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.     

Taking the bait: The influence of attractants and microhabitat on detections of fauna by remote‐sensing cameras

Autonomously triggered cameras are a common wildlife survey technique. The use of attractants and surrounding microhabitats is likely to influence detection probabilities and survey outcomes; however, few studies consider these factors. We compared three attractants (peanut butter‐based, tuna‐based and a control) in a Latin square design through a coastal shrubland with high microhabitat variability at Cape Otway, Victoria, Australia (38º50?S, 143º30?E). Deployments involved 36 cameras for four days in each of five years. The percentage cover of each vegetation structural type (low [no or sparse cover], moderate [grass] or high [shrubs]) within 20 m of each camera was calculated and reduced to a single variable using PCA. Dynamic occupancy modelling, with lure type and vegetation structure as covariates of detection probability, found that peanut butter attracted the greatest diversity of species (24 of 35 species, 69%) and yielded the greatest number of detections (50% of 319) when compared with tuna oil (66% and 24%, respectively) and the control (43% and 26%, respectively). Peanut butter attracted more Macropodidae (wallabies) and Muridae (rats and mice); however, vegetation structural variables were the greatest influence on Corvidae/Artamidae (raven/currawong) detections with higher detectability in more open areas. Vegetation structure also influenced Muridae detections. This study reinforces the critical choice of appropriate attractants and camera placement when investigating vertebrate groups and highlights the role of microhabitat in the detection of small mammals and birds. We suggest future large‐scale camera surveys consider different bait types and microhabitats in their designs, to control for any biases and enable future advice on ‘optimal’ methods.     

Are Private Reserves Effective for Jaguar Conservation?

We present the first study of density and apparent survival for a jaguar (Panthera onca) population in northern Mexico using 13 years of camera trap data from 2000 to 2012. We used the Barker robust design model which combines data from closed sampling periods and resight data between these periods to estimate apparent survival and abundance. We identified 467 jaguar pictures that corresponded to 48 jaguar individuals. We included camera type and field technician as covariates for detection probabilities. We used three covariates to evaluate the effect of reserve on jaguar apparent survival: i) private reserve creation ii) later reserve expansions, and iii) cattle ranches’ conservation activities. We found that the use of digital cameras in addition to film cameras increased detection probability by a factor of 6x compared with the use of only film cameras (p = 0.34 ± 0.05 and p = 0.05 ± 0.02 respectively) in the closed period and more than three times in the open period (R = 0.91 ± 0.08 and R = 0.30 ± 0.13 mixed and film cameras respectively). Our availability estimates showed no temporary emigration and a fidelity probability of 1. Despite an increase of apparent survival probability from 0.47 ± 0.15 to 0.56 ± 0.11 after 2007, no single covariate explained the change in these point estimates. Mean jaguar density was 1.87 ± 0.47 jaguars/100 km². We found that 13 years of jaguar population monitoring with our sampling size were not enough for detecting changes in survival or density. Our results provide a baseline for studies evaluating the effectiveness of protected areas and the inclusion of ranch owners in jaguar conservation programs and long-term population viability.     

Designing occupancy surveys and interpreting non‐detection when observations are imperfect

Aim Conservation practitioners use biological surveys to ascertain whether or not a site is occupied by a particular species. Widely used statistical methods estimate the probability that a species will be detected in a survey of an occupied site. However, these estimates of detection probability are alone not sufficient to calculate the probability that a species is present given that it was not detected. The aim of this paper is to demonstrate methods for correctly calculating (1) the probability a species occupies a site given one or more non‐detections, and (2) the number of sequential non‐detections necessary to assert, with a pre‐specified confidence, that a species is absent from a site. Location Occupancy data for a tree frog in eastern Australia serve to illustrate methods that may be applied anywhere species’ occupancy data are used and detection probabilities are < 1. Methods Building on Bayesian expressions for the probability that a site is occupied by a species when it is not detected, and the number of non‐detections necessary to assert absence with a pre‐specified confidence, we estimate occupancy probabilities across tree frog survey locations, drawing on information about where and when the species was detected during surveys. Results We show that the number of sequential non‐detections necessary to assert that a species is absent increases nonlinearly with the prior probability of occupancy, the probability of detection if present, and the desired level of confidence about absence. Main conclusions If used more widely, the Bayesian analytical approaches illustrated here would improve collection and interpretation of biological survey data, providing a coherent way to incorporate detection probability estimates in the design of minimum survey requirements for monitoring, impact assessment and distribution modelling.     

Optimizing surveys for marsh songbirds: does timing matter?

Analysis of data from point counts, a common method for monitoring bird population trends, has evolved to produce estimates of various population parameters (e.g., density, abundance, and occupancy) while simultaneously estimating detection probability. An important consideration when designing studies using point counts is to maximize detection probability while minimizing variation in detection probability both within and between counts. Our objectives were to estimate detection probabilities for three marsh songbirds, including Marsh Wrens (Cistothorus palustris), Swamp Sparrows (Melospiza georgiana), and Yellow‐headed Blackbirds (Xanthocephalus xanthocephalus), as a function of weather covariates and to evaluate temporal variability in detection probability of these three species. We conducted paired, unlimited radius, 10‐min point counts during consecutive morning and evening survey periods for our three focal species at 56 wetlands in Iowa from 20 April to 10 July 2010. Mean detection probabilities ranged from 0.272 (SE = 0.042) for Marsh Wrens to 0.365 (SE = 0.052) for Swamp Sparrows. Time of season was positively correlated with detection probability for Swamp Sparrows, but was negatively correlated with detection probability for Yellow‐headed Blackbirds, suggesting that detection probability increased during the breeding season for Swamp Sparrows and was highest early in the breeding season for Yellow‐headed Blackbirds. Understanding how detection probabilities of marsh songbirds vary throughout the breeding season allows targeted survey efforts that maximize detection probabilities for these species. Furthermore, consistent detection probabilities of marsh songbirds during morning and evening survey periods mean that investigators have more time to conduct surveys for these birds, allowing greater flexibility to increase spatial and temporal replication of surveys that could provide more precise estimates of desired population parameters.     

同域分布红腹锦鸡和红腹角雉在不同空间尺度下的生境分化

生境分化是群落物种缓解种间竞争压力,实现同域稳定共存的重要途径,是群落生态学领域的重要研究内容。同域动物的生境分化是空间尺度依赖的生态过程,从不同空间尺度分层研究物种的生境分化,对于全面了解同域动物的共存模式和机制,以及实现多物种整合保护都具有重要意义。2018年1月至8月,在四川白水河国家级自然保护区对同域分布的红腹锦鸡(Chrysolophus pictus)和红腹角雉(Tragopan temmminckii)进行了野外调查,基于MaxEnt模型和样方法,从宏生境和微生境两个空间尺度对其生境分化进行了研究。结果显示:1)在宏生境尺度,两种雉类的适宜宏生境重叠面积达44.59 km~2,分别占红腹锦鸡和红腹角雉适宜宏生境面积的58.73%和44.3%,表明二者在宏生境尺度上没有发生明显的种间分化;2)微生境尺度是两种雉类生境分化的关键尺度,海拔、坡位、最近水源距离和乔木层盖度4个特征上的显著差异,使二者的微生境发生显著的种间分化;3)虽然在不同空间尺度下具有不同的分化程度和方式,但两种雉类在海拔适应性、人为干扰耐受性以及对水源的依赖性上的差异在两个尺度下表现出了一定的一致性。此外,基于二者生境需求的异同,提出了控制人为干扰、加强宣传教育、维持自然植被多样性和镶嵌格局等针对该区域雉类物种共同保护的建议。     

Toward reliable population density estimates of partially marked populations using spatially explicit mark–resight methods

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Can Camera Traps Monitor Komodo Dragons a Large Ectothermic Predator?

Camera trapping has greatly enhanced population monitoring of often cryptic and low abundance apex carnivores. Effectiveness of passive infrared camera trapping, and ultimately population monitoring, relies on temperature mediated differences between the animal and its ambient environment to ensure good camera detection. In ectothermic predators such as large varanid lizards, this criterion is presumed less certain. Here we evaluated the effectiveness of camera trapping to potentially monitor the population status of the Komodo dragon (Varanus komodoensis), an apex predator, using site occupancy approaches. We compared site-specific estimates of site occupancy and detection derived using camera traps and cage traps at 181 trapping locations established across six sites on four islands within Komodo National Park, Eastern Indonesia. Detection and site occupancy at each site were estimated using eight competing models that considered site-specific variation in occupancy (ψ)and varied detection probabilities (p) according to detection method, site and survey number using a single season site occupancy modelling approach. The most parsimonious model [ψ (site), p (site*survey); ω = 0.74] suggested that site occupancy estimates differed among sites. Detection probability varied as an interaction between site and survey number. Our results indicate that overall camera traps produced similar estimates of detection and site occupancy to cage traps, irrespective of being paired, or unpaired, with cage traps. Whilst one site showed some evidence detection was affected by trapping method detection was too low to produce an accurate occupancy estimate. Overall, as camera trapping is logistically more feasible it may provide, with further validation, an alternative method for evaluating long-term site occupancy patterns in Komodo dragons, and potentially other large reptiles, aiding conservation of this species.     

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.     

Effects of point‐count duration on estimated detection probabilities and occupancy of breeding birds

Increasingly, point‐count data are used to estimate occupancy, the probability that a species is present at a given location; occupancy accounts for imperfect detection, the probability that a species is detected given that it is present. To our knowledge, effects of sampling duration on inferences from models of bird occupancy have not been evaluated. Our objective was to determine whether changing count duration from 5 to 8 min affected inferences about the occupancy of birds sampled in the Chesapeake Bay Lowlands (eastern United States) and the central and western Great Basin (western United States) in 2012 and 2013. We examined the proportion of species (two doves, one cuckoo, two swifts, five hummingbirds, 11 woodpeckers, and 122 passerines) for which estimates of detection probability were ≥ 0.3. For species with single‐season detection probabilities ≥ 0.3, we compared occupancy estimates derived from 5‐ and 8‐min counts. We also compared estimates for three species sampled annually for 5 yr in the central Great Basin. Detection probabilities based on both the 5‐ and 8‐min counts were ≥ 0.3 for 40% ± 3% of the species in an ecosystem. Extending the count duration from 5 to 8 min increased the detection probability to ≥ 0.3 for 5% ± 0.5% of the species. We found no difference in occupancy estimates that were based on 5‐ versus 8‐min counts for species sampled over two or five consecutive years. However, for 97% of species sampled over 2 yr, precision of occupancy estimates that were based on 8‐min counts averaged 12% ± 2% higher than those based on 5‐min counts. We suggest that it may be worthwhile to conduct a pilot season to determine the number of locations and surveys needed to achieve detection probabilities that are sufficiently high to estimate occupancy for species of interest.     

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.     

The effect of attractant lures in camera trapping: a case study of population estimates for the Iberian lynx (Lynx pardinus)

Capture–recapture analysis of camera trap data is a conventional method to estimate the abundance of free-ranging wild felids. Due to notorious low detection rates of felids, it is important to increase the detection probability during sampling. In this study, we report the effectiveness of attractants as a tool for improving the efficiency of camera trap sampling in abundance estimation of Iberian lynx. We developed a grid system of camera stations in which stations with and without attractant lures were spatially alternated across known Iberian lynx habitat. Of the ten individuals identified, five were detected at stations with no attractant (blind sets), and nine, at the lured stations. Thirty-eight percent of blind set station’s independent captures and 10?% of lured station’s independent captures resulted in photographs unsuitable for correct individual identification. The total capture probability at lured stations was higher than that obtained at blind set stations. The estimates obtained with blind set cameras underestimated the number of lynxes compared to lured cameras. In our study, it appears that the use of lures increased the efficiency of trail camera captures and, therefore, the accuracy of capture–recapture analysis. The observed failure to detect known individuals at blind set camera stations may violate capture–recapture assumptions and bias abundance estimates.     

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.     

Solar Radiation Determines Site Occupancy of Coexisting Tropical and Temperate Deer Species Introduced to New Zealand Forests

Assemblages of introduced taxa provide an opportunity to understand how abiotic and biotic factors shape habitat use by coexisting species. We tested hypotheses about habitat selection by two deer species recently introduced to New Zealand’s temperate rainforests. We hypothesised that, due to different thermoregulatory abilities, rusa deer (Cervus timorensis; a tropical species) would prefer warmer locations in winter than red deer (Cervus elaphus scoticus; a temperate species). Since adult male rusa deer are aggressive in winter (the rut), we also hypothesised that rusa deer and red deer would not use the same winter locations. Finally, we hypothesised that in summer both species would prefer locations with fertile soils that supported more plant species preferred as food. We used a 250 × 250 m grid of 25 remote cameras to collect images in a 100-ha montane study area over two winters and summers. Plant composition, solar radiation, and soil fertility were also determined for each camera location. Multiseason occupancy models revealed that direct solar radiation was the best predictor of occupancy and detection probabilities for rusa deer in winter. Multistate, multiseason occupancy models provided strong evidence that the detection probability of adult male rusa deer was greater in winter and when other rusa deer were present at a location. Red deer mostly vacated the study area in winter. For the one season that had sufficient camera images of both species (summer 2011) to allow two-species occupancy models to be fitted, the detection probability of rusa deer also increased with solar radiation. Detection probability also varied with plant composition for both deer species. We conclude that habitat use by coexisting tropical and temperate deer species in New Zealand likely depends on the interplay between the thermoregulatory and behavioural traits of the deer and the abiotic and biotic features of the habitat.     

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.     

An improved survey method for monitoring population trends of Golden‐winged Warblers and other patchily distributed birds

Conventional surveys designed to monitor common and widespread species may fail to adequately track population changes of rare or patchily distributed species that are often of high conservation concern. We evaluated the performance of a new monitoring approach that employs both a spatially balanced sampling design and a targeted survey protocol designed to estimate population trends of one such patchily distributed species, the Golden‐winged Warbler (Vermivora chrysoptera), in the Appalachian Mountains Bird Conservation Region (BCR 28), USA. Our spatially balanced survey consisted of 105 sample quads (one‐quarter Delorme Atlas pages) across the current range of Golden‐winged Warblers within BCR 28, each with five sample points located in early successional habitat. From 2009 to 2013, collaborators visited each sample point once per year during the peak breeding season and conducted a 17‐min survey consisting of passive observation and playback of conspecific songs and mobbing vocalizations. We used multi‐season, single‐species occupancy models to estimate probability of quad occupancy, detection probability, and occupancy dynamics for Golden‐winged Warblers and closely related Blue‐winged Warblers (Vermivora cyanoptera). Our survey protocol resulted in high estimates of detection probability for Golden‐winged (92%) and Blue‐winged (79%) warblers, with 47% and 56% of quads estimated to be initially occupied, respectively. Derived population trend estimates (λ) indicated an average decline in population of 6% for Golden‐winged Warblers and 7% for Blue‐winged Warblers, resulting in estimated 21% and 22% declines, respectively, in quad occupancy after 5 yr. Our results demonstrate that coupling a spatially balanced survey design in appropriate habitat with a playback protocol to increase detection rates is a viable strategy for tracking populations of Golden‐winged Warblers in the Appalachian Mountains BCR. Similar survey methods should be considered for other rare, declining, or patchily distributed bird species that require targeted monitoring.     

Habitat use of the endangered golden‐rumped sengi Rhynchocyon chrysopygus

The endangered golden‐rumped sengi are found only in Arabuko‐Sokoke Forest with 395.4 km² of forest habitat, and perhaps in a few isolated forest and thicket fragments of total area less than 30 km² all within central coastal Kenya. Understanding its habitat use is an important requirement to develop better conservation measures for the species and its remaining forest habitat. A more reliable method for monitoring its status is also needed. We used the Bayesian occupancy modelling with camera trap data and habitat mapping to characterise the species habitat use in the Arabuko‐Sokoke Forest. The species uses 328 km² (95% CI: 289–364 km²) of Arabuko‐Sokoke Forest habitat, and its site use increases with distance from forest edge, with the highest site use in the Cynometra thicket (0.93; 95% CI: 0.82–1). Its use of the mixed forest habitat has been significantly reduced following years of logging of Afzelia quanzensis. We recommend the use of modelled occupancy, interpreted as the proportion of area used by the species, to monitor the species status. Occupancy models account for detection probability, and heterogeneity in site use and detection can be incorporated. Estimated territory sizes can be combined to obtain abundance estimates.     

Modeling stream fish distributions using interval‐censored detection times

Controlling for imperfect detection is important for developing species distribution models (SDMs). Occupancy‐detection models based on the time needed to detect a species can be used to address this problem, but this is hindered when times to detection are not known precisely. Here, we extend the time‐to‐detection model to deal with detections recorded in time intervals and illustrate the method using a case study on stream fish distribution modeling. We collected electrofishing samples of six fish species across a Mediterranean watershed in Northeast Portugal. Based on a Bayesian hierarchical framework, we modeled the probability of water presence in stream channels, and the probability of species occupancy conditional on water presence, in relation to environmental and spatial variables. We also modeled time‐to‐first detection conditional on occupancy in relation to local factors, using modified interval‐censored exponential survival models. Posterior distributions of occupancy probabilities derived from the models were used to produce species distribution maps. Simulations indicated that the modified time‐to‐detection model provided unbiased parameter estimates despite interval‐censoring. There was a tendency for spatial variation in detection rates to be primarily influenced by depth and, to a lesser extent, stream width. Species occupancies were consistently affected by stream order, elevation, and annual precipitation. Bayesian P‐values and AUCs indicated that all models had adequate fit and high discrimination ability, respectively. Mapping of predicted occupancy probabilities showed widespread distribution by most species, but uncertainty was generally higher in tributaries and upper reaches. The interval‐censored time‐to‐detection model provides a practical solution to model occupancy‐detection when detections are recorded in time intervals. This modeling framework is useful for developing SDMs while controlling for variation in detection rates, as it uses simple data that can be readily collected by field ecologists.     

The importance of incorporating imperfect detection in biodiversity assessments: a case study of small mammals in an Australian region

Aim Assessments of biodiversity are an essential requirement of conservation management planning. Species distributional modelling is a popular approach to quantifying biodiversity whereby occurrence data are related to environmental covariates. An important confounding factor that is often overlooked in the development of such models is uncertainty due to imperfect detection. Here, I demonstrate how an analytical approach that accounts for the bias due to imperfect detection can be applied retrospectively to an existing biodiversity survey data set to produce more realistic estimates of species distributions and unbiased covariate relationships. Location Pilbara biogeographic region, Australia. Methods As a component of the Pilbara survey, presence/absence (i.e. undetected) data on small ground‐dwelling mammals were collected. I applied a multiseason occupancy modelling approach to six of the most common species encountered during this survey. Detection and occupancy rates, as well as extinction and colonization probabilities, were determined, and the influence of covariates on these parameters was examined using the multi‐model inference approach. Results Detection probabilities for all six species were considerably lower than 1.0 and varied across time and species. Naïve estimates of occupancy underestimated occupancy rates corrected for species detectability by up to 45%. Seasonal variation in occupancy status was attributed to changes in detection for two of the focal species, while reproductive events explained variation in occupancy in three others. Covariates describing the substrate strongly influenced site occupancy for most of the species modelled. A comparison of the occupancy model with a generalized linear model, assuming perfect detection, showed that the effects of the covariates were underestimated in the latter model. Main conclusions The application of the multiseason occupancy modelling approach to the Pilbara mammal data set demonstrated a powerful framework for examining changes in site occupancy, as well as local colonization and extinction rates of species which are not confounded by variable species detection rates.     

Occupancy and detectability of Snowy Plovers in western Utah: an application to a low density population

Occupancy modeling can be used to identify habitat characteristics associated with species occurrence. Additionally, occupancy sampling can provide measures of detection probability, increasing confidence in monitoring efforts. Little is known about the distribution and habitat preferences of a small population of Snowy Plovers (Charadrius nivosus) in western Utah. We conducted a study to estimate occupancy and detection probability of Snowy Plovers in western Utah during 2011 and 2012. We made repeated visits to randomly selected survey plots during the breeding period, sampling 84 64‐ha plots in 2011 and 100 64‐ha plots in 2012 and recording the number of adults and habitat characteristics in each plot. We then modeled the relationship between detection, occupancy, and covariates that included distance to water, distance to roads, land cover types, and characteristics of the vegetation. We also included covariates for observer, Julian date, temperature, cloud cover, and wind speed when modeling detection probability. Detection probability was high (0.74, 95% CI = 0.57–0.86) and positively influenced by temperature. Occupancy of 64‐ha plots was low (0.27, 95% CI = 0.18–0.39) and did not vary by year. Occupancy of Snowy Plovers was negatively associated with distance to water (β = ?0.62 ± 0.31, 95% CI = ?1.23 to ?0.01) and percent shrub cover (β = ?0.28 ± 0.02, 95% CI = ?0.58 to ?0.01). Land cover types also influenced plot occupancy. Management actions that conserve shallow water and adjacent habitats or minimize disturbance in these areas are likely to have conservation benefits for Snowy Plovers where water is scarce. Because our detection probabilities were high, investigators involved in future monitoring efforts can achieve reasonable precision with limited revisits to sample plots.