after a significant land-use change has occurred (Epps et al., 2013). This limitation from environmental variability and uncertainty cannot be ignored in conservation studies.
Comprehending the role of the network of habitat patches to promote connectivity and species’ movement should, therefore, be a conservation priority, along with the development of effective conservation strategies that could help to mitigate the impacts of global change on biodiversity (Heller & Zavaleta, 2009). Despite the clear importance of habitat connectivity for population persistence, the most adequate ways to mitigate population isolation remains poorly known (Mateo-Sánchez et al., 2014), and specific factors mediating connectivity for many species are still unknown. For this reason, studies will need to evaluate the degree to which habitat patches scattered throughout the landscape may function as stepping stones facilitating dispersal among otherwise isolated habitat areas (Saura et al., 2014). The loss of intermediate and sufficiently large stepping-stone habitat patches may cause a sharp injcrease in the distance that can be traversed by a species. The capacity of species to exploit the
opportunities created by networks of stepping-stone patches largely depends on species-specific life-history traits. Therefore, connectivity models developed for conservation or management purposes should indicate areas (i) of sufficient size to be of conservation value, (ii) crucial for the spread of species or genotypes over long distances and (iii) areas that decrease the isolation of the largest habitat blocks in reserves, and contribute to species persistence across wide spatial and temporal scales (Saura et al., 2014).
The destruction and fragmentation of habitats due to anthropogenic land use changes have led to the decline of numerous species by reducing the size and the connectivity of their remaining local populations, thus contributing to their isolation (Tournant et al., 2013). Cianfrani et al. (2013) detected a steady decline on several carnivorous species of Western Europe since the beginning of the XXth century causing
the disappearance of several species of carnivores such as the wolf, the lynx, the bear and the otter from most of those Western European countries. This decline was generally attributed to a combination of human persecution, habitat destruction and the loss of their main prey species (Ceballos et al., 2005). Another example of the consequences of habitat reduction and fragmentation is its correlation with the decline of the lesser horseshoe bat Rhinolophus hipposideros in most of western and central Europe (Tournant et al., 2013). In this case, the populations located in small isolated habitat patches face a high risk of extinction (Fahrig, 2003). Such populations are particularly sensitive to habitat modifications, which may put an end to connectivity between several sub-populations (Forman & Alexander, 1998). Species habitat fragmentation has thus become a major issue in conservation biology leading many researchers to create new tools with which to assess, model, and predict the impact of human activities on species distribution and population dynamics (Tournant et al., 2013).
1.2.1. Challenges in studying population connectivity
Despite the widespread concern about impacts of the land-use change on connectivity among animal and plant populations, those impacts are difficult to quantify (Epps et al., 2013). Sometimes, results of empirical studies of habitat fragmentation are often difficult to interpret: (1) researchers often measure fragmentation at a patch scale, rather than a landscape scale; (2) most researchers are likely to measure fragmentation in ways that do not distinguish between habitat loss and habitat fragmentation per se (Fahrig, 2003). In order to perform appropriate studies to evaluate connectivity across various complex landscapes there is a wide range of techniques,
such as: including least-cost path modeling (Adriaensen et al., 2003), circuit theory (McRae & Beier, 2007), other forms of network analysis (Saura et al., 2014), resistant kernel modeling (Compton et al., 2007), agent-based movement (Palmer et al., 2011), gene flow simulations (Landguth et al., 2010), statistical modeling (Compton et al., 2007; Spear et al., 2010) or empirically derived understandings from detailed movement data. Robust models are pivotal for the prediction of disturbance impacts and responses of biodiversity and ecosystems.
To provide meaningful guidance to regional conservation efforts, it is essential to redirect the analysis from a local, patch-level definition of habitat connectivity, to a broader gradient perspective on landscape structure, in order to assess population performance across a complex landscape (Berger et al., 2006). One method of achieving this approach successfully is to evaluate landscape connectivity among all the individuals of a population across the entire occupied range and across multiple landscape resistance scenarios (Mateo-Sánchez et al., 2014). Resistance maps represent an integration of several behavioral and physiological factors such as aversion, energy expenditure or mortality risk when moving through a particular landscape (Mateo-Sánchez et al., 2014), and cost is the cumulative resistance incurred in moving from the source to the destination locations (Adriaensen et al., 2003). For this reason, integrating least-cost movement assessments in a spatially extensive range of analysis (i.e. across all locations occupied by a population), the strength of corridors and locations of movement barriers can be more rigorous. The production of quantitative predictive models of species distribution by spatial multivariate approaches has made this method recognized to be a significant component of conservation planning (Tournant et al., 2013). These models are widely applied in ecology and conservation biology including for the assessment of the impact of climate, land use, and other environmental changes on species distributions. However, there are still limitations with species distribution models (SDMs) (Guisan & Thuiller, 2005). Although they are based on empirical data such as field observations, they often ignore ecological principles such as population dynamics (Tournant et al., 2013) and including the potential quality of the habitat when modeling the distribution of a certain species. The approaches that use resistance or cost surfaces based on predicted influences of different habitats on animal movement are increasingly being used to quantify connectivity by predicting potential movement or gene flow over large landscapes (Epps et al., 2013). Least-cost path or circuit-theory are becoming popular methods to estimate distances between locations and predict likely movement paths or predict areas with higher use (McRae et al., 2008).
Population genetic data have been used to test and optimize resistance surfaces (Epps et al., 2013). However, genetic connectivity may be only indirectly related to demographic connectivity (Lowe & Allendorf, 2010), and interpretation of population genetic structure is confounded by time. This is given to the fact that genetic drift is weak for species with large effective population sizes and long generation times. Thus, although standard metrics of genetic distance among populations will be influenced by effects of recent landscape changes, the influence of historical landscapes may be much stronger (Epps et al., 2013). Yet, the lack of available historical landscape data has been hampering research on the influence of long-term land use changes on the current structure of wild populations.
1.2.2. Landscape genetics
Landscape genetics was only recently defined as an independent research field. Landscape genetics is a method which aims to understand the processes of gene flow and local adaptation by studying the interactions between genetic and spatial or environmental variation (Manel & Segelbacher, 2009) at both the population and individual levels. This technique is utilized in ecological/conservation studies by investigating gene flow among populations and aims to determine if genetic structure is mainly shaped by isolation/distance, or by current landscape patterns (Manel & Segelbacher, 2009). The recent improvements in molecular genetic tools, combined with statistical tools (e.g. geostatistics and Bayesian approaches) and powerful computers has led to the emergence of the field of landscape genetics, which is an amalgamation of molecular population genetics and landscape ecology (Manel et al., 2003). Landscape genetics helps researchers to integrate the effect of landscape connectivity into gene flow analysis and further allows the better understanding of local adaptation processes by helping the formation of new hypothesis on potential selection pressures (Manel et al., 2003).
Understanding the processes and patterns of gene flow within a population and their local adaptations requires a detailed knowledge of how landscape characteristics structure populations. This knowledge is paramount, not only for improving ecological knowledge, but also for managing the genetic diversity of threatened and endangered species populations (Manel et al., 2003). To quantify landscape structure, landscape genetics mainly uses landscape resistance surfaces and least-cost paths or straight-line transects (Van Strien et al., 2012). Cost or resistance surfaces are representations of a landscape’s permeability to animal movement or gene flow (Koen et al., 2012). It is a tool for measuring functional connectivity in landscape ecology and genetics studies,
however, parameterizing cost surfaces by assigning weights to different landscape elements has been challenging because true costs are rarely known.
By combining landscape ecological methods, spatial statistics and population genetic analyses, landscape genetics provides new possibilities to answer questions which are relevant for conservation management, such as the identification of dispersal barriers or corridors (Keller et al., 2015). Although, there is a lag between current demographic processes and population genetic structure due to the sharp acceleration in the global rate of anthropogenic landscape change after the mid-twentieth century. This acceleration often makes it challenging to interpret how contemporary landscapes and anthropogenic activity shape gene flow, given the influence of time to reach equilibrium, genetic structure may more strongly represent past rather than contemporary landscapes (Epps & Keyghobadi, 2015). As issues arise regarding this technique it is important to note that there are a few factors which play an important role in any landscape genetics study, in order to provide feasible results. Factors such as: (i) choosing a spatial extent of a landscape genetic study that corresponds to conservation management units and species-specific dispersal characteristics; (ii) the formation of landscape genetic models optimized to deliver better-supported results, and landscape parameters chosen considering their use in practical application; (iii) the identification of thresholds and the quantification of landscape effects on gene flow, e.g. in form of thresholds; (iv) the development of planning tools which should include such thresholds would enable the formulation of concrete management recommendations; (v) the consideration of multi-species studies and replication at the landscape scale to allow drawing general conclusions, which are of high priority in conservation management (Keller et al., 2015).
1.3. Land use change
in Europe during the XX
thcentury
Recent studies have emphasized the fact that current patterns of genetic differentiation among populations reflect processes that have acted over temporal scales ranging from contemporary to ancient (Bohonak & Vandergast, 2011). During the second half of the 20th century, Europe’s land use has predominantly changed along various intensification gradients (Rounsevell et al., 2012). Agricultural systems have experienced a substantial intensification, especially during the 1960s–1980s, and today has some of the most intensively managed croplands in the world (Kuemmerle et
agriculture, partly due to a declining farming profit as well as rural exodus (MacDonald
et al., 2000; Navarro & Pereira, 2012). This decline has triggered a widespread loss of
traditional agricultural landscapes and, together with active afforestation efforts, increased the forest area by a large percentage (25%) since the 1950s (Kuemmerle et
al., 2016). More recently, Europe expanded its conservation network substantially
(Jones-Walters & Čivić, 2013), resulting from an increasingly concern about the environmental costs of the intensification of habitat exploitation, resulting in a growing emphasis on multifunctionality of a landscape, for example through agri-environment and set-aside schemes (Kuemmerle et al., 2016). However, only a few studies have observed conversions among broad land-use classes at the pan-European scale, while most studies have explored European land-use change typically focused in small study regions, over short time periods, or at the level of coarse administrative units (Kuemmerle et al., 2016). This has proven to be insufficient in understanding the spatial patterns of changes in the extent and intensity of land use, and how these relate to each other, and therefore understanding land-use change trajectories. Although there are some clear patterns which arise when synthesizing across the individual land-use change processes (Kuemmerle et al., 2016): (1) a clear, but not ubiquitous East– West divide in terms of land-use change, (2) spatially diverging trends of stable or intensifying agriculture in areas highly suitable for agriculture, and disintensification and abandonment in more marginal areas, (3) a spatial separation of areas with increasing forest area and increasing management intensity, and (4) a marked geographic heterogeneity of land-use change, with pockets of co-occurring area decline and intensification, as well as co-occurring area increase and disintensification scattered across of Europe.
1.4. Bats as indicators of environmental change
The earth has been subject to climate change and habitat deterioration on various different scales (Jones et al., 2009). In order to understand the effects of climate change and habitat loss it is important to identify bioindicator taxa that show measurable responses to climate change and habitat loss and that reflect wider-scale impacts on the biota of interest, or else monitoring on its own would be insufficient (Jones et al., 2009). Bioindicators are key tools to mitigate human impact on biota and to achieve “sustainable development” declared as a major goal in the 1992 Rio Convention on Biodiversity (Russo & Jones, 2012), offering the potential for assessing
an ecosystem’s health state before it is functionally compromised, and allow the detection of biological responses on a community scale that can inform policy makers. Bats are among the most diverse of vertebrate groups, with more than 1300 species (Fenton & Simmons, 2014). These small mammals have enormous potential as bioindicators, proven by the fact that: they show taxonomic stability; trends in populations can be well monitored; short- and long-term effects on populations can be measured according to disturbances; and they are distributed widely around the globe which exposes them to a large variety of disturbances (Jones et al., 2009). A wide range of disturbances capable of affecting many other taxa also affects bat populations. Particularly, insectivorous bats occupy high trophic levels, which makes them sensitive to the accumulations of pesticides and other toxins, changes in their abundance may reflect changes in populations of arthropod prey species. Also, bats provide several ecosystem services, and some reflect the status of the plant populations on which they feed and pollinate, and the productivity of insect communities (Jones et al., 2009).
Bats are also especially sensitive to habitat fragmentation phenomenon and changes in land use throughout their ranges, and in many cases can be valuable indicators of environmental quality more generally (Russo & Jones, 2012). Changes in bat numbers or activity can be related to climate change, including extreme cases of drought, heat, cold and precipitation, cyclones and even sea level rise, but also the deterioration of water quality, agricultural intensification, loss and fragmentation of forests, fatalities at wind turbines, disease, pesticide use and overhunting (Jones et al., 2009). That being said there is a huge importance in the implementation of the monitoring of bat populations given their role as bioindicators.
1.4.1. Relevance of forests for bats
Several bat species show a strong dependence of forested habitats, either for foraging grounds or for roosts, mainly in hollow trunks or branches or woodpecker holes (Russo et al., 2016). The spatial complexity of a habitat, alongside insect availability, influence habitat use by foraging bats (Grindal & Brigham, 1999). In many cases, stand age significantly influenced bat foraging activity or insect availability (e.g., Cruz et al., 2016). Previous studies have suggested that the impact of forest harvesting on habitat use by foraging bats varies with spatial scale, although an edge habitat appears to be an important foraging habitat for some bats, whereas the effects of forest fragmentation is acknowledged to have a negative impact on the majority of the bat populations (Russo et al., 2016). In fact, mature woodlands provide a wider availability
of roosts and foraging opportunities, thus supporting larger populations and diversity of species (Crampton & Barclay, 1998; Ruczynski et al., 2010).
1.4.2. Plecotus auritus begognae as a forest dependent species
Regarding insectivorous bats, my research focused specifically on Plecotus
auritus begognae (Vespertilionidae) (Fig. 1), an Iberian lineage of the European
counterpart, Plecotus auritus auritus. This bat species occurs throughout Europe being associated to forested environments, especially along the Mediterranean region. Therefore, the loss and fragmentation of mature forests is acknowledged to be a major threat to this bat’s populations (Hutson et al., 2001).
During the summertime, Plecotus a. auritus forms stable maternity colonies within its home range, including both adult females and males and young of the year (Burland et al., 1999), where especially females are bound to return every night to feed their offspring (Burland et al., 2001). There are explicit differences in the pattern of activity during the nighttime, the way this bat exploits different habitats and where it forages in relation to the position of their roosting site. However, within this foraging behavior, a certain degree of variation is present and could be attributed to differences in habitat availability between roosting sites, differences linked to sex, as well as reproductive status, and nightly variation in climatic conditions (Entwistle et al., 1996). Gene flow between colonies of P. a. auritus seems to occur although some divergence has been found between close colonies even in the apparent absence of geographical barriers (Burland et al., 1999). The low dispersal ability of this bat, together with a marked female phylopatry, could explain this marked pattern (Burland et al., 2001). Genetic isolation by distance is possible and can occur naturally, although the occurrence of an unnatural barrier caused by anthropogenic activities (like extensive habitat loss) could lead to genetic isolation and therefore compromise the populations’ survival. The use of habitat in this particular species has strong implications for its management, and suggests that the protection of deciduous woodland, particularly in the vicinity of roost sites, and habitat connectivity should be key priorities (Entwistle et
al., 1996), that being said, this can also be considered an important bioindicator in its
1.4.3. The Iberian Peninsula as a case study
P. a. begognae is a recently discovered lineage in the Iberian Peninsula (Ibáñez et al., 2006). Until recently, this subspecies had a relatively unknown distribution,
ecological requirements and biogeographical affinity. The distribution of Plecotus a.
begognae seems to be restricted to the central and northern mountainous regions of
Iberia with an overlap in the Pyrenees with the European Plecotus a. auritus (Santos et
al., 2014). The Iberian Peninsula has been considered an important area functioning as
a reservoir of biodiversity. In fact, 10 major cryptic lineages of bats exist nowadays in Iberia within the five existing bat species complexes bearing genetic discontinuity. Iberia’s special geographic features have determined its particular function as a mosaic of suitable refugia for many different species (Gómez & Lunt, 2006). However, the Iberian Peninsula has been a target of climate and land-use change over the XXth
century, leading to spatial changes in the landscape. In the Middle Ages, a time where livestock became an important social and economical factor, local forests were transformed into communal wood pastures and arable lands (Pardo & Gil, 2005). Both human and livestock pressures decreased the forested area substantially and pine species disappeared, at the time, transforming the area into a savannah like landscape (Pardo & Gil, 2005). This obviously brought great consequences on local biodiversity and endangered various species from different taxonomic groups, however, reforestation alongside other management strategies have since then been implemented and a recovery of both diversity and density have been noticed.
2. Objectives
The main goal of my thesis is to evaluate the effect of historical land use changes on the population structure of P. a. begognae through landscape genetics analyses. This was only achievable due to the recently made available historical land cover data covering most of Europe, comprising the dates between 1900 to 2010 (Fuchs et al., 2014).
The Iberian Peninsula is an important reservoir of biodiversity (Ibáñez et al., 2006), having a variety of patterns of genetic structure described among different groups of taxa, supporting that Iberia is more of a mosaic of suitable refugia than as a homogenous unit (Gómez & Lunt, 2006). However, it has been a target for several kinds of disturbances, from climate to land use change, mostly caused by anthropogenic activity. As mentioned before, the brown long-eared bats are sensitive to habitat fragmentation and changes in land use throughout their ranges (Entwistle et al., 1996). It is expected that the population structure of this bat may be affected by land use changes faster than other bat species because of their low dispersal abilities (Veith
et al., 2004). Moreover, the population size and genetic diversity of this bat may be
limited by the existence of mature woodlands in a region. To test this hypothesis I will test for the effect of the presence of mature woodlands on the spatial patterns of this bat’s genetic richness.
Specifically, this study will aim to tackle to the following questions:
a) How are the populations of Plecotus auritus begognae structured in Iberia? b) How has land use changed during the XXth century in Iberia?
c) How have past land use changes, and in particular forest area change, affected current population structure of P. a. begognae in Iberia?
d) How has forest change and persistence over space affected the genetic richness of individuals?
3. Methods
3.1. Study area
The study area was the Iberian Peninsula, located in Europe’s south-western extremity. Covering nearly 600 000 km2, it is bordered to the south and east by the
Mediterranean Sea and to the north and west by the Atlantic Ocean, and is divided from the remaining Europe by the Pyrenees mountain range in the north-east. It has a highly heterogeneous topography and is characterized by two main biogeographical regions: Eurosiberian and Mediterranean (Sillero et al., 2009; Romo & García-Barros, 2010). The Iberian Peninsula has been considered an important area functioning as a reservoir of biodiversity, resulting in high levels of speciation and endemisms as attested by the existence of 10 major cryptic lineages of bats (Ibáñez et al., 2006). Iberia’s special geographic features have determined its function as a refugia for many different species (Gómez & Lunt, 2006). However, the Iberian Peninsula has been a target of climate and land-use change over the XXth century, leading to various spatial
changes in the landscape. This brought a great deal of consequences for local biodiversity and endangered various species from different taxonomic groups.
P. auritus species is known to forage in woodland habitats, specifically
deciduous woodland, in the vicinity of roost sites where insect prey, particularly moths, are frequent (Entwistle et al., 1997). Consequently, in order to study the gene flow in P.
auritus begognae, samples were collected since 2010 in various locations from North
to Central Iberia, covering the full range of the Iberian populations (Fig. 2). In order to guarantee that the study area covered the full range and to avoid the inclusion of unnecessary background data that could overfit the models (Merow et al., 2013), we restricted the study area using a 100km buffer south of southernmost locations.
Figure 2. The study area in which red symbols represent samples collected over that last decade. In this map of the Iberia Peninsula it is also presented a classification of the total forest area present in 2010 (blue – forest; green – no forest).
3.2. Fieldwork and sample collection
Over 300 Plecotus auritus begognae samples were obtained from mist netting and roost trapping sessions in the Iberian Peninsula over the last decade (Ibáñez et al., 2006; García-Mudarra et al., 2009; Salicini et al., 2011; Santos et al., 2014). From each specimen, a tissue sample was collected in the field, through a small biopsy punch in the wing membrane (Fig. 3 – (A) and (B)). Samples were then stored in ethanol for posterior laboratory analyses. Species identification of all records was validated by molecular analyses.
Figure 3. One of the captured specimens (A), and an example of the small biopsy punch in the wing membrane, for tissue collection (B).
3.3. Molecular analyses and genotyping
Genomic DNA was extracted from individuals with the QIAamp DNA Micro Kit (QIAGEN), using the whole wing membrane punch. Individuals were genotyped using three multiplex reactions for 23 autosomal microsatellite loci previously developed for
P. austriacus (n=20; Razgour et al., 2013) and P. auritus (n=3; Burland et al., 1998)
(Annex 1 outlines markers per multiplex panel, allele range, PCR reaction conditions and PCR cycle programs). The forward primers were M13-tailed to follow a specific fluorescent labelling protocol (Blacket et al., 2012). A negative control was always used to monitor possible contaminants. PCR products were separated by size in an ABI3130xl genetic analyzer. Alleles got scored against the GeneScan500 LIZ size standard using the GENEMAPPER 4.0 (Applied Biosystems) and manually checked by two observers. Result accuracy was measured through re-amplification of 15% random selected samples for each locus (Bonin et al., 2004), which resulted in a complete concordance among replicates. Four loci exhibiting low amplification rates (Pau10, Pau11, Pau13 and Pau16) were removed from analysis. From this point 19 microsatellites loci were to be analyzed, but after the removal of monomorphic loci and loci with excessive missing data, only 17 were used for the final analyses. Thus, we finalized with the dataset of 316 samples of the P. a. begognae lineage. The molecular dataset was then submitted to a number of genetic and statistical analyses, aiming
towards the calculation of different genetic indices, including genetic distance between samples, within the Iberian Peninsula.
Merging genetic and geospatial data, is helping to bring explicit hypothesis testing to various fields. Hypotheses derived from a certain approach can then be reciprocally tested with data derived from another field and the synthesis of these data can help place demographic events in an historical and spatial context, guide genetic sampling, and point to areas for further investigation (Chan et al., 2011). After sample processing in the laboratory, genetic population structure was determined using individual-based Bayesian assignment tests, implemented in the programe STRUCTURE v2.3.4 (Pritchard et al., 2000), in order to corroborate whether the Iberian population of P. a. begognae can be considered a unique population or if there is some level of genetic divergence within it. All genotyped samples were used in this analysis, allowing K=1 to K=10 populations to be tested. Individual membership proportions (qi) were assessed using the admixture model in 10 independent runs each with 106
MCMC iterations followed by a burn-in period of 105 iterations and no prior population
information.
Finally, genotypes were analysed to obtain various different indices of genetic diversity, namely allelic richness and genetic distance (Hardy & Vekemans, 2002). For this task, a software Excel extension, GenAlEx (Peakall & Smouse, 2012), was used to obtain a matrix of genetic distance between P. a. begognae samples and allelic richness (HL – the contribution of each locus to the homozygosity index, depending on their allelic variability) was calculated using Cernicalin V.1 (Aparicio et al., 2006), was later related to spatial environmental data. This is essential to understand how the genetic richness of individuals may be related or affected by the age of forest stands.
3.4. Land use variables
Historical land use variables in the Iberian Peninsula were calculated using historical data from 1900 until 2010. This data was utilized to determine for differences in the location between present habitats and historical habitats (Fuchs et al., 2014). The sources for this information were historical maps and the HILDA (HIstoric Land Dynamics Assessment) model (Fuchs et al., 2015). Reconstruction approaches depend on available land cover/use databases containing country level statistics, population statistics and model assumptions, resulting from a lack of available historic
data sets for long time periods. However, strong assumptions are made to fill data gaps and identify sub-national patterns of land use (Fuchs et al., 2014). Such reconstructions have been widely applied in international biogeochemical and environmental assessments, for example, the 5th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC, 2013) and have provided new insights on land use dynamics throughout the history of man-kind (Fuchs et al., 2014). Global reconstructions of historic land use provide valuable estimates of land use for a certain historic period, however, they do not provide detailed insights into the dynamic changes in land use that may have taken place over time. Most reconstructions are based on the difference in land use areas between two time steps (net changes). But for larger areas these net change estimates deviate from the sum of all area gains and losses of the different land use types (gross changes). Accounting only for net changes can lead to serious underestimation of the land use changes, which may have implications for biogeochemical, ecological and environmental assessments (Fuchs et
al., 2014). Previous studies on the historic land cover/use reconstruction for Europe
revealed that, extrapolated to long periods, the difference in estimated land changes between gross and net changes has serious consequences on the quantity of overall change in land use. The consideration of gross changes lead to almost double the amount of change and increase the dynamics of change per grid cell. This results in higher amounts of land changes for all land categories compared to existing global reconstructions that adopt a net change approach (Fuchs et al., 2014). For this reason, this study has considered solemnly gross changes. The processing of this information was done using ArcGIS 10.1 software (ESRI, 2012). Land cover data was available from 1900 till 2010 with a raster per decade, thus totalizing 12 decades in study. This dataset has landcover classified into six broad classes: forests, urban, grassland, shrubland, cropland and water bodies. From these classes we processed a number of landscape variables, such as habitat age (resulting from the sum of the rasters to a maximum of 12 decades), forest persistence (that identifies areas with forests occurring for at least 12 decades), habitat area per decade and euclidian distance to each habitat per decade. All variables had a resolution of 1 × 1 km and were also standardized for posterior spatial analyses through the formula [("raster" - "raster".minimum) / ("raster".maximum - "raster".minimum) x 100], in ArcGIS 10.1 (ESRI, 2012).
3.4.1. Calculation of friction layers
Friction layers that represent the potential patterns of gene flow across the environmental space were calculated using Circuitscape (McRae et al., 2016). This software analyses species’ movement and gene flow across fragmented landscapes (Fig. 4). It uses an algorithm based on the circuit theory to incorporate all possible pathways across a landscape, thus providing the most probable path by which species, as well as gene flow, move across the landscape. A cost or resistance surface is a representation of a landscape’s permeability to animal movement or gene flow and is a tool for measuring functional connectivity in landscape ecology and genetics studies. They are simply planar surfaces where cost weights are applied to different landscape elements reflecting their cost to movement or gene flow for the species in question (Koen et al., 2012). However, the parametrization of the surface by applying cost weights to different landscape elements to estimate least cost paths or circuit-based resistance is challenging, because the true costs of movement are rarely known (Koen
et al., 2012). Circuit theory takes advantage of similarities between current travelling
through an electrical circuit and animal or gene movements across the landscape (Doyle & Snell, 1984). Voltage can vary depending on the cost weights of each landscape element, so multiple paths of greater or lesser resistance can be identified. Resistance also varies with the number of paths; as a higher number of routes for current is added, more the resistance declines (McRae & Shah, 2009). In this work, resistance as well as conductance measures were utilized. All the input variables were scaled from 1 to 101 and then assigned as either resistance or conductance surfaces. When calculating current maps rasters were analyzed using a pairwise modeling mode, connecting raster cells to four neighbors. After this process, resistance matrices of the samples were built for each variable resulting in univariate friction layers.
Figure 4. An example of a friction layers calculated using Circuitscape software. The lighter colors represent the voltage passing through the possible pathways species/gene flow in more likely to cross the landscape. The green pathways indicate those which are the most favorable paths and as the favorability diminishes, green turns from a lighter blue to darker.
3.5. Landscape genetics
Landscape genetics approaches are employed to understand how landscape patterns (distribution of suitable habitat, barriers, climate, etc.) affect gene flow and genetic differentiation among Plecotus populations. In order to determine which landscape elements, habitats or decades shaped current population structure of P. a.
begognae in Iberia, multiple regressions on distance matrices (MRDMs) were
calculated. For this the resulting landscape resistance matrices from Circuitscape, and the genetic and geographic distance matrices previously calculated, were used. All these calculations were performed in R version 3.3.2 (R Core Team, 2013), where distance matrices were imported into R and standardized from 0 to 1, using the “scales” package. Geographic variation was removed from all of the variables so that the final models would only reflect the role that landscape elements, habitats or decades play in structuring the patterns of gene flow, without considering the effect of
geographic distance, which is known to affect populations’ genetic structure (Razgour
et al., 2014). This step was accomplished by calculating the residuals of linear models of geographic distance against each of the variables. Finally, using the “ecodist” package (Goslee & Urban, 2007), univariate MRDMs of genetic distance were calculated for each variable, with 10,000 permutations between population pairs, to identify which of these variables best correlated with genetic distance (in Annex 2 a table with each calculated MRDM is presented), and therefore, better explain the
current genetic structure of the species.
3.6. Relationship between individual allelic richness and forest
age
To understand if forest age could have a significant influence on the spatial structure of genetic diversity, I followed several analytical steps that end with the plotting of individual allelic richness (Hl) with the forest age. I started by using species distribution models to delimit this analyses only to the habitats (with a focus on forests) occurring in areas where the bats have the potential to occur.
Species distribution models (SDMs) were built using a maximum entropy modelling technique, with Maxent version 3.3.3k (Phillips et al., 2006). It is highly reliable when using presence-only data, and even with limited datasets has been proven to outperform other modelling methods (Hernandez et al., 2006; Wisz et al., 2008; Elith et al., 2010; Rebelo & Jones, 2010). By using presence-only data in this study, we aim to avoid issues of ‘false absences’, which occur in situations when a species was not detected, due to its conspicuous behavior (Ahlén & BaagØe, 1999), although it was present (Elith et al., 2010). Maxent software was used, in each species presence records, were considered dependent variables, and selected EGVs, the independent variables (see below for variable selection procedure). The program ran in auto features with a regularization multiplier of 2. A regularization multiplier was selected after model selection tests were calculated through ENM Tools 1.3 (Warren et
al., 2010) (http://enmtools.blogspot.com) and Akaike information criterion was used to
facilitate their choosing, after being corrected for smaller sample sizes (AICc) value. Afterwards, the entire presence data set was randomly split into equal-sized partitions, by running 10 model replicates with cross-validation. The area under the curve (AUC) of the receiver operating characteristics (ROCs) plot was featured as a measure of the overall fit of the models (Fielding & Bell, 1997). The AUC values range from 0 (complete randomness), to 1 (perfect discrimination) (Phillips et al., 2006). The selected variables were all climatic because the goal was to delimit which areas have suitable climate for the bat (Santos et al., 2014). A set of 9 variables (Table 1) was selected based on its relevance to the species, and was then used to build the final models. Finally, the SDMs built were imported into ArcGIS 10.0 and reclassified into presence–absence using the maximum training sensitivity plus a cumulative threshold
Variable Code
Annual Mean Temperature (ºC) BIO1
Mean Diurnal Range (Mean of monthly (max temp - min temp)) (ºC) BIO2 Isothermality (BIO2/Temperature Annual Range) (* 100) (ºC) BIO3 Temperature Seasonality (standard deviation *100) (ºC) BIO4
Mean Temperature of Warmest Quarter (ºC) BIO10
Mean Temperature of Coldest Quarter (ºC) BIO11
Precipitation Seasonality (Coefficient of Variation) (mm) BIO15
Precipitation of Wettest Quarter (mm) BIO16
Precipitation of Driest Quarter (mm) BIO17
value (Liu et al., 2013). Subsequent spatial analyses were then restricted to the climatically suitable areas of bat occurrence and this way avoiding the confounding effect of including unsuitable areas for the bats in the analyses. I then calculated the histogram of forest age restricted to the forests that have suitable climate for the bat.
4. Results
4.1. Genetic structure of the populations
Results obtained in STRUCTURE revealed that this species’ population in Iberia is, in fact, one unique population, given its lack of genetic divergence between samples across the study area (K = 1) (Fig. 5).
4.2. Land use change in Iberia
Throughout the XXth century land use experienced various degrees of change.
Most major changes include a rapid and continuous increase in the total km’s occupied by urban areas, with a decrease in intensity since the 1980’s. A similar scenario can be observed regarding forests, where there is a considerably constant increase in forest area up until the 1990’s, when values seem to remain stable. Lastly, grasslands experienced a large decrease in total area, since the early 1900’s, reaching its lowest around 1990, the same time when forests reached their largest area. After this point in time, grassland area increased rapidly in the last two decades, having already surpassed the total area occupied by forests in Iberia (Fig. 6).
Figure 5. Graphic representation harvested from STRUCTURE, where the highest mean indicates the presence of a unique P.a. begognae population in Iberia (K=1).
Figure 6. The variation in the total area occupied by tree major types of land use in Iberia throughout the XXth century
(x). The left y axis’ scale is adjusted to Forest (green) and Grassland (blue) values and the right y axis was adjusted to better represent the Urban (red) values. Areas are presented in total km2.
4.3. The structuring of populations due to historical land use
Population structure resulting from land use was inferred by choosing the significant MRDM models with the highest R2 (Table 2.). Results from the best 10
MRDM models revealed that the P. a. begognae population structure was mainly influenced by land use changes occurred in forests in the first decades of the XXth
century (1900-1930) and grasslands during the first half of the century (1900-1950). The sum of the total changes occurred in forest and grasslands throughout the XXth
century also revealed to be significant in the structuring of the current Plecotus population. Also of note, all significant forest models resulted from conductance matrices whereas grassland models were originated with resistance matrices, suggesting a dependence of the forests and an avoidance of grasslands.
Variable Estimate p-value R² F.test sgras 5.94E-17 0.002 0.0055712 278.82230 dgras1940 5.83E-17 0.0022 0.0047551 237.78164 dgras1930 5.49E-17 0.0061 0.0043726 218.57225 dgras1900 6.01E-17 0.0019 0.0042897 214.40793 dgras1920 5.02E-17 0.0109 0.0041116 205.47300 dgras1950 6.15E-17 0.0014 0.0039680 198.26806 dfor1900 5.55E-17 0.0047 0.0039070 195.20409 sfor 5.72E-17 0.0028 0.0038744 193.57015 dfor1920 5.92E-17 0.0026 0.0035726 178.44004 dfor1930 5.81E-17 0.0027 0.0031967 159.60290
Table 2. Table with the 10 best MRDM models, based on the highest R2 values. Both sgras and sfor stand for the sum
of all the layers of data regarding decades from 1900 to 2010, for grasslands and forests, respectively. The remaining variables represent the influence of distance, to grasslands (dgras-) and forests (dfor-), in the gene flow of P. a. begognae in the study area. The most significant decades in the shaping of the present population structure are indicated along with the variables.
4.4. Influence of forest age in the presence and allelic richness
of individuals
The presence and genetic diversity of P. a. bebognae individuals has proven to be mostly associated to the presence and age of forest areas. As shown in Figure 7, the majority of bat presences were found at forests at least 5 decades old. As for recent forests (1 to 2,5 decades old), bat presences were close to none, even though there was a high number of km2 available for exploitation. When analyzing availability
of different forest age classes, within the suitable area for the bat, it is possible to see that this species shows a clear selection for older forests despite large areas of younger stands are available in climatically suitable areas (Fig. 7). As for genetic diversity, the variation of the HL allelic index throughout the Iberian Peninsula showed no relationship with forest age (Annex 3).
Figure 7. The genetic variability of the P. a. begognae species, in Iberia, according to HL genetic index (blue markers), distributed throughout forests of different ages. The orange line represents the percentage of forest area occupied by bats, in relation to forest age.
5. Discussion
This work has revealed that the historical distribution of forests seems to shape the current genetic structure of the Iberian population of Plecotus auritus begognae, despite the massive land use changes that have occurred in Iberia throughout the XXth
century. Previous studies have proven that several species of bats have a preference for older forests, even when given the opportunity to occupy recently grown habitats (Hallett & Connell, 2016; Crampton & Barclay, 1998; Hulmes et al., 2016; Thomas, 2013; Crampton & Barclay, 1995). This fact alone suggests that the genetic structuring of populations by the extent of older forests is not an uncommon trait in bat ecology. Also, contrarily to expectations, the observed expansion in forest areas, during the XXth
century, did not have a major effect on bats genetic diversity, being that their presence remained restricted to areas in which old forests prevailed, or in close proximity. Even when individuals where captured in younger forests, that only happened when older stands were within P. auritus home range (Entwistle et al., 1996).
5.1. Plecotus a. begognae
’s dependence of forests
As previously described, P. a. begognae use woodland areas and trees as their preferred habitat (Entwistle et al., 1996). There is a clear preference demonstrated by this species’ individuals for deciduous woodlands, mainly given to the amount of prey available in this type of forests. Also, this habitat provides the optimal amount of cover from areal predation (Entwistle et al., 1996), making Plecotus auritus begognae highly selective of the sites in which it roosts (Entwistle et al., 2000). It has been previously described that, even though females are known to be more philopatric than males, both males and females, have shown long-term associations with a particular roosting site, by using it exclusively, with very few movements documented to neighbouring roosts. For this reason, it can be inferred that bats do not use roosts indiscriminately, but exhibit strong philopatry to particular sites over a long period of time (Entwistle et al., 2000). Coupled with the long longevity of this species, roosts can be considered a traditional site that may be used by a colony over several generations. High roost fidelity is likely to have a great impact in the genetic structure of the populations, even more so when associated with a limited dispersal in summer (Strelkov, 1969), and such low dispersal rates tend to restrict the mixing of individuals from different colonies. This
