Simplified and still meaningful: assessing butterfly habitat quality in grasslands with data collected by pupils

Public participation in scientific research, now commonly referred to as citizen science, is increasingly promoted as a possibility to overcome the large-scale data limitations related to biodiversity and conservation research. Furthermore, public data-collection projects can stimulate public engagement and provide transformative learning situations. However, biodiversity monitoring depends on sound data collection and warranted data quality. Therefore, we investigated if and how trained and supervised pupils are able to systematically collect data about the occurrence of diurnal butterflies, and how this data could contribute to a permanent butterfly monitoring system. We developed a specific assessment scheme suitable for laypeople and applied it at 35 sampling sites in Tyrol, Austria. Data quality and its explanatory power to predict butterfly habitat quality was investigated comparing data collected by pupils with independent assessments of professional butterfly experts. Despite substantial identification uncertainties for some species or species groups, the data collected by pupils was successfully used to predict the general habitat quality for butterflies using a linear regression model (r²?=?0.73, p?<0.001). Applying the proposed method in a citizen science context with laypeople could support both the long term monitoring of butterfly habitat quality, as well as the efficient selection of sites for professional in-depth assessments.     

Cost-effectiveness of habitat-suitability maps using low-detailed data for elusive bat species

European bat species are strictly protected by law, and the Member States of the European Union are obliged to record species condition and to contribute to their conservation. Habitat-suitability models are an essential aid in assessing the conservation status and distribution of a species. However, model performance depends on the data quality. This study compares habitat-suitability models that were generated from two data sets that differ in the degree of details included. The first model used data that were low in detail but freely available and the second used data that were very detailed but costly. Three hypotheses were addressed: (1) that the model using low-detailed data is sufficient in its performance to aid the assessment of species distribution and infrastructural planning; (2) the visualisation of actual species distribution is more accurate in the high-detailed model; and (3) habitat-suitability maps can depict species distribution better than species occurrence data alone. To develop models, climate, geographic and roosting data of Myotis bechsteinii were used. Models allowed very good spatial predictions of suitable habitats. However, the model using low-detailed data overestimated suitable habitat. The high-detailed model was more able to predict actual species distribution. These findings were supported by field evaluation where M. bechsteinii could only be detected in areas where both models predicted high habitat suitability. This framework is promising as it resulted in spatially explicit habitat-suitability maps and suggests that similar models may be used to improve the understanding of bat distribution and factors endangering other species of bats.     

Predicting habitat distribution and frequency from plant species co-occurrence data

Aim Species frequency data have been widely used in nature conservation to aid management decisions. To determine species frequencies, information on habitat occurrence is important: a species with a low frequency is not necessarily rare if it occupies all suitable habitats. Often, information on habitat distribution is available for small geographic areas only. We aim to predict grid‐based habitat occurrence from grid‐based plant species distribution data in a meso‐scale analysis. Location The study was carried out over two spatial extents: Germany and Bavaria. Methods Two simple models were set up to examine the number of characteristic plant species needed per grid cell to predict the occurrence of four selected habitats (species data from FlorKart, http://www.floraweb.de ). Both models were calibrated in Bavaria using available information on habitat distribution, validated for other federal states, and applied to Germany. First, a spatially explicit regression model (generalized linear model (GLM) with assumed binomial error distribution of response variable) was obtained. Second, a spatially independent optimization model was derived that estimated species numbers without using spatial information on habitat distribution. Finally, an additional uncalibrated model was derived that calculated the frequencies of 24 habitats. It was validated using NATURA2000 habitat maps. Results Using the Bavarian models it was possible to predict habitat distribution and frequency from the co‐occurrence of habitat‐specific species per grid cell. As the model validations for other German federal states were successful, the models were applied to all of Germany, and habitat distribution and frequencies could be retrieved for the national scale on the basis of habitat‐specific species co‐occurrences per grid cell. Using the third, uncalibrated model, which includes species distribution data only, it was possible to predict the frequencies of 24 habitats based on the co‐occurrence of 24% of formation‐specific species per grid cell. Predicted habitat frequencies deduced from this third model were strongly related to frequencies of NATURA2000 habitat maps. Main conclusions It was concluded that it is possible to deduce habitat distributions and frequencies from the co‐occurrence of habitat‐specific species. For areas partly covered by habitat mappings, calibrated models can be developed and extrapolated to larger areas. If information on habitat distribution is completely lacking, uncalibrated models can still be applied, providing coarse information on habitat frequencies. Predicted habitat distributions and frequencies can be used as a tool in nature conservation, for example as correction factors for species frequencies, as long as the species of interest is not included in the model set‐up.     

GPS项圈系统在野生动物管理与监测中的应用

野生动物管理与监测是生物多样性保护工作的重要内容。而监测手段决定了保护工作中收集数据的质量和数量。收集动物活动位点数据是野生动物管理与监测中的一项重要工作,可通过样线法收集动物活动痕迹(实体)位点或采用无线电遥测获取动物的活动位点等,采用这些位点数据可以研究野生动物种群数量、家域、运动、栖息地利用和选择等。通过给野生动物佩戴GPS项圈,利用GPS项圈系统进行定位记录其活动位点,较前述常规方法更为便利,能高效地收集大量高质量位点,这种方法可以为野生动物的管理与生物多样性保护提供更为可靠的数据基础。     

Mapping from heterogeneous biodiversity monitoring data sources

Field monitoring can vary from simple volunteer opportunistic observations to professional standardised monitoring surveys, leading to a trade-off between data quality and data collection costs. Such variability in data quality may result in biased predictions obtained from species distribution models (SDMs). We aimed to identify the limitations of different monitoring data sources for developing species distribution maps and to evaluate their potential for spatial data integration in a conservation context. Using Maxent, SDMs were generated from three different bird data sources in Catalonia, which differ in the degree of standardisation and available sample size. In addition, an alternative approach for modelling species distributions was applied, which combined the three data sources at a large spatial scale, but then downscaling to the required resolution. Finally, SDM predictions were used to identify species richness and high quality areas (hotspots) from different treatments. Models were evaluated by using high quality Atlas information. We show that both sample size and survey methodology used to collect the data are important in delivering robust information on species distributions. Models based on standardized monitoring provided higher accuracy with a lower sample size, especially when modelling common species. Accuracy of models from opportunistic observations substantially increased when modelling uncommon species, giving similar accuracy to a more standardized survey. Although downscaling data through a SDM approach appears to be a useful tool in cases of data shortage or low data quality and heterogeneity, it will tend to overestimate species distributions. In order to identify distributions of species, data with different quality may be appropriate. However, to identify biodiversity hotspots high quality information is needed.     

Assessing species’ habitat associations from occurrence records,standardised monitoring data and expert opinion: A test with British butterflies

Accurate knowledge of species’ habitat associations is important for conservation planning and policy. Assessing habitat associations is a vital precursor to selecting appropriate indicator species for prioritising sites for conservation or assessing trends in habitat quality. However, much existing knowledge is based on qualitative expert opinion or local scale studies, and may not remain accurate across different spatial scales or geographic locations. Data from biological recording schemes have the potential to provide objective measures of habitat association, with the ability to account for spatial variation. We used data on 50 British butterfly species as a test case to investigate the correspondence of data-derived measures of habitat association with expert opinion, from two different butterfly recording schemes. One scheme collected large quantities of occurrence data (c. 3 million records) and the other, lower quantities of standardised monitoring data (c. 1400 sites). We used general linear mixed effects models to derive scores of association with broad-leaf woodland for both datasets and compared them with scores canvassed from experts.Scores derived from occurrence and abundance data both showed strongly positive correlations with expert opinion. However, only for occurrence data did these fell within the range of correlations between experts. Data-derived scores showed regional spatial variation in the strength of butterfly associations with broad-leaf woodland, with a significant latitudinal trend in 26% of species. Sub-sampling of the data suggested a mean sample size of 5000 occurrence records per species to gain an accurate estimation of habitat association, although habitat specialists are likely to be readily detected using several hundred records. Occurrence data from recording schemes can thus provide easily obtained, objective, quantitative measures of habitat association.     

Ecological traps and species distribution models: a challenge for prioritizing areas of conservation importance

Species distribution models analyse how species use different types of habitats. Their spatial predictions are often used to prioritize areas for conservation. Individuals may, however, prefer settling in habitat types of low quality compared to other available habitats. This ecological trap phenomenon is usually studied in a small number of habitat patches and consequences at the landscape level are largely unknown. It is therefore often unclear whether the spatial pattern of habitat use is aligned with the behavioural decisions made by the individuals during habitat selection or reflects actual variation in the quality of different habitat types. As species distribution models analyse the pattern of occurrence in different habitats, there is a conservation interest in examining what their predictions mean in terms of habitat quality when ecological traps are operating. Previous work in Belgium showed that red-backed shrikes Lanius collurio are more attracted to newly available clear-cut habitat in plantation forests than to the traditionally used farmland habitat. We developed models with shrike distribution data and compared their predictions with spatial variation in shrike reproductive performance used as a proxy for habitat quality. Models accurately predicted shrike distribution and identified the preferred clear-cut patches as the most frequently used habitat, but reproductive performance was lower in clear-cut areas than in farmland. With human-induced rapid environmental changes, organisms may indeed be attracted to low-quality habitats and occupy them at high densities. Consequently, the predictions of statistical models based on occurrence records may not align with variation in significant population parameters for the maintenance of the species. When species expand their range to novel habitats, such models are useful to document the spatial distribution of the organisms, but data on population growth rates are worth collecting before using model predictions to guide the spatial prioritization of conservation actions.     

Evaluating the costs and benefits of systematic data acquisition for conservation assessments

Effective decision‐making in conservation often is constrained by data quality. Uncertainties associated with poor quality or sparse data can lead to the misuse of limited resources and potentially the failure of conservation practice. Data acquisition, which can help improve decision‐making, is constrained by limited budgets and time. This is especially concerning for rare species, the most in need of conservation, but the most difficult to accurately represent in conservation plans. Here we test the suitability of three different sampling design strategies (two systematic vs random) designed to improve the quality of information available for conservation planning involving rare species. We modelled the spatial distribution of freshwater fish species in a data rich area in northern Australia using a large dataset (representing the best attainable data or true distribution) and simulate increasing subsets of data acquired through the three alternative sampling designs. We then evaluated omission and commission errors in conservation planning outcomes, efficiency and return on investment of data acquisition for conservation planning outcomes obtained from the different data availability × sampling design strategies. Even though we were able to find new species more effectively through systematic sampling designs, this did not 1) translate into reduced errors in conservation planning outcomes for rare species and 2) meet our goal of enhancing cost‐effectiveness of conservation planning. Our results suggest that collecting more biodiversity data, irrespective of the sampling design used, does not necessarily reduce data uncertainty issues and could lead to the misuse of the limited resources and ultimately the failure of conservation practice.     

Optimizing resiliency of reserve networks to climate change: multispecies conservation planning in the Pacific Northwest,USA

The effectiveness of a system of reserves may be compromised under climate change as species' habitat shifts to nonreserved areas, a problem that may be compounded when well‐studied vertebrate species are used as conservation umbrellas for other taxa. The Northwest Forest Plan was among the first efforts to integrate conservation of wide‐ranging focal species and localized endemics into regional conservation planning. We evaluated how effectively the plan's focal species, the Northern Spotted Owl, acts as an umbrella for localized species under current and projected future climates and how the regional system of reserves can be made more resilient to climate change. We used the program maxent to develop distribution models integrating climate data with vegetation variables for the owl and 130 localized species. We used the program zonation to identify a system of areas that efficiently captures habitat for both the owl and localized species and prioritizes refugial areas of climatic and topographic heterogeneity where current and future habitat for dispersal‐limited species is in proximity. We projected future species' distributions based on an ensemble of contrasting climate models, and incorporating uncertainty between alternate climate projections into the prioritization process. Reserve solutions based on the owl overlap areas of high localized‐species richness but poorly capture core areas of localized species' distribution. Congruence between priority areas across taxa increases when refugial areas are prioritized. Although core‐area selection strategies can potentially increase the conservation value and resilience of regional reserve systems, they accentuate contrasts in priority areas between species and over time and should be combined with a broadened taxonomic scope and increased attention to potential effects of climate change. Our results suggest that systems of fixed reserves designed for resilience can increase the likelihood of retaining the biological diversity of forest ecosystems under climate change.     

三江源地区雪豹保护优先区规划

为了将有限资源合理投放到关键区域, 实现物种保护成效的最大化, 找出质量最好的栖息地及它们之间的迁徙通道是制定保护规划的第一步。本研究以三江源的雪豹(Panthera uncia)栖息地为对象, 基于野外调查数据和高分辨率卫星遥感数据, 利用物种分布模型、保护规划模型和连通度分析工具, 找出了三江源地区雪豹的核心栖息地分布和潜在迁徙通道位置, 分析了目前保护中的潜在威胁, 并提出了针对三江源西、中、东三块区域的不同保护对策。结果表明: (1)三江源西部核心栖息地比较小而破碎, 但迁徙通道较多且没有明显窄点, 未来应关注青藏线的潜在阻碍作用, 同时应防范道路沿线的野生动物盗猎; (2)中部区域横跨玉树-杂多-囊谦的雪豹栖息地是三江源最大的核心雪豹栖息地, 在连通其他种群中也处于中心地位, 应通过种群监测确定其健康稳定, 对开发、偷猎等威胁防微杜渐, 保持其源种群的作用; (3)东部区域人口密度高, 受人类活动的影响最大, 需保证阿尼玛卿、年保玉则两块核心栖息地的质量, 并重点监测甘德县境内的省道处雪豹的迁徙通道是否畅通。三江源地区雪豹栖息地总体质量较好, 建议将维持核心源种群的稳定性, 保持种群间迁徙通道的畅通作为三江源的雪豹景观保护工作的整体目标。未来应充分利用天地一体化监测手段, 开展重要保护物种栖息地状况的评估和预警, 尤其是非保护地区域物种核心栖息地的开发建设活动。     

Citizen science data predict high potential for macrofungal refugia outside protected riparian areas

Protected areas with restricted management can provide refugia for fungi, but are usually selected based on conservation strategies for flora and fauna. Despite the important role of fungi in floodplains, they are rarely considered in conservation projects. The SwissFungi database covering all biogeographic regions in Switzerland, and consisting of 84% citizen science data, provided a valuable basis to define fungal riparian species: 99.29% of 990 species were reported at least once from the riparian zone, while 15% of species showed a measurable riparian affinity. Species distribution modelling for 129 riparian macrofungi revealed that the predicted distribution is driven by temperature for most species. There were significantly more records per species inside compared to outside protected areas, when correcting for size differences (21% of the area in Switzerland is protected). In contrast, the model predicted significantly more suitable habitat outside currently protected areas. Unprotected areas harbor a high potential for the creation of newly protected areas for the conservation of riparian fungi. The database information and the modelling approach provided crucial information for future monitoring and conservation projects along rivers.     

长江中游生态区湿地保护空缺分析及其保护网络构建

构建了综合反映长江中游生态区地形、植被与地表覆盖特征的GIS综合空间数据库,在对长江中游生态区核心湿地保护物种,即白鹤（Grusleucogeranus）、东方白鹳（Ciconiaboyciana）、小白额雁（Anser erythropus）、中华秋沙鸭（Mergus squamatus）分布范围和生境需求分析基础上,建立了反映物种分布与GIS数据库中生态地理因子关联的生境适宜性单元,从而确定了核心物种分布的潜在生境,对照现有保护区分布格局,找到了区域湿地生境保护的薄弱和空缺区域（Gaps）,评价了现有保护区系统对潜在生境保护的有效性。基于Gap分析结果和现有保护区分布格局,进一步考虑保护网络的整体性和连通性以及保护成本,构建了长江中游湿地保护区生物保护网络的合理格局。研究结果表明：长江中游生态区核心物种潜在生境涉及134个县（市）级单元,而目前长江中游湿地保护网络仅覆盖了23．49％的潜在生境,仍有大量潜在生境游离于现有保护区系统外。为完善长江中游生态区湿地保护网络,应在湖北（13县）、安徽（8县）和江西（1县）所属22个县域内建立新的湿地保护区或保护小区,并与现有湿地保护系统有机整合,最终才能形成较为完善的由45个县级单元构成的的长江中游生态区湿地生物保护网络合理格局,并保护80％以上核心物种的潜在生境。本研究同时也表明基于GIS生态地理空间数据库和生境适宜性单元概念进行大尺度生境分析具有一定可行性和应用价值。     

Habitat suitability models to make conservation decisions based on areas of high species richness and endemism

Biodiversity positively relates with the provisioning of ecosystem services and preserving areas with elevated diversity of highly-functional species could help to ensure human well-being. Most studies addressed to make these decisions use maps relying on species occurrences, where sites containing several species are proposed as priority conservation areas. These maps, however, may underestimate species richness because of the incompleteness of occurrence data. To improve this methodology, we propose using habitat suitability models to estimate the potential distribution of species from occurrence data, and later shaping richness maps by overlapping these predicted distribution ranges. We tested this proposal with Mexican oaks because they provide several ecosystem services and habitat suitability models of species were calibrated with MaxEnt. We used linear regressions to compare the outputs of these predictive maps with those of maps based on species occurrences only and, for both mapping methods, we assessed how much surface of sites with elevated richness and endemism of oaks is currently included within nature reserves. Both mapping methods indicated that oak species are concentrated in mountain regions of Mexico, but predictive maps based on habitat suitability models indicated higher oak richness and endemism that maps based on species occurrences only. Our results also indicated that nature reserves cover a small fraction of areas harboring elevated richness and endemism of oaks. These results suggest that estimating richness across extensive geographic regions using habitat suitability models quickly provides accurate information to make conservation decisions for highly-functional species groups.     

Evaluating the reliability of species distribution models with an indirect measure of bird reproductive performance

Measures of fitness such as reproductive performance are considered reliable indicators of habitat quality for a species. Such measures are, however, only available in a restricted number of sites, which prevents them from being used to quantify habitat quality across landscapes or regions. Alternatively, species presence records can be used along with environmental variables to build models that predict the distribution of species across larger spatial extents. Model predictions are often used for management purposes as they are assumed to describe the quality of the habitats to support a species. Yet, given that species are often present both in optimal and suboptimal areas, the use of data collected during the breeding season to build these models may potentially result in misleading predictions of habitat quality for the reproduction of the species, with potentially significant conservation consequences. In this study we analysed the relationship between fitness parameters informing on habitat quality for reproduction and predictions of species distribution models at multiple spatial scales using two independent sets of data. For 19 passerine bird species, we compared an indirect measure of reproductive performance (ratio of juveniles‐to‐adults) – obtained from Constant Effort Sites (CES) mist‐netting data in Catalonia – with the predictions of models based on bird presence records collected during the Catalan Breeding Bird Atlas (CBBA). A positive relationship between the predictions derived from species distribution models and the reproductive performance of the species was found for almost half of the species at one or more spatial scales. This result suggests that species distribution models may help to predict habitat quality for some species over some extents. However, caution is needed as this is not consistent for all species at all scales. Further work based on species‐ and scale‐specific approaches is now required to understand in which situations species distribution models provide predictions that are in line with reproductive performance.     

The national responsibility approach to setting conservation priorities—Recommendations for its use

The implementation of conservation strategies for species and habitats is frequently hampered by the availability of the necessary resources. These should be prioritized and focused on those species and habitats most in need, but also in regard to the importance of their distribution in a certain region, country or other administrative unit. In that perspective, the concept of national responsibilities (NR) is a recently developed tool to support priority setting. It captures the impact of the loss of a particular species or habitat within the focal region (usually a country) may have on the global persistence of that species or habitat type. Although the method consists of a few simple steps and is not very demanding in regard to data availability per species and habitat type, it is still impossible to determine NRs for all species and habitats. Here, we focus on the difficulties in determining NRs due to missing distribution data, varying interpretations of definitions especially in respect to habitat types, and differences in data formats and maps using European examples of these data limitations and sources of bias. These include artificially enlarged distribution areas resulting from grid cells being reported more than once, gridded shapefiles stretching into the sea or into other biogeographic regions, and differences in the size and the shape of grid cells and hence the resolution of maps. While focusing on European examples, these sources of bias are also relevant for conservation efforts on a global scale. Our analysis stresses the importance of quickly improving data quality, availability and comparability to render conservation more efficient. We give policy relevant examples on how the NR approach can be applied, e.g. how to help attributing budgets to poorer countries, on which species and habitats to focus limited monitoring resources, and how to consider newly emerging diseases. Generally, our analyses suggests (i) to develop clear global data standards, (ii) to regularly assess data to keep up with advances in data handling, and (iii) to use downscaling approaches for biodiversity data to a higher resolution for reducing the impact of bias to a negligible level together with improving the overall quality of distribution data for conservation purposes.     

Habitat selection by South Island saddlebacks and Stewart Island robins reintroduced to Ulva Island

Determining whether animals select some habitats over others provides basic information about how animals meet their requirements for survival and reproduction. Habitat selection is therefore an important component of conservation research. Studies involving the release and establishment of threatened species on island refuges can be particularly insightful because breeding pairs should be able to select habitat of the highest quality within the range available. This study uses GIS technology to investigate the spatial distribution of breeding territories in relation to overall habitat availability of two threatened passerines, South Island saddlebacks (Philesturnus carunculatus carunculatus) and Stewart Island robins (Petroica australis rakiura) two years after their release onto predator-free Ulva Island. Both species established breeding territories around the periphery of the island in coastal forest fringe habitat and away from mature forest in the interior of the island. Compositional analysis suggested that both species prefer dense, fringe-type habitat with open ground cover and deep litter layers and avoid more mature forests, especially with moss cover. Thus habitat structure is likely to be more important for both species than plant-species composition. However, the possibility exists that the preference of coastal fringe habitat could represent an ‘ecological trap’, where habitat preference does not correspond to better quality habitat in terms of reproductive fitness. It will be useful to continue monitoring saddlebacks and robins to obtain data on survival and fecundity as the density of birds increases, and breeding pairs are forced to establish territories in what is presently perceived to be less preferred habitat in the interior part of the island.     

The dragonfly delusion: why it is essential to sample exuviae to avoid biased surveys

Odonate populations and species numbers are declining globally. Successful conservation requires sound assessments of both odonate distributions and habitat requirements. Odonates have aquatic (larval) and terrestrial (adult) stages, but most surveys that are used to inform conservation managers are undertaken of the adult stage. This study investigates whether this bias towards adult records in odonate recording is misinterpreting the environmental quality of sites. The habitat focus is farmland ponds, a key feature of agricultural landscapes. We tested whether or not, adult, larval and exuvial surveys lead to similar conclusions on species richness and hence on pond quality. Results showed that pond surveys based upon larvae and exuviae are equally suitable for the reliable assessment of presence/absence of odonates, but that adult surveys are not interchangeable with surveys of larvae/exuviae. Larvae were also found at ponds with no emerging individuals due to changes in habitat quality, therefore presence of exuviae remains the only proof of life-cycle completion at a site. Ovipositing females were recorded at all ponds where exuviae were totally absent hence adult surveys over-estimate pond quality and low-quality ponds are functioning as ecological traps. Highly mobile and generalist species were recorded at more locations than other species. Adult surveys also bias recording towards genera, species and populations with non-territorial mate-location strategies. Odonate biodiversity monitoring would benefit from applying the best survey method (exuviae) to avoid wasting valuable financial resources while providing unbiased data, necessary to achieve conservation objectives.     

Integrating Multiple Distribution Models to Guide Conservation Efforts of an Endangered Toad

Species distribution models are used for numerous purposes such as predicting changes in species’ ranges and identifying biodiversity hotspots. Although implications of distribution models for conservation are often implicit, few studies use these tools explicitly to inform conservation efforts. Herein, we illustrate how multiple distribution models developed using distinct sets of environmental variables can be integrated to aid in identification sites for use in conservation. We focus on the endangered arroyo toad (Anaxyrus californicus), which relies on open, sandy streams and surrounding floodplains in southern California, USA, and northern Baja California, Mexico. Declines of the species are largely attributed to habitat degradation associated with vegetation encroachment, invasive predators, and altered hydrologic regimes. We had three main goals: 1) develop a model of potential habitat for arroyo toads, based on long-term environmental variables and all available locality data; 2) develop a model of the species’ current habitat by incorporating recent remotely-sensed variables and only using recent locality data; and 3) integrate results of both models to identify sites that may be employed in conservation efforts. We used a machine learning technique, Random Forests, to develop the models, focused on riparian zones in southern California. We identified 14.37% and 10.50% of our study area as potential and current habitat for the arroyo toad, respectively. Generally, inclusion of remotely-sensed variables reduced modeled suitability of sites, thus many areas modeled as potential habitat were not modeled as current habitat. We propose such sites could be made suitable for arroyo toads through active management, increasing current habitat by up to 67.02%. Our general approach can be employed to guide conservation efforts of virtually any species with sufficient data necessary to develop appropriate distribution models.     

Applications and techniques for non-invasive faecal genetics research in felid conservation

Non-invasive genetic techniques utilising DNA extracted from faeces hold great promise for felid conservation research. These methods can be used to establish species distributions, model habitat requirements, analyse diet, estimate abundance and population density, and form the basis for population, landscape and conservation genetic analyses. Due to the elusive nature of most felid species, non-invasive genetic methods have the potential to provide valuable data that cannot be obtained with traditional observational or capture techniques. Thus, these methods are particularly valuable for research and conservation of endangered felid species. Here, we review recent studies that use non-invasive faecal genetic techniques to survey or study wild felids; provide an overview of field, laboratory and analysis techniques; and offer suggestions on how future non-invasive genetic studies can be expanded or improved to more effectively support conservation.