Spatially explicit estimation of occupancy,detection probability and survey effort needed to inform conservation planning

Aim It is increasingly recognized the importance of accounting for imperfect detection in species distribution modelling and conservation planning. However, the integration of detectability into a spatially explicit frame has received little attention. We aim (1) to show how to develop distribution maps of both detection probability and survey effort required to reliably determine a species presence/absence and (2) to increase awareness of the spatial variation of detection error inherent in studies of species occurrence. Location North‐western Spain. Methods  We registered the presence/absence of the endangered Egyptian vulture (Neophron percnopterus) in 213 surveys performed in 40 of 104 territories once known to be occupied. We model simultaneously both detection probability and occurrence, using site occupancy modelling. With the resulting regression equations, we developed distribution maps of both detection probability and required sampling effort throughout the area. Results Of the studied territories, 72.5% were detected as occupied, but after accounting for imperfect detection, the proportion of sites truly occupied was 79%. Detectability decreased in territories with higher topographical irregularity and increased with both the time of day of the survey and the progress of the season. Spatial distribution of detectability showed a mainly north–south gradient following the distribution of slope in the area. The likelihood of occupancy increased with rockier, less forested surface and less topographical irregularity within the territory. A minimum of five surveys, on average, are needed to assess, with 95% probability, the occupancy status of a site, ranging from ≤ 3 to > 24 visits/territory depending on survey‐ and site‐specific features. Main conclusions Accounting for detectability and its sources of variation allows us to elaborate distribution maps of detectability‐based survey effort. These maps are useful tools to reliably assess (e.g. with 95% probability) occupancy status throughout a landscape and provide guidance for species conservation planning.     

Conservation Assessments of Arboreal Mammals in Difficult Terrain: Occupancy Modeling of Pileated Gibbons (Hylobates pileatus)

Long-term monitoring programs, wildlife surveys, and other research involving species population assessment require reliable data on population status. Given the logistically challenging nature of some species’ habitats and cryptic behaviors, collecting these data can prove to be a considerable barrier. We used detection/nondetection data from pileated gibbons (Hylobates pileatus) in the Cardamom Mountains of southwest Cambodia to estimate their population occupancy and detectability. We modeled occupancy using elevation, tree height, tree density, tree diversity, and disturbance covariates. Modeling demonstrated that 83% of the sites are occupied by Hylobates pileatus and that the detectability of the species varies positively with elevation. No clear relationship between habitat quality covariates and occupancy of Hylobates pileatus emerged. Effort analysis based on model estimates demonstrated that at high elevations, less than half the number of site visits is needed to attain the same detectability estimate precision as across all elevations. We suggest that human activities at low elevations, which affect forest composition, are the central factors impacting the detectability and occupancy of Hylobates pileatus. Longer sampling durations and/or a higher number of site visits, especially at lower elevations, increase precision of the occupancy estimator for the least effort. For effective future monitoring and research for this and similar species, using this relatively simple method, applied with repeat site visits, would allow a longitudinal comparison of detection at sites in difficult terrain.     

Occupancy modelling as a new approach to assess supranational trends using opportunistic data: a pilot study for the damselfly Calopteryx splendens

There is limited information available on changes in biodiversity at the European scale, because there is a lack of data from standardised monitoring for most species groups. However, a great number of observations made without a standardised field protocol is available in many countries for many species. Such opportunistic data offer an alternative source of information, but unfortunately such data suffer from non-standardised observation effort and geographical bias. Here we describe a new approach to compiling supranational trends using opportunistic data which adjusts for these two major imperfections. The non-standardised observation effort is dealt with by occupancy modelling, and the unequal geographical distribution of sites by a weighting procedure. The damselfly Calopteryx splendens was chosen as our test species. The data were collected from five countries (Ireland, Great Britain, the Netherlands, Belgium and France), covering the period 1990–2008. We used occupancy models to estimate the annual number of occupied 1 × 1 km sites per country. Occupancy models use presence-absence data, account for imperfect detection of species, and thereby correct for between-year variability in observation effort. The occupancy models were run per country in a Bayesian mode of inference using JAGS. The occupancy estimates per country were then aggregated to assess the supranational trend in the number of occupied 1 × 1 km². To adjust for the unequal geographical distribution of surveyed sites, we weighted the countries according to the number of sites surveyed and the range of the species per country. The distribution of C. splendens has increased significantly in the combined five countries. Our trial demonstrated that a supranational trend in distribution can be derived from opportunistic data, while adjusting for observation effort and geographical bias. This opens new perspectives for international monitoring of biodiversity.     

Site-occupancy models may offer new opportunities for dragonfly monitoring based on daily species lists

Monitoring biodiversity is necessary but difficult to achieve in practice, in part because standardized field work is often demanding for volunteer field workers. Collecting opportunistic data on presence and absence of species is much less demanding, but such data may suffer from a number of biases, such as variation in observation effort over time. Here we explore whether site-occupancy models may be helpful to reduce such biases in opportunistic data, especially those caused by temporal variation of observation effort and by incomplete reporting of sightings. Site-occupancy models represent a generalisation of classical metapopulation models to account for imperfect detection; they estimate the probability of sites to be occupied (and of the rates of change, colonisation and extinction rates) while taking into account imperfect detection of a species. The models require so-called presence–absence data from replicated visits for a number of sites (e.g., 20–50). We tested whether these models provide reliable trend estimates if collectors of opportunistic data do not report all species detected. We applied the models to three opportunistic datasets of dragonfly species (1999–2007) in the Netherlands: (1) one-species records, (2) short daily species lists and (3) comprehensive daily species lists. Trend estimates based on a fourth dataset from a standardized monitoring scheme were used as a yardstick to judge the results.The analyses showed that occupancy trends based on comprehensive daily species lists in combination with site-occupancy models were generally similar to those based on the monitoring scheme. But trends based on one-species records and short daily lists were too imprecise to be very useful. In addition, site-occupancy models lead to more realistic occupancy estimates than those obtained from conventional logistic regression analysis. We conclude that comprehensive daily species lists can be useful surrogates for monitoring schemes to assess distributional trends.     

Exploiting opportunistic observations to estimate changes in seasonal site use: An example with wetland birds

Nonsystematically collected, a.k.a. opportunistic, species observations are accumulating at a high rate in biodiversity databases. Occupancy models have arisen as the main tool to reduce effects of limited knowledge about effort in analyses of opportunistic data. These models are generally using long closure periods (e.g., breeding season) for the estimation of probability of detection and occurrence. Here, we use the fact that multiple opportunistic observations in biodiversity databases may be available even within days (e.g., at popular birding localities) to reduce the closure period to 1 day in order to estimate daily occupancies within the breeding season. We use a hierarchical dynamic occupancy model for daily visits to analyze opportunistic observations of 71 species from nine wetlands during 10 years. Our model derives measures of seasonal site use within seasons from estimates of daily occupancy. Comparing results from our “seasonal site use model” to results from a traditional annual occupancy model (using a closure criterion of 2 months or more) showed that our model provides more detailed biologically relevant information. For example, when the aim is to analyze occurrences of breeding species, an annual occupancy model will over‐estimate site use of species with temporary occurrences (e.g., migrants passing by, single itinerary prospecting individuals) as even a single observation during the closure period will be viewed as an occupancy. Alternatively, our model produces estimates of the extent to which sites are actually used. Model validation based on simulated data confirmed that our model is robust to changes and variability in sampling effort and species detectability. We conclude that more information can be gained from opportunistic data with multiple replicates (e.g., several reports per day almost every day) by reducing the time window of the closure criterion to acquire estimates of occupancies within seasons.     

Backpack electrofishing effort and imperfect detection: Influence on riverine fish inventories and monitoring

An increased electrofishing sampling effort will increase detection probabilities of riverine fishes. In this study, a repeat‐sampling approach was used in small to medium‐sized Ontario (Canada) rivers to estimate: (i) species‐specific detection probabilities of freshwater fishes, (ii) the number of sampling events required to confidently detect species, and (iii) the power of timed‐search surveys to detect future distribution (or occupancy) declines. Wadeable habitats at 36 sites were sampled with a backpack electrofisher on four separate dates during the summer low‐flow period in 2013 and 2014. Forty‐two species were collected, including three species of conservation concern (American eel Anguilla rostrata Lacépède, 1802, channel darter Percina copelandi Jordan, 1877, northern sunfish Lepomis peltastes Cope, 1870), and two recreationally important species (largemouth bass Micropterus salmoides Lacépède, 1802 and smallmouth bass Micropterus dolomieu Lacépède, 1802). A hierarchical Bayesian modelling approach was used to estimate detection probabilities and site occupancy for 18 species at four levels of effort: 250, 500, 750 and 1,000 s. In all cases, species detection was imperfect. Search effort had a positive effect on estimates of detection probability and site occupancy and the power to detect declines in future distribution. Detection probabilities ranged from 0.11 to 0.66 with an effort of 250 s, and 0.27 to 0.92 with an effort of 1,000 s. For 13 species, detection and power to detect changes in distribution were significantly improved by increasing sampling effort from 250 to 750 s or 1,000 s. For the channel darter and northern sunfish, three replicate sampling visits (of 750 or 1,000 s duration) are recommended for confident detection.     

Predicting species distributions from checklist data using site‐occupancy models

Aim (1) To increase awareness of the challenges induced by imperfect detection, which is a fundamental issue in species distribution modelling; (2) to emphasize the value of replicate observations for species distribution modelling; and (3) to show how ‘cheap’ checklist data in faunal/floral databases may be used for the rigorous modelling of distributions by site‐occupancy models. Location Switzerland. Methods We used checklist data collected by volunteers during 1999 and 2000 to analyse the distribution of the blue hawker, Aeshna cyanea (Odonata, Aeshnidae), a common dragonfly in Switzerland. We used data from repeated visits to 1‐ha pixels to derive ‘detection histories’ and apply site‐occupancy models to estimate the ‘true’ species distribution, i.e. corrected for imperfect detection. We modelled blue hawker distribution as a function of elevation and year and its detection probability of elevation, year and season. Results The best model contained cubic polynomial elevation effects for distribution and quadratic effects of elevation and season for detectability. We compared the site‐occupancy model with a conventional distribution model based on a generalized linear model, which assumes perfect detectability (p = 1). The conventional distribution map looked very different from the distribution map obtained using site‐occupancy models that accounted for the imperfect detection. The conventional model underestimated the species distribution by 60%, and the slope parameters of the occurrence–elevation relationship were also underestimated when assuming p = 1. Elevation was not only an important predictor of blue hawker occurrence, but also of the detection probability, with a bell‐shaped relationship. Furthermore, detectability increased over the season. The average detection probability was estimated at only 0.19 per survey. Main conclusions Conventional species distribution models do not model species distributions per se but rather the apparent distribution, i.e. an unknown proportion of species distributions. That unknown proportion is equivalent to detectability. Imperfect detection in conventional species distribution models yields underestimates of the extent of distributions and covariate effects that are biased towards zero. In addition, patterns in detectability will erroneously be ascribed to species distributions. In contrast, site‐occupancy models applied to replicated detection/non‐detection data offer a powerful framework for making inferences about species distributions corrected for imperfect detection. The use of ‘cheap’ checklist data greatly enhances the scope of applications of this useful class of models.     

Cannot see the diversity for all the species: Evaluating inclusion criteria for local species lists when using abundant citizen science data

Abundant citizen science data on species occurrences are becoming increasingly available and enable identifying composition of communities occurring at multiple sites with high temporal resolution. However, for species displaying temporary patterns of local occurrences that are transient to some sites, biodiversity measures are clearly dependent on the criteria used to include species into local species lists. Using abundant opportunistic citizen science data from frequently visited wetlands, we investigated the sensitivity of α‐ and β‐diversity estimates to the use raw versus detection‐corrected data and to the use of inclusion criteria for species presence reflecting alternative site use. We tested seven inclusion criteria (with varying number of days required to be present) on time series of daily occurrence status during a breeding season of 90 days for 77 wetland bird species. We show that even when opportunistic presence‐only observation data are abundant, raw data may not produce reliable local species richness estimates and rank sites very differently in terms of species richness. Furthermore, occupancy model based α‐ and β‐diversity estimates were sensitive to the inclusion criteria used. Total species lists (all species observed at least once during a season) may therefore mask diversity differences among sites in local communities of species, by including vagrant species on potentially breeding communities and change the relative rank order of sites in terms of species richness. Very high sampling effort does not necessarily free opportunistic data from its inherent bias and can produce a pattern in which many species are observed at least once almost everywhere, thus leading to a possible paradox: The large amount of biological information may hinder its usefulness. Therefore, when prioritizing among sites to manage or preserve species diversity estimates need to be carefully related to relevant inclusion criteria depending on the diversity estimate in focus.     

Decisions on Temporal Sampling Protocol Influence the Detection of Ecological Patterns

Within‐site variability in species detectability is a problem common to many biodiversity assessments and can strongly bias the results. Such variability can be caused by many factors, including simple counting inaccuracies, which can be solved by increasing sample size, or by temporal changes in species behavior, meaning that the way the temporal sampling protocol is designed is also very important. Here we use the example of mist‐netted tropical birds to determine how design decisions in the temporal sampling protocol can alter the data collected and how these changes might affect the detection of ecological patterns, such as the species‐area relationship (SAR). Using data from almost 3400 birds captured from 21,000 net‐hours at 31 sites in the Brazilian Atlantic Forest, we found that the magnitude of ecological trends remained fairly stable, but the probability of detecting statistically significant ecological patterns varied depending on sampling effort, time of day and season in which sampling was conducted. For example, more species were detected in the wet season, but the SAR was strongest in the dry season. We found that the temporal distribution of sampling effort was more important than its total amount, discovering that similar ecological results could have been obtained with one‐third of the total effort, as long as each site had been equally sampled over 2 yr. We discuss that projects with the same sampling effort and spatial design, but with different temporal sampling protocol are likely to report different ecological patterns, which may ultimately lead to inappropriate conservation strategies.     

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.     

Power to detect trends in abundance of secretive marsh birds: Effects of species traits and sampling effort

Standardized protocols for surveying secretive marsh birds have been implemented across North America, but the efficacy of surveys to detect population trends has not been evaluated. We used survey data collected from populations of marsh birds across North America and simulations to explore how characteristics of bird populations (proportion of survey stations occupied, abundance at occupied stations, and detection probability) and aspects of sampling effort (numbers of survey routes, stations/route, and surveys/station/year) affect statistical power to detect trends in abundance of marsh bird populations. In general, the proportion of survey stations along a route occupied by a species had a greater relative effect on power to detect trends than did the number of birds detected per survey at occupied stations. Uncertainty introduced by imperfect detection during surveys reduced power to detect trends considerably, but across the range of detection probabilities for most species of marsh birds, variation in detection probability had only a minor influence on power. For species that occupy a relatively high proportion of survey stations (0.20), have relatively high abundances at occupied stations (2.0 birds/station), and have high detection probability (0.50), ≥40 routes with 10 survey stations per route surveyed 3 times per year would provide an 80% chance of detecting a 3% annual decrease in abundance after 20 years of surveys. Under the same assumptions but for species that are less common, ≥100 routes would be needed to achieve the same power. Our results can help inform the design of programs to monitor trends in abundance of marsh bird populations, especially with regards to the amount of sampling effort necessary to meet programmatic goals. © 2013 The Wildlife Society     

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.     

Evaluating noninvasive genetic sampling techniques to estimate large carnivore abundance

Monitoring large carnivores is difficult because of intrinsically low densities and can be dangerous if physical capture is required. Noninvasive genetic sampling (NGS) is a safe and cost‐effective alternative to physical capture. We evaluated the utility of two NGS methods (scat detection dogs and hair sampling) to obtain genetic samples for abundance estimation of coyotes, black bears and Canada lynx in three areas of Newfoundland, Canada. We calculated abundance estimates using program capwire , compared sampling costs, and the cost/sample for each method relative to species and study site, and performed simulations to determine the sampling intensity necessary to achieve abundance estimates with coefficients of variation (CV) of <10%. Scat sampling was effective for both coyotes and bears and hair snags effectively sampled bears in two of three study sites. Rub pads were ineffective in sampling coyotes and lynx. The precision of abundance estimates was dependent upon the number of captures/individual. Our simulations suggested that ~3.4 captures/individual will result in a < 10% CV for abundance estimates when populations are small (23–39), but fewer captures/individual may be sufficient for larger populations. We found scat sampling was more cost‐effective for sampling multiple species, but suggest that hair sampling may be less expensive at study sites with limited road access for bears. Given the dependence of sampling scheme on species and study site, the optimal sampling scheme is likely to be study‐specific warranting pilot studies in most circumstances.     

Detection heterogeneity in underwater visual‐census data

This study shows how capture–mark–recapture (CMR) models can provide robust estimates of detection heterogeneity (sources of bias) in underwater visual‐census data. Detection biases among observers and fish family groups were consistent between fished and unfished reef sites in Kenya, even when the overall level of detection declined between locations. Species characteristics were the greatest source of detection heterogeneity and large, highly mobile species were found to have lower probabilities of detection than smaller, site‐attached species. Fish family and functional‐group detectability were also found to be lower at fished locations, probably due to differences in local abundance. Because robust CMR models deal explicitly with sampling where not all species are detected, their use is encouraged for studies addressing reef‐fish community dynamics.     

The influence of climate and topography in patterns of territory establishment in a range‐expanding bird

The Dartford Warbler Sylvia undata has recently expanded its range northwards and upwards in the UK, consistent with the hypothesis that this cold‐sensitive species has responded to a warming climate. We interrogated distribution data, collected during four national surveys of this species between 1974 and 2006, to assess whether this large‐scale range expansion has been accompanied by finer‐scale changes in topographic characteristics of breeding locations. Within sites occupied in successive surveys, there was some evidence of limited altitudinal expansion between surveys. Within wider landscapes occupied in successive surveys, the preceding winter climate tended to be harsher at newly colonized sites than at sites that had already been occupied in the previous survey, while territories in newly colonized sites also tended to be on steeper slopes, especially if at higher altitude, and (in 1994 only) to be more south‐facing. Territories in sites that had already been occupied in the previous survey tended to be lower altitude, less steep and more north‐facing than territories in newly colonized landscapes. In 2006 only, the winter climate was significantly milder in newly colonized landscapes than in already occupied sites. The combined effects of a changing climate and topography may have influenced the pattern of in‐filling in the existing range, while colonization of distant areas, especially more latterly, may have been facilitated by a combination of increased dispersal pressure from the existing range and warming of climate which made higher altitude habitat in the new areas more suitable for occupancy. Careful consideration needs to be given to the importance of fine‐scale topographical variation in determining species’ responses to climate change in order to underpin robust adaptation strategies.     

Optimal surveillance strategy for invasive species management when surveys stop after detection

Invasive species are a cause for concern in natural and economic systems and require both monitoring and management. There is a trade‐off between the amount of resources spent on surveying for the species and conducting early management of occupied sites, and the resources that are ultimately spent in delayed management at sites where the species was present but undetected. Previous work addressed this optimal resource allocation problem assuming that surveys continue despite detection until the initially planned survey effort is consumed. However, a more realistic scenario is often that surveys stop after detection (i.e., follow a “removal” sampling design) and then management begins. Such an approach will indicate a different optimal survey design and can be expected to be more efficient. We analyze this case and compare the expected efficiency of invasive species management programs under both survey methods. We also evaluate the impact of mis‐specifying the type of sampling approach during the program design phase. We derive analytical expressions that optimize resource allocation between monitoring and management in surveillance programs when surveys stop after detection. We do this under a scenario of unconstrained resources and scenarios where survey budget is constrained. The efficiency of surveillance programs is greater if a “removal survey” design is used, with larger gains obtained when savings from early detection are high, occupancy is high, and survey costs are not much lower than early management costs at a site. Designing a surveillance program disregarding that surveys stop after detection can result in an efficiency loss. Our results help guide the design of future surveillance programs for invasive species. Addressing program design within a decision‐theoretic framework can lead to a better use of available resources. We show how species prevalence, its detectability, and the benefits derived from early detection can be considered.     

Changes in the avifauna in a high Andean cloud forest in Colombia over a 24‐year period

The upper altitude ecosystems of the Andes are among the most threatened by climate change. Computer models suggest that a large percentage of species in these ecosystems will be at risk of extinction and that avian communities will suffer disruption and impoverishment. Studies in other Andean countries lend some support to these predictions, but there are no quantitative data from Colombia appropriate to test these models. In 1991–1992, we conducted a bird survey in a high Andean cloud forest to gather information about the species present and their abundance. We attempted to replicate this earlier study 24 yr later to detect any changes in the avifauna and determine possible causes for those changes. From June 2015 to May 2016, we made bimonthly trips to the study site and identified all birds detected either visually or by voice along a number of trails. We supplemented our observational data by also capturing birds in mist‐nets. Community species richness and composition as well as the overall abundance of birds changed little from 1991–1992 to 2015–2016, but nearly 30% of bird species changed in abundance. Changes in the presence or abundance of nine or 10 species reflected upward shifts in elevational limits potentially due to climate change. However, most changes in abundance appeared to reflect changes in the vegetation of the study area due to successional changes in forest and subparamo habitats and a large number of relatively recent treefalls of old canopy trees with heavy epiphyte loads and subsequent changes in the understory vegetation. Our results suggest that the effects of climate change on the avifauna in our study area at a high‐altitude site in Colombia are apparently occurring more slowly than predicted by recent computer models, although we conclude that the possible effects of climate change should definitely be considered in future studies. However, single‐site studies such as ours have limitations in documenting elevation shifts; the most conclusive and quantitative evidence for elevational shifts comes from long‐term studies conducted over a wide range of elevations. As such, we recommend establishment of such a monitoring program in Colombia because data obtained from such a program might be important in designing measures to mitigate the effects of climate change and conserve biodiversity.     

An empirical evaluation of the African elephant as a focal species for connectivity planning in East Africa

Aim Large, charismatic and wide‐ranging animals are often employed as focal species for prioritizing landscape linkages in threatened ecosystems (i.e. ‘connectivity conservation’), but there have been few efforts to assess empirically whether focal species co‐occur with other species of conservation interest within potential linkages. We evaluated whether the African elephant (Loxodonta africana), a world‐recognized flagship species, would serve as an appropriate focal species for other large mammals in a potential linkage between two major protected area complexes. Location A 15,400 km² area between the Ruaha and Selous ecosystems in central Tanzania, East Africa. Methods We used walking transects to assess habitat, human activity and co‐occurrence of elephants and 48 other large mammal species (> 1 kg) at 63 sites using animal sign and direct sightings. We repeated a subset of transects to estimate species detectability using occupancy modelling. We used logistic regression and AIC model selection to characterize patterns of elephant occurrence and assessed correlation of elephant presence with richness of large mammals and subgroups. We considered other possible focal species, compared habitat‐based linear regression models of large mammal richness and used circuit theory to examine potential connectivity spatially. Results Elephants were detected in many locations across the potential linkage. Elephant presence was highly positively correlated with the richness of large mammals, as well as ungulates, carnivores, large carnivores and species > 45 kg in body mass (‘megafauna’). Outside of protected areas, both mammal richness and elephant presence were negatively correlated with human population density and distance from water. Only one other potential focal species was more strongly correlated with species richness than elephants, but detectability was highest for elephants. Main conclusions Although African elephants have dispersal abilities that exceed most other terrestrial mammals, conserving elephant movement corridors may effectively preserve habitat and potential landscape linkages for other large mammal species among Tanzanian reserves.     

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.