Advancing Primate Research and Conservation Through the Use of Camera Traps: Introduction to the Special Issue

Effective conservation and management of primates depend on our ability to accurately assess and monitor populations through research. Camera traps are proving to be useful tools for studying a variety of primate species, in diverse and often difficult habitats. Here, we discuss the use of camera traps in primatology to survey rare species, assess populations, and record behavior. We also discuss methodological considerations for primate studies, including camera trap research design, inherent biases, and some limitations of camera traps. We encourage other primatologists to use transparent and standardized methods, and when appropriate to consider using occupancy framework to account for imperfect detection, and complementary techniques, e.g., transect counts, interviews, behavioral observation, to ensure accuracy of data interpretation. In addition, we address the conservation implications of camera trapping, such as using data to inform industry, garner public support, and contributing photos to large-scale habitat monitoring projects. Camera trap studies such as these are sure to advance research and conservation of primate species. Finally, we provide commentary on the ethical considerations, e.g., photographs of humans and illegal activity, of using camera traps in primate research. We believe ethical considerations will be particularly important in future primate studies, although this topic has not previously been addressed for camera trap use in primatology or any wildlife species.     

Improving species occupancy estimation when sampling violates the closure assumption

Site occupancy models that account for imperfect detection of species are increasingly utilized in ecological research and wildlife monitoring. Occupancy models require replicate surveys to estimate detection probability over a time period where the occupancy status at sampled sites is assumed closed. Unlike mark–recapture models, few studies have examined how violations of closure can bias occupancy estimates. Our study design allowed us to differentiate among two processes that violate the closure assumption during a sampling season: 1) repeated destructive sampling events that result in either short‐ or long‐term site avoidance by the target species and 2) sampling occurring over a time period during which non‐random movements of the target species result in variable occupancy status. We used dynamic occupancy models to quantify the potential bias in occupancy estimation associated with these processes for a terrestrial salamander system. Our results provide strong evidence of a systematic decrease in salamander occupancy within a field season. Chronic disturbance due to repeated searches of natural cover objects accelerated natural declines in species occurrence on the forest surface as summer progressed. We also observed a strong but temporary disturbance effect on salamander detection probability associated with repeated sampling within a 24‐h. period. We generalized our findings by conducting a simulation to evaluate how violations of closure can bias occupancy estimates when local extinction occurs within a sampling season. Our simulation study revealed general sensitivity of estimates from single‐season occupancy models to violations of closure, with the strength and direction of bias varying between scenarios. Bias was minimal when extinction proba bility or the number of sample occasions was relatively low. Our research highlights the importance of addressing closure in occupancy studies and we provide multiple solutions, using both design‐ and model‐based frameworks, for minimizing bias associated with non‐random changes in occupancy and repeated sampling disturbances.     

Predator–Primate Distribution,Activity, and Co-occurrence in Relation to Habitat and Human Activity Across Fragmented and Contiguous Forests in Northeastern Madagascar

Predator–primate interactions are understudied, yet predators have been shown to influence primate behavior, population dynamics, and spatial distribution. An understanding of these interactions is important for the successful management and conservation of these species. Novel approaches are needed to understand better the spatial relationships between predators and primates across changing landscapes. We combined photographic surveys of predators and humans with line-transect sampling of lemurs across contiguous and fragmented forests in Madagascar to 1) compare relative activity; 2) estimate probability of occupancy and detection; 3) estimate predator–primate and local people–primate co-occurrence; and 4) assess variables influencing these parameters across contiguous and fragmented forests. In fragmented (compared to contiguous) forest sites endemic predator and lemur activity were lower whereas introduced predator and local people activity were higher. Our two-species interaction occupancy models revealed a higher number of interactions among species across contiguous forest where predator and lemur occupancy were highest. Mouse lemurs show evidence of “avoidance” (SIF < 1.0) with all predator species (endemic and introduced) in contiguous forest whereas white-fronted brown lemurs show “attraction” (SIF > 1.0) with feral cats and local people in contiguous forest. Feral cats demonstrated the highest number of interactions with lemurs, despite their distribution being limited to only contiguous forest. Distance to forest edge and distance to nearby villages were important in predicting predator occupancy and detection. These results highlight the growing threat to endemic predators and lemurs as habitat loss and fragmentation increase throughout Madagascar. We demonstrate the effectiveness of a novel combination of techniques to investigate how predator species impact primate species across a gradient of forest fragmentation.     

Estimating occupancy dynamics for large‐scale monitoring networks: amphibian breeding occupancy across protected areas in the northeast United States

Regional monitoring strategies frequently employ a nested sampling design where a finite set of study areas from throughout a region are selected and intensive sampling occurs within a subset of sites within the individual study areas. This sampling protocol naturally lends itself to a hierarchical analysis to account for dependence among subsamples. Implementing such an analysis using a classic likelihood framework is computationally challenging when accounting for detection errors in species occurrence models. Bayesian methods offer an alternative approach for fitting models that readily allows for spatial structure to be incorporated. We demonstrate a general approach for estimating occupancy when data come from a nested sampling design. We analyzed data from a regional monitoring program of wood frogs (Lithobates sylvaticus) and spotted salamanders (Ambystoma maculatum) in vernal pools using static and dynamic occupancy models. We analyzed observations from 2004 to 2013 that were collected within 14 protected areas located throughout the northeast United States. We use the data set to estimate trends in occupancy at both the regional and individual protected area levels. We show that occupancy at the regional level was relatively stable for both species. However, substantial variation occurred among study areas, with some populations declining and some increasing for both species. In addition, When the hierarchical study design is not accounted for, one would conclude stronger support for latitudinal gradient in trends than when using our approach that accounts for the nested design. In contrast to the model that does not account for nesting, the nested model did not include an effect of latitude in the 95% credible interval. These results shed light on the range‐level population status of these pond‐breeding amphibians, and our approach provides a framework that can be used to examine drivers of local and regional occurrence dynamics.     

Estimating the abundance of burrow‐nesting species through the statistical analysis of combined playback and visual surveys

The conservation of elusive species relies on our ability to obtain unbiased estimates of their abundance trends. Many species live or breed in cavities, making it easy to define the search units (the cavity) yet hard to ascertain their occupancy. One such example is that of certain colonial seabirds like petrels and shearwaters, which occupy burrows to breed. In order to increase the chances of detection for these types of species, their sampling can be done using two independent methods to check for cavity occupancy: visual inspection, and acoustic response to a playback call. This double‐detection process allows us to estimate the probability of burrow occupancy by accounting for the probability of detection associated with each method. Here we provide a statistical framework to estimate the occupancy and population size of burrow‐dwelling species. We show how to implement the method using both maximum likelihood and Bayesian approaches, and test its precision and bias using simulated datasets. We subsequently illustrate how to extend the method to situations where two different species may occupy the burrows, and apply it to a dataset on wedge‐tailed shearwaters Puffinus pacificus and tropical shearwaters P. bailloni on Aride Island, Seychelles. The simulations showed that the single‐species model performed well in terms of error and bias except when detection probabilities and occupancies were very low. The two‐species model applied to shearwaters showed that detection probabilities were highly heterogeneous. The population sizes of wedge‐tailed and tropical shearwaters were estimated at 13 716 (95% CI: 12 909–15 874) and 25 550 (23 667–28 777) pairs respectively. The advantages of formulating the call‐playback sampling method statistically is that it provides a framework to calculate uncertainty in the estimates and model assumptions. This method is applicable to a variety of cavity‐dwelling species where two methods can be used to detect cavity occupancy.     

Assessing the impacts of imperfect detection on estimates of diversity and community structure through multispecies occupancy modeling

Detecting all species in a given survey is challenging, regardless of sampling effort. This issue, more commonly known as imperfect detection, can have negative impacts on data quality and interpretation, most notably leading to false absences for rare or difficult‐to‐detect species. It is important that this issue be addressed, as estimates of species richness are critical to many areas of ecological research and management. In this study, we set out to determine the impacts of imperfect detection, and decisions about thresholds for inclusion in occupancy, on estimates of species richness and community structure. We collected data from a stream fish assemblage in Algonquin Provincial Park to be used as a representation of ecological communities. We then used multispecies occupancy modeling to estimate species‐specific occurrence probabilities while accounting for imperfect detection, thus creating a more informed dataset. This dataset was then compared to the original to see where differences occurred. In our analyses, we demonstrated that imperfect detection can lead to large changes in estimates of species richness at the site level and summarized differences in the community structure and sampling locations, represented through correspondence analyses.     

Inferring species richness using multispecies occupancy modeling: Estimation performance and interpretation

Multispecies occupancy models can estimate species richness from spatially replicated multispecies detection/non‐detection survey data, while accounting for imperfect detection. A model extension using data augmentation allows inferring the total number of species in the community, including those completely missed by sampling (i.e., not detected in any survey, at any site). Here we investigate the robustness of these estimates. We review key model assumptions and test performance via simulations, under a range of scenarios of species characteristics and sampling regimes, exploring sensitivity to the Bayesian priors used for model fitting. We run tests when assumptions are perfectly met and when violated. We apply the model to a real dataset and contrast estimates obtained with and without predictors, and for different subsets of data. We find that, even with model assumptions perfectly met, estimation of the total number of species can be poor in scenarios where many species are missed (>15%–20%) and that commonly used priors can accentuate overestimation. Our tests show that estimation can often be robust to violations of assumptions about the statistical distributions describing variation of occupancy and detectability among species, but lower‐tail deviations can result in large biases. We obtain substantially different estimates from alternative analyses of our real dataset, with results suggesting that missing relevant predictors in the model can result in richness underestimation. In summary, estimates of total richness are sensitive to model structure and often uncertain. Appropriate selection of priors, testing of assumptions, and model refinement are all important to enhance estimator performance. Yet, these do not guarantee accurate estimation, particularly when many species remain undetected. While statistical models can provide useful insights, expectations about accuracy in this challenging prediction task should be realistic. Where knowledge about species numbers is considered truly critical for management or policy, survey effort should ideally be such that the chances of missing species altogether are low.     

The importance of including imperfect detection models in eDNA experimental design

Environmental DNA (eDNA) is DNA that has been isolated from field samples, and it is increasingly used to infer the presence or absence of particular species in an ecosystem. However, the combination of sampling procedures and subsequent molecular amplification of eDNA can lead to spurious results. As such, it is imperative that eDNA studies include a statistical framework for interpreting eDNA presence/absence data. We reviewed published literature for studies that utilized eDNA where the species density was known and compared the probability of detecting the focal species to the sampling and analysis protocols. Although biomass of the target species and the volume per sample did not impact detectability, the number of field replicates and number of samples from each replicate were positively related to detection. Additionally, increased number of PCR replicates and increased primer specificity significantly increased detectability. Accordingly, we advocate for increased use of occupancy modelling as a method to incorporate effects of sampling effort and PCR sensitivity in eDNA study design. Based on simulation results and the hierarchical nature of occupancy models, we suggest that field replicates, as opposed to molecular replicates, result in better detection probabilities of target species.     

Studying Large Mammals With Imperfect Detection: Status and Habitat Preferences of Wild Cattle and Large Carnivores in Eastern Cambodia

Studying large mammal species in tropical forests is a conservation challenge with species’ behavior and ecology often increasing the probability of non‐detection during surveys. Consequently, knowledge of the distribution, status, and natural history of many large mammal species in Southeast Asia is limited. I developed occupancy models from camera‐trapping data, thereby accounting for imperfect detection at sampling sites, to clarify the status and habitat requirements of four globally threatened or near threatened large mammals (banteng Bos javanicus, gaur Bos gaurus, dhole Cuon alpinus, and leopard Panthera pardus) in Mondulkiri Protected Forest, eastern Cambodia. Camera traps were operational for >3500 trap nights with 202 photographic encounters of the four study species. Model averaged occupancy estimates were between 5 percent (leopard) and 140 percent (gaur) higher than naive estimates (i.e., proportion of camera‐trap sites species recorded from) thus highlighting the importance of accounting for detectability during conservation surveys. I recommend the use of an occupancy framework when using camera‐trap data to study the status, ecology, and habitat preferences of poorly known and elusive species. The results highlight the importance of mixed deciduous and semi‐evergreen forest for wild cattle in eastern Cambodia and I emphasize that these habitats must be considered in conservation planning across the Lower Mekong Dry Forest Ecoregion.     

Use of classical bird census transects as spatial replicates for hierarchical modeling of an avian community

New monitoring programs are often designed with some form of temporal replication to deal with imperfect detection by means of occupancy models. However, classical bird census data from earlier times often lack temporal replication, precluding detection‐corrected inferences about occupancy. Historical data have a key role in many ecological studies intended to document range shifts, and so need to be made comparable with present‐day data by accounting for detection probability. We analyze a classical bird census conducted in the region of Murcia (SE Spain) in 1991 and 1992 and propose a solution to estimating detection probability for such historical data when used in a community occupancy model: the spatial replication of subplots nested within larger plots allows estimation of detection probability. In our study, the basic sample units were 1‐km transects, which were considered spatial replicates in two aggregation schemes. We fit two Bayesian multispecies occupancy models, one for each aggregation scheme, and evaluated the linear and quadratic effect of forest cover and temperature, and a linear effect of precipitation on species occupancy probabilities. Using spatial rather than temporal replicates allowed us to obtain individual species occupancy probabilities and species richness accounting for imperfect detection. Species‐specific occupancy and community size decreased with increasing annual mean temperature. Both aggregation schemes yielded estimates of occupancy and detectability that were highly correlated for each species, so in the design of future surveys ecological reasons and cost‐effective sampling designs should be considered to select the most suitable aggregation scheme. In conclusion, the use of spatial replication may often allow historical survey data to be applied formally hierarchical occupancy models and be compared with modern‐day data of the species community to analyze global change process.     

The Use (and Misuse) of Phylogenetic Trees in Comparative Behavioral Analyses

Phylogenetic comparative methods play a critical role in our understanding of the adaptive origin of primate behaviors. To incorporate evolutionary history directly into comparative behavioral research, behavioral ecologists rely on strong, well-resolved phylogenetic trees. Phylogenies provide the framework on which behaviors can be compared and homologies can be distinguished from similarities due to convergent or parallel evolution. Phylogenetic reconstructions are also of critical importance when inferring the ancestral state of behavioral patterns and when suggesting the evolutionary changes that behavior has undergone. Improvements in genome sequencing technologies have increased the amount of data available to researchers. Recently, several primate phylogenetic studies have used multiple loci to produce robust phylogenetic trees that include hundreds of primate species. These trees are now commonly used in comparative analyses and there is a perception that we have a complete picture of the primate tree. But how confident can we be in those phylogenies? And how reliable are comparative analyses based on such trees? Herein, we argue that even recent molecular phylogenies should be treated cautiously because they rely on many assumptions and have many shortcomings. Most phylogenetic studies do not model gene tree diversity and can produce misleading results, such as strong support for an incorrect species tree, especially in the case of rapid and recent radiations. We discuss implications that incorrect phylogenies can have for reconstructing the evolution of primate behaviors and we urge primatologists to be aware of the current limitations of phylogenetic reconstructions when applying phylogenetic comparative methods.     

Red-Shouldered Hawk Occupancy Surveys in Central Minnesota,USA

ABSTRACT Forest-dwelling raptors are often difficult to detect because many species occur at low density or are secretive. Broadcasting conspecific vocalizations can increase the probability of detecting forest-dwelling raptors and has been shown to be an effective method for locating raptors and assessing their relative abundance. Recent advances in statistical techniques based on presence—absence data use probabilistic arguments to derive probability of detection when it is < 1 and to provide a model and likelihood-based method for estimating proportion of sites occupied. We used these maximum-likelihood models with data from red-shouldered hawk (Buteo lineatus) call-broadcast surveys conducted in central Minnesota, USA, in 1994–1995 and 2004–2005. Our objectives were to obtain estimates of occupancy and detection probability 1) over multiple sampling seasons (yr), 2) incorporating within-season time-specific detection probabilities, 3) with call type and breeding stage included as covariates in models of probability of detection, and 4) with different sampling strategies. We visited individual survey locations 2–9 times per year, and estimates of both probability of detection (range = 0.28-0.54) and site occupancy (range = 0.81-0.97) varied among years. Detection probability was affected by inclusion of a within-season time-specific covariate, call type, and breeding stage. In 2004 and 2005 we used survey results to assess the effect that number of sample locations, double sampling, and discontinued sampling had on parameter estimates. We found that estimates of probability of detection and proportion of sites occupied were similar across different sampling strategies, and we suggest ways to reduce sampling effort in a monitoring program.     

A patch-dynamic framework for food web metacommunities

The metacommunity concept has proved to be a valuable tool for studying how space can affect the properties and assembly of competitive communities. However, the concept has not been as extensively applied to the study of food webs or trophically structured communities. Here, we demonstrate how to develop a modelling framework that permits food webs to be considered from a spatial perspective. We do this by broadening the classic metapopulation patch-dynamic framework so that it can also account for trophic interactions between many species and patches. Unlike previous metacommunity models, we argue that this requires a system of equations to track the changing patch occupancy of the various species interactions, not the patch occupancy of individual species. We then suggest how this general theoretical framework can be used to study complex and spatially extended food web metacommunities.     

Inferential biases linked to unobservable states in complex occupancy models

Modeling of species distributions has undergone a shift from relying on equilibrium assumptions to recognizing transient system dynamics explicitly. This shift has necessitated more complex modeling techniques, but the performance of these dynamic models has not yet been assessed for systems where unobservable states exist. Our work is motivated by the impacts of the emerging infectious disease chytridiomycosis, a disease of amphibians that is associated with declines of many species worldwide. Using this host‐pathogen system as a general example, we first illustrate how misleading inferences can result from failing to incorporate pathogen dynamics into the modeling process, especially when the pathogen is difficult or impossible to survey in the absence of a host species. We found that traditional modeling techniques can underestimate the effect of a pathogen on host species occurrence and dynamics when the pathogen can only be detected in the host, and pathogen information is treated as a covariate. We propose a dynamic multistate modeling approach that is flexible enough to account for the detection structures that may be present in complex multistate systems, especially when the sampling design is limited by a species’ natural history or sampling technology. When multistate occupancy models are used and an unobservable state is present, parameter estimation can be influenced by model complexity, data sparseness, and the underlying dynamics of the system. We show that, even with large sample sizes, many models incorporating seasonal variation in vital rates may not generate reasonable estimates, indicating parameter redundancy. We found that certain types of missing data can greatly hinder inference, and we make study design recommendations to avoid these issues. Additionally, we advocate the use of time‐varying covariates to explain temporal trends in the data, and the development of sampling techniques that match the biology of the system to eliminate unobservable states when possible.     

Environmental DNA (eDNA) Sampling Improves Occurrence and Detection Estimates of Invasive Burmese Pythons

Environmental DNA (eDNA) methods are used to detect DNA that is shed into the aquatic environment by cryptic or low density species. Applied in eDNA studies, occupancy models can be used to estimate occurrence and detection probabilities and thereby account for imperfect detection. However, occupancy terminology has been applied inconsistently in eDNA studies, and many have calculated occurrence probabilities while not considering the effects of imperfect detection. Low detection of invasive giant constrictors using visual surveys and traps has hampered the estimation of occupancy and detection estimates needed for population management in southern Florida, USA. Giant constrictor snakes pose a threat to native species and the ecological restoration of the Florida Everglades. To assist with detection, we developed species-specific eDNA assays using quantitative PCR (qPCR) for the Burmese python (Python molurus bivittatus), Northern African python (P. sebae), boa constrictor (Boa constrictor), and the green (Eunectes murinus) and yellow anaconda (E. notaeus). Burmese pythons, Northern African pythons, and boa constrictors are established and reproducing, while the green and yellow anaconda have the potential to become established. We validated the python and boa constrictor assays using laboratory trials and tested all species in 21 field locations distributed in eight southern Florida regions. Burmese python eDNA was detected in 37 of 63 field sampling events; however, the other species were not detected. Although eDNA was heterogeneously distributed in the environment, occupancy models were able to provide the first estimates of detection probabilities, which were greater than 91%. Burmese python eDNA was detected along the leading northern edge of the known population boundary. The development of informative detection tools and eDNA occupancy models can improve conservation efforts in southern Florida and support more extensive studies of invasive constrictors. Generic sampling design and terminology are proposed to standardize and clarify interpretations of eDNA-based occupancy models.     

Design and Analysis of Line Transect Surveys for Primates

Line transect surveys are widely used for estimating abundance of primate populations. The method relies on a small number of key assumptions, and if these are not met, substantial bias may occur. For a variety of reasons, primate surveys often do not follow what is generally considered to be best practice, either in survey design or in analysis. The design often comprises too few lines (sometimes just 1), subjectively placed or placed along trails, so lacks both randomization and adequate replication. Analysis often involves flawed or inefficient models, and often uses biased estimates of the locations of primate groups relative to the line. We outline the standard method, emphasizing the assumptions underlying the approach. We then consider options for when it is difficult or impossible to meet key assumptions. We explore the performance of these options by simulation, focusing particularly on the analysis of primate group sizes, where many of the variations in survey methods have been developed. We also discuss design issues, field methods, analysis, and potential alternative methodologies for when standard line transect sampling cannot deliver reliable abundance estimates.     

Considerations for using occupancy surveys to monitor forest primates: a case study with Sclater’s monkey (<Emphasis Type="Italic">Cercopithecus sclateri</Emphasis>)

Count-based indices and distance sampling are widely used to monitor primate populations. Indices are often confounded by variation in detectability, whereas distance sampling is generally ineffective with species that flee or hide from observers and where it is difficult to accurately measure detection distances. We tested occupancy modeling as a means to monitor Sclater’s monkey (Cercopithecus sclateri), an endemic of Nigeria. We evaluated effects of survey methodology, habitat, and human disturbance on detection probability and site occupancy. Average detectability was high (p = 0.81), but varied substantially between two observers. Occupancy was highest in areas with intermediate levels (20–40%) of farmland and secondary forest, and was unaffected by human disturbance. Sampling plots (4 and 6.25 ha) did not concurrently contain >1 monkey group, were likely closed to monkey movements during the replicate surveys of each plot, and were spatially separated so that it was unlikely the same group was observed in >1 plot. These conditions enabled the conversion of occupancy to group density. Scaled to 6.25 ha, model-weighted occupancy averaged 0.230 (SE 0.103), yielding an estimate of 3.7 groups/km² (95% CI 1.4–7.7 groups/km²). Because some groups straddled plot boundaries, we assumed that half of these groups were inside the plots, resulting in an adjusted estimate of 3.1 groups/km². Our results illustrate that occupancy can be suitable for monitoring vigilant forest primates where detection distances are difficult to measure. However, special attention is required to choose spatial and temporal scales that accommodate the method’s closure and independent-detection assumptions.     

Does variation between dry and wet seasons affect tropical forest mammals' occupancy and detectability by camera traps? Case study from the Udzungwa Mountains,Tanzania

The increasing use of camera trapping coupled to occupancy analysis to study terrestrial mammals has opened the way to inferential studies that besides estimating the probability of presence explicitly consider detectability. This in turn allows considering factors that can potentially confound the estimation of occupancy and detection probability, including seasonal variations in rainfall. To address this, we conducted a systematic camera trapping survey in the Udzungwa Mountains of Tanzania by deploying twenty camera traps for 30 days in dry and wet seasons and used dynamic occupancy modelling to determine the effect of season on estimated occupancy and detection probability for species with >10 capture events. The sampling yielded 7657 and 6015 images in dry and wet seasons, respectively, belonging to 21 mammal species. Models with no season dependency and with season‐dependent detectability were best supported, indicating that neither colonization nor extinction varied with seasons and hence occupancy did not vary. Only bush pig (Potamochoerus larvatus) showed a significant decrease in detectability from dry to wet seasons. Our study indicates that seasonal variation in rainfall may have limited effect on occupancy and detectability of resident mammals in Udzungwa rainforests; however, it remains a factor to consider when designing future studies.     

Using species distribution and occupancy modeling to guide survey efforts and assess species status

Habitat loss and fragmentation continue to be major issues affecting the persistence and conservation of species, but identification of critical habitat remains a challenge. Species distribution modeling and occupancy modeling are both approaches that have been used to predict species distributions and can identify critical habitat characteristics associated with species occurrence. Additionally, occupancy sampling can provide measures of detectability, increasing the confidence that a species is truly absent when not detected. While increasingly popular, these methods are infrequently used in synergy, and rarely at fine spatial scales. We provide a case study of using distribution and occupancy modeling in unison to direct survey efforts, provide estimates of species presence/absence, and to identify local and landscape features important for species occurrence. The focal species for our study was Ambystoma jeffersonianum, a threatened salamander in the state of Illinois, U.S.A. We found that fine-scale distribution models accurately discriminated occupied from unoccupied breeding ponds (78–91% accuracy), and surveys could be effectively guided using a well-fit model. We achieved a high detection rate (0.774) through occupancy sampling, and determined that A. jeffersonianum never used ponds inhabited by fish, and the probability of a pond being used for breeding increased as canopy cover increased. When faced with limited resources, combining fine-scale distribution modeling with a robust occupancy sampling design can expedite survey efforts, confidently designate species occupancy status, prioritise habitat for future surveys and/or restoration, and identify critical habitat features. This approach is broadly applicable to other taxa that have specific habitat requirements.     

Dry season factors determining habitat use and distribution of mouse deer (Moschiola indica) in the Western Ghats

Ecology of Asia's smallest ungulate, the Indian chevrotain or mouse deer (Moschiola indica), has been poorly assessed. We used camera-trapping data to investigate habitat use of mouse deer in Mudumalai Tiger Reserve. Presence/absence data, collected under a systematic sampling framework, were used to test a priori hypotheses incorporating covariates believed to influence mouse deer occurrence and detection. The average occupancy rate of mouse deer in the study area was 0.56 (SE?=?0.22) with a low detection probability (0.29, SE?=?0.14). Model selection indicated that presence of moist bamboo brakes positively influenced while percent leaf litter negatively influenced mouse deer occupancy. Placement of camera-traps along narrow trails positively influenced detection probability of mouse deer. Future conservation efforts in India should focus on preservation of bamboo vegetation and dense forest cover which provide refuge for the mouse deer. Our results illustrate that occupancy can be suitable for monitoring elusive, forest dwelling, small ungulates; however, caution is needed when applying these models on small ranging species, as our study identifies the limitations in our survey design and its improvement for future monitoring which are applicable for similar-sized species across a range of habitats.