Drivers of survival in a small mammal of conservation concern: An assessment using extensive genetic non-invasive sampling in fragmented farmland
Introduction
Estimating large-scale demographic patterns (e.g. abundance, population growth, survival) of animal species in relation to both individual traits (e.g. sex, age, weight) and environmental factors (e.g. climate or land-use change) is a difficult but necessary goal to understand species ecology and sustain conservation policies (Smallwood and Schonewald, 1998; Williams et al., 2002). This is particularly true for species occurring in agricultural landscapes where major declines in biodiversity due to agricultural intensification have been reported worldwide (Tscharntke et al., 2012). However, achieving these goals is often difficult due to a number of technical, ethical, and logistic constraints in data collection, particularly for species that are rare, elusive, or otherwise hard to capture or observe.
Capture-Mark-Recapture (CMR) is one of the most popular methods to assess demographic parameters in animal populations (Lebreton et al., 1992), and hence to understand species' biology and ecology in different environments (Smallwood and Schonewald, 1998). Traditional CMR studies have been mostly based on live-trapping techniques, which are usually logistically difficult to implement over large spatial and temporal scales and often expensive (Cheng et al., 2017). In addition, because live-trapping implies both physical confinement and handling of animals, it often involves behavioural and physiological responses due to trapping-induced stress (Beja-Pereira et al., 2009; De Bondi et al., 2010). Stress responses can be reduced with the use of minimally invasive techniques such as camera-trapping, which is expected to be more time-efficient than live-trapping and does not require physical capturing and handling of animals (De Bondi et al., 2010; Mondol et al., 2009). However, camera trapping is unsuitable for CMR studies in species that are difficult to morphologically identify at the individual level, which is the case of most small mammal species (Glen et al., 2013). Furthermore, in the case for rare and elusive species, both live- and camera-trapping often yield insufficient data to be used in CMR models, thus hampering proper evaluation of their population status and trends (Burgar et al., 2018; Mondol et al., 2009).
Genetic non-invasive sampling (gNIS) has been increasingly used to estimate demographic parameters of species that are difficult to trap, mainly due to decreased field sampling effort, ever decreasing lab costs, and increasing DNA amplification success (Beja-Pereira et al., 2009; Marucco et al., 2011). Despite its limitations in retrieving information on relevant individual traits like age, body mass, or reproductive condition, gNIS can provide a more cost-effective solution than traditional live-trapping (Cheng et al., 2017; Ferreira et al., 2018). DNA extracted from non-invasive samples (e.g. feces, hairs, feathers) allows the identification of individuals, providing data that can be easily combined with CMR methods to obtain population parameters that otherwise would be difficult to obtain over large spatial scales (Cheng et al., 2017; Petit and Valiere, 2006). However, to date, applications of gNIS in CMR studies have mostly focused on large and medium-sized mammal species, and often provide snapshots of population size estimates rather than variations over time (but see Brøseth et al., 2010 for an example). Furthermore, very few studies have used gNIS to estimate other important population parameters such as survival (Lampa et al., 2015; Marucco et al., 2012; Zielinski et al., 2013). In the case of small mammals, while some recent studies have used gNIS to estimate population density (DeMay et al., 2017; Gillet, 2016; Sabino-Marques et al., 2018) or to infer dispersal (Ferreira et al., 2018; Gillet, 2016), to our knowledge no study has yet explored the application of this method to understand how demographic parameters relate to large-scale environmental variation.
In this study, we combined gNIS and CMR methods to assess the seasonal variations in abundance, and to evaluate factors affecting survival probability of an elusive small mammal species in a Mediterranean farmland landscape. We focused on the ‘near-threatened’, Iberian endemic Cabrera vole (Microtus cabrerae, Thomas 1906), for which genotyping protocols based on fecal samples have been recently optimized (Barbosa et al., 2013; Ferreira et al., 2018). Additionally, previous studies have also shown the ability of gNIS to provide reliable density estimates for this species (Sabino-Marques et al., 2018). Based on repeated surveys of Cabrera vole feces, we explored the potential of gNIS to (i) assess the seasonal variation in population abundance; and (ii) estimate capture and survival probabilities in relation to variables reflecting survey conditions (genotyping success and season), individual traits (sex), and local and landscape environmental features. We considered variables that might affect survival both positively (e.g. patch area and presence of water) and negatively (e.g. isolation, patch persistence, interactions with the competitor Arvicola sapidus, and human disturbances) (Pita et al., 2014) (see Table 1 for a full description and rationale of covariates considered). Overall, our study illustrates the use of gNIS within a CMR framework, demonstrating its application to retrieve demographic data from elusive small mammals, thus enhancing conservation planning in areas that have been highly modified by human activities.
Section snippets
Study area and species
The study was carried out in a 461.8 ha area within the coastal plateau of south-western Europe, Portugal (37° 21′–38° 04′ N, 08° 51′–08° 30′ W) (Fig. 1). The region is included in the thermo-Mediterranean bioclimatic zone (Rivas-Martínez, 1981), with a mean annual temperature of 16.5 °C (monthly temperatures ranging from 6 to 29 °C), and an annual rainfall of about 650 mm (of which >80% falls between October and March) (Pita et al., 2007, Pita et al., 2006). The landscape is mostly flat
Results
The amount of suitable habitat increased from the early wet season (EWS; 36 ha) to the late wet season (LWS; 46 ha), and declined both in the early dry season (EDS; 41 ha), and in the late dry season (LDS; 29 ha) (Fig. 1). The percentage of occupied patches was of 45% (n = 131) in EWS, 51% (n = 138) in LWS, 61% (n = 137) in EDS, and 54% (n = 149) in LDS (Fig. 1).
We collected a total of 2711 fecal samples (mean ± SE per season = 678 ± 54), of which 48.4% (n = 1312; 328 ± 24 per season) were
Discussion
We demonstrated for the first time the usefulness of large-scale genetic non-invasive sampling combined with capture-mark-recapture methods to estimate and identify the factors affecting small mammal demographic parameters and infer their population dynamics. Using the near-threatened Cabrera vole in Mediterranean farmland, we showed that our approach provides key information to improve conservation planning of elusive small mammals, especially those threatened by human activities and that are
Conclusions
Overall, our study provides empirical evidence that gNIS is a useful tool to monitor small mammal population parameters, and to identify management actions that may prove necessary to maintain their populations. Regarding the Cabrera vole, our results support the idea that conservation measures aimed to increase its survival in Mediterranean farmland, should promote low intensity agricultural management near occupied patches (encompassing longer fallow periods, low-disturbed margins, and high
Author contributions
Conception (PCA, PB, AM, RP); Design (XL, PCA, PB, AM, RP); Data collection (APF, IL, CF, JP, HSM, SB); Data analysis (APF, CF, RP); Writing (APF, RP); Revision (APF, CF, JP, HSM, SB, XL, PCA, PB, FM, AM, RP).
Funding
This study was supported by the Portuguese Foundation for Science and Technology (FCT) under projects NETPERSIST (PTDC/AAG-MAA/3227/2012) and MATEFRAG (PTDC/BIA-BIC/6582/2014). APF was supported by FCT grant SFRH/BD/109242/2015. JP was supported by the project ‘Genomics and Evolutionary Biology’ co-financed by North Portugal Regional Operational Programme 2007/2013 (ON.2 - O Novo Norte), under the National Strategic Reference Framework, through the ERDF and by the European Union's Horizon 2020
Acknowledgments
We are deeply grateful to Dinora Peralta, Vânia Salgueiro, Pedro Costa, and Bruno Martins for their invaluable assistance in field work. We also acknowledge the important contribution of Jeremy Searle during the study design and revision of the paper. Finally, we thank Robin Pakeman and two anonymous reviewers for their valuable suggestions, which greatly improved an initial version of the paper.
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