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.     

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.     

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.     

Occupancy versus colonization–extinction models for projecting population trends at different spatial scales

Understanding spatiotemporal population trends and their drivers is a key aim in population ecology. We further need to be able to predict how the dynamics and sizes of populations are affected in the long term by changing landscapes and climate. However, predictions of future population trends are sensitive to a range of modeling assumptions. Deadwood‐dependent fungi are an excellent system for testing the performance of different predictive models of sessile species as these species have different rarity and spatial population dynamics, the populations are structured at different spatial scales, and they utilize distinct substrates. We tested how the projected large‐scale occupancies of species with differing landscape‐scale occupancies are affected over the coming century by different modeling assumptions. We compared projections based on occupancy models against colonization–extinction models, conducting the modeling at alternative spatial scales and using fine‐ or coarse‐resolution deadwood data. We also tested effects of key explanatory variables on species occurrence and colonization–extinction dynamics. The hierarchical Bayesian models applied were fitted to an extensive repeated survey of deadwood and fungi at 174 patches. We projected higher occurrence probabilities and more positive trends using the occupancy models compared to the colonization–extinction models, with greater difference for the species with lower occupancy, colonization rate, and colonization:extinction ratio than for the species with higher estimates of these statistics. The magnitude of future increase in occupancy depended strongly on the spatial modeling scale and resource resolution. We encourage using colonization–extinction models over occupancy models, modeling the process at the finest resource‐unit resolution that is utilizable by the species, and conducting projections for the same spatial scale and resource resolution at which the model fitting is conducted. Further, the models applied should include key variables driving the metapopulation dynamics, such as the availability of suitable resource units, habitat quality, and spatial connectivity.     

Land use drivers of species re‐expansion: inferring colonization dynamics in Eurasian otters

Aim Land use intensity has been recognized as one of the major determinants of native species declines. The re‐expansion of species previously constrained by habitat degradation has been rarely investigated. Here, we use site occupancy models incorporating imperfect detection to identify the land use drivers of the re‐expansion of the Eurasian otter (Lutra lutra). Location Czech Republic. Methods We applied multi‐season occupancy models to otter presence–non‐detection data collected in three national surveys (1992, 2000, 2006) at 552 sites (11.2 × 12 km grid cells). Model parameters included site occupancy, colonization and extinction probabilities, and detection probability at a sub‐site level. We modelled changes in occupancy over time as a function of agricultural, urban and industrial land use and change in the extent of agricultural land use. Results Under the best fitting model, occupancy was estimated to be 34.6% in 1992, 51.3% in 2000 and 83.7% in 2006. Detection probability was neither perfect nor constant. Occupancy probability in 1992 was negatively related to land use gradients. Colonization was more likely to occur where a reduction in agricultural land was larger. Variation in extinction and colonization rates along land use gradients resulted in increased occupancy in industrial and especially urban landscapes. Conversely, occupancy remained almost unchanged along agricultural gradients. Main conclusions Dynamics of otter expansion were strongly associated with the two main patterns of the rapid environmental transition that has taken place in the Czech Republic since the early 1990s. Results show that a reduction in intensive agricultural land use led to an increase in otter distribution, providing evidence of the impact of agricultural land use on stream ecosystems. Moreover, otters recolonized urban and industrial landscapes, probably as a result of extensive reduction in water pollution from point sources. Our results suggest that active conservation of otter populations should focus on restoration of freshwater habitat at large scales, especially in agricultural landscapes.     

Occupancy models including local and landscape variables are useful to assess the distribution of a salamander species at risk

Numerous amphibian species are at risk of extinction worldwide. Therefore, reliable estimations of the distribution and abundance of these species are necessary for their conservation. Generally, amphibians are difficult to detect in the wild, which compromises the accuracy of long-term population monitoring and management. Occupancy models are useful tools to assess how environmental variables, at a local and at a landscape scale, affect the distribution and abundance of organisms taking into account species imperfect detectability. In this study, we evaluated with an environmental multiscale approach the seasonal variation of the occupation area of the threatened salamander, Ambystoma ordinarium along its distribution range. We obtained readings in 60 streams of physicochemical variables associated with habitat quality and landscape features. We found that detection and occupation probability of A. ordinarium are seasonally associated with different environmental variables. During the dry season, detectability was positively associated with temperature and stream depth, whereas occupancy was positively associated with the proportion of crops in the landscape and stream elevation. In the rainy season, the detection probability was not explained by any variable considered, and occupancy was negatively associated with stream's electrical conductivity and dissolved oxygen. Based on the estimation of occupied sites, we showed that A. ordinarium presents a more restricted distribution range than previously projected. Therefore, our results reveal the importance of evaluating the accuracy of distribution estimates for the conservation of threatened species as A. ordinarium.     

Evaluating Multi-Level Models to Test Occupancy State Responses of Plethodontid Salamanders

Plethodontid salamanders are diverse and widely distributed taxa and play critical roles in ecosystem processes. Due to salamander use of structurally complex habitats, and because only a portion of a population is available for sampling, evaluation of sampling designs and estimators is critical to provide strong inference about Plethodontid ecology and responses to conservation and management activities. We conducted a simulation study to evaluate the effectiveness of multi-scale and hierarchical single-scale occupancy models in the context of a Before-After Control-Impact (BACI) experimental design with multiple levels of sampling. Also, we fit the hierarchical single-scale model to empirical data collected for Oregon slender and Ensatina salamanders across two years on 66 forest stands in the Cascade Range, Oregon, USA. All models were fit within a Bayesian framework. Estimator precision in both models improved with increasing numbers of primary and secondary sampling units, underscoring the potential gains accrued when adding secondary sampling units. Both models showed evidence of estimator bias at low detection probabilities and low sample sizes; this problem was particularly acute for the multi-scale model. Our results suggested that sufficient sample sizes at both the primary and secondary sampling levels could ameliorate this issue. Empirical data indicated Oregon slender salamander occupancy was associated strongly with the amount of coarse woody debris (posterior mean = 0.74; SD = 0.24); Ensatina occupancy was not associated with amount of coarse woody debris (posterior mean = -0.01; SD = 0.29). Our simulation results indicate that either model is suitable for use in an experimental study of Plethodontid salamanders provided that sample sizes are sufficiently large. However, hierarchical single-scale and multi-scale models describe different processes and estimate different parameters. As a result, we recommend careful consideration of study questions and objectives prior to sampling data and fitting models.     

Host selection and responses to forest fragmentation in acorn weevils: inferences from dynamic occupancy models

Prior studies on species‐specific responses to habitat alteration have demonstrated that niche breadth is positively associated with patch occupancy rates in landscapes fragmented by agriculture. However, these studies generally have focused on vertebrates and relied upon data collected at a single point in time, neglecting dynamic processes that could alter inferences. We studied the effects of host selection and forest fragmentation on population dynamics of acorn weevils Curculio, the primary insect seed predators of oaks in North America. Detection/non‐detection data were collected from 174 red and white oaks in 19 forested fragments from 2005–2008. We used dynamic multi‐season site‐occupancy models within a Bayesian framework to explore variation in patch (tree‐level) occupancy dynamics of three species of weevils that vary in their specialization, i.e. their relative selection of red and white oak as hosts: C. pardalis (white oak specialist), C. sulcatulus (generalist) and C. proboscideus (generalist). Contrary to expectations, the specialist exhibited greater estimated rates of occupancy than generalists. However, red oak trees occupied by the white oak specialist appeared to function as sink populations maintained by frequent colonization following local extinction. Specialists also exhibited greater relative variation in occupancy and relative abundance on their host trees among years. Generalists exhibited lower local extinction and colonization rates than the specialist. Occupancy and vital rates of weevils on a host tree increased with acorn production and were significantly influenced by neighborhood forest density. Our results suggest that across much of their range in the eastern United States acorn weevils exist in fragmented, temporally dynamic landscapes, with generalists occurring on a lower proportion of usable trees but buffered by access to more suitable patches and greater patch‐specific survival. More generally, our results demonstrate that estimates of specialization derived from occupancy data may be misleading in the absence of patch‐specific information on vital rates.     

Non‐native salmonids affect amphibian occupancy at multiple spatial scales

Aim The introduction of non‐native species into aquatic environments has been linked with local extinctions and altered distributions of native species. We investigated the effect of non‐native salmonids on the occupancy of two native amphibians, the long‐toed salamander (Ambystoma macrodactylum) and Columbia spotted frog (Rana luteiventris), across three spatial scales: water bodies, small catchments and large catchments. Location Mountain lakes at ≥ 1500 m elevation were surveyed across the northern Rocky Mountains, USA. Methods We surveyed 2267 water bodies for amphibian occupancy (based on evidence of reproduction) and fish presence between 1986 and 2002 and modelled the probability of amphibian occupancy at each spatial scale in relation to habitat availability and quality and fish presence. Results After accounting for habitat features, we estimated that A. macrodactylum was 2.3 times more likely to breed in fishless water bodies than in water bodies with fish. Ambystoma macrodactylum also was more likely to occupy small catchments where none of the water bodies contained fish than in catchments where at least one water body contained fish. However, the probability of salamander occupancy in small catchments was also influenced by habitat availability (i.e. the number of water bodies within a catchment) and suitability of remaining fishless water bodies. We found no relationship between fish presence and salamander occupancy at the large‐catchment scale, probably because of increased habitat availability. In contrast to A. macrodactylum, we found no relationship between fish presence and R. luteiventris occupancy at any scale. Main conclusions Our results suggest that the negative effects of non‐native salmonids can extend beyond the boundaries of individual water bodies and increase A. macrodactylum extinction risk at landscape scales. We suspect that niche overlap between non‐native fish and A. macrodactylum at higher elevations in the northern Rocky Mountains may lead to extinction in catchments with limited suitable habitat.     

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.     

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.     

Scaling Occupancy Estimates up to Abundance for Wolves

Management of wildlife populations often requires reliable estimates of population size or distribution. Estimating abundance can be logistically difficult, and occupancy models have been used as a less expensive proxy for abundance estimation. Another alternative is to use independent estimates of home-range size and mean group size to directly scale occupancy estimates up to abundance. We used simulations to explore when scaling occupancy up to abundance is reliable, and as an example we applied an occupancy approach to estimate abundance of wolves (Canis lupus) from roadside snow-tracking surveys in northern Wisconsin, USA, in 2016 and 2018. Estimates of wolf abundance were plausible and compared favorably with independent estimates produced by territory mapping, and snow-tracking data requirements were lower than for territory mapping. Simulation results suggested that reasonable abundance estimates could be obtained under some conditions but also that severe positive bias could result under other conditions, especially when populations were small and dispersed, home range size was small, and areal sampling units were large. Positive bias in abundance estimates occurs because of closure assumption violations when tracks from a single wolf or pack are detected in >1 sample unit, and the sum of the sample unit areas where tracks were detected exceed the sum of the home range areas. Bias was minimized when sampling units were small relative to home range size or when sampling units were route segments that approximate point sample units, and when home ranges were highly aggregated. We conclude that, although caution is warranted when scaling occupancy estimates up to abundance, scaled occupancy models can provide feasible and reliable estimates of abundance, assuming home range size and mean group size are accurately known or estimated, sampling units are appropriately chosen, and covariates that aggregate home ranges can be used to accurately predict occupancy probability. © 2021 The Authors. The Journal of Wildlife Management published by Wiley Periodicals LLC on behalf of The Wildlife Society.     

Human disturbance and stage‐specific habitat requirements influence snowy plover site occupancy during the breeding season

Habitat use has important consequences for avian reproductive success and survival. In coastal areas with recreational activity, human disturbance may limit use of otherwise suitable habitat. Snowy plovers Charadrius nivosus have a patchy breeding distribution along the coastal areas on the Florida Panhandle, USA. Our goal was to determine the relative effects of seasonal human disturbance and habitat requirements on snowy plover habitat use. We surveyed 303 sites for snowy plovers, human disturbance, and habitat features between January and July 2009 and 2010. We made multiple visits during three different sampling periods that corresponded to snowy plover breeding: pre‐breeding, incubation, and brood‐rearing and used multi‐season occupancy models to examine whether human disturbance, habitat features, or both influenced site occupancy, colonization (probability of transition from an unoccupied site to an occupied site), and extinction (probability of transition from an occupied site to an unoccupied site). Snowy plover site occupancy and colonization was negatively associated with human disturbance and site extinction was positively associated with human disturbance. Interdune vegetation had a negative effect on occupancy and colonization, indicating that plovers were less likely to use areas with uniform, dense vegetation among dunes. Also, dune shape, beach debris, and access to low‐energy foraging areas influenced site occupancy, colonization, and extinction. Plovers used habitat based on beach characteristics that provided stage‐specific resource needs; however, human disturbance was the strongest predictor of site occupancy. In addition, vegetation plantings used to enhance dune rehabilitation may negatively impact plover site occupancy. Management actions that decrease human disturbance, such as symbolic fencing and signage, may increase the amount of breeding habitat available to snowy plovers on the Florida Panhandle and in other areas with high human activity. The specific areas that require this protection may vary across snowy plover life history stages.     

The relative contribution of local habitat and landscape context to metapopulation processes: a dynamic occupancy modeling approach

Changes in site occupancy across habitat patches have often been attributed to landscape features in fragmented systems, particularly when considering metapopulations. However, failure to include habitat quality of individual patches can mask the relative importance of local scale features in determining distributional changes. We employed dynamic occupancy modeling to compare the strength of local habitat variables and metrics of landscape patterns as drivers of metapopulation dynamics for a vulnerable, high‐elevation species in a naturally fragmented landscape. Repeat surveys of Bicknell's thrush Catharus bicknelli presence/non‐detection were conducted at 88 sites across Vermont, USA in 2006 and 2007. We used an organism‐based approach, such that at each site we measured important local‐scale habitat characteristics and quantified landscape‐scale features using a predictive habitat model for this species. We performed a principal component analysis on both the local and landscape features to reduce dimensionality. We estimated site occupancy, colonization, and extinction probabilities while accounting for imperfect detection. Univariate, additive, and interaction models of local habitat and landscape context were ranked using AICc scores. Both local and landscape scales were important in determining changes in occupancy patterns. An interaction between scales was detected for occupancy dynamics indicating that the relationship of the parameters to local‐scale habitat conditions can change depending on the landscape context and vice versa. An increase in both landscape‐ and local‐scale habitat quality increased occupancy and colonization probability while decreasing extinction risk. Colonization and extinction were both more strongly influenced by local habitat quality relative to landscape patterns. We also identified clear, qualitative thresholds for landscape‐scale features. Conservation of large habitat patches in high‐cover landscapes will help ensure persistence of Bicknell's thrushes, but only if local scale habitat quality is maintained. Our results highlight the importance of incorporating information beyond landscape characteristics when investigating patch occupancy patterns in metapopulations.     

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.     

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.     

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.     

Effect of detection heterogeneity in occupancy‐detection models: an experimental test of time‐to‐first‐detection methods

Imperfect detection can bias estimates of site occupancy in ecological surveys but can be corrected by estimating detection probability. Time‐to‐first‐detection (TTD) occupancy models have been proposed as a cost–effective survey method that allows detection probability to be estimated from single site visits. Nevertheless, few studies have validated the performance of occupancy‐detection models by creating a situation where occupancy is known, and model outputs can be compared with the truth. We tested the performance of TTD occupancy models in the face of detection heterogeneity using an experiment based on standard survey methods to monitor koala Phascolarctos cinereus populations in Australia. Known numbers of koala faecal pellets were placed under trees, and observers, uninformed as to which trees had pellets under them, carried out a TTD survey. We fitted five TTD occupancy models to the survey data, each making different assumptions about detectability, to evaluate how well each estimated the true occupancy status. Relative to the truth, all five models produced strongly biased estimates, overestimating detection probability and underestimating the number of occupied trees. Despite this, goodness‐of‐fit tests indicated that some models fitted the data well, with no evidence of model misfit. Hence, TTD occupancy models that appear to perform well with respect to the available data may be performing poorly. The reason for poor model performance was unaccounted for heterogeneity in detection probability, which is known to bias occupancy‐detection models. This poses a problem because unaccounted for heterogeneity could not be detected using goodness‐of‐fit tests and was only revealed because we knew the experimentally determined outcome. A challenge for occupancy‐detection models is to find ways to identify and mitigate the impacts of unobserved heterogeneity, which could unknowingly bias many models.     

Evaluating the effects of urbanisation on salamander abundances using a before‐after control‐impact design

1. Urbanisation represents a significant threat to semi‐aquatic amphibian populations, especially stream‐dwelling salamanders. Although studies of urbanisation effects on amphibians have been conducted, there is an urgent need to follow populations over longer time periods, account for imperfect detection and determine the response time to urbanisation. Consequently, we used a before‐after control‐impact (BACI) study design to estimate changes in abundances of larval and adult salamanders in streams affected by urbanisation. 2. From 2005 to 2009, we used standard sampling techniques to obtain a count of salamanders in 13 first‐order streams that underwent urbanisation of their catchments after the first year of sampling. Simultaneously, we counted salamanders in 17 streams that experienced no disturbance within stream catchments. Additionally, we measured environmental variables at each stream. 3. We used Royle’s binomial mixture model to estimate annual mean abundances and individual detection probabilities, and Bayesian inference was used to estimate population parameters for each stage and species. 4. Although mean abundance estimates varied among years in control and urbanised streams, we found that urbanisation had a negative effect on larval and adult salamander abundances. Larval salamander abundances at sites 1 year after urbanisation were significantly lower than abundances from control sites. Abundances of adult two‐lined salamanders (Eurycea cirrigera) at urbanised sites were lower than abundances at control sites 2 years post‐urbanisation, and adult dusky salamander (Desmognathus fuscus) abundances at urbanised sites were lower than abundances at control sites 3 years post‐urbanisation. Maximum conductivity, sedimentation level and maximum stream channel width differed between urban and non‐urban streams. 5. Our results suggest that stream‐dwelling salamanders exhibit little resistance to urbanisation. Our study also highlights the use of the BACI design to study how urbanisation affects populations in semi‐aquatic habitats. We emphasise that inferences regarding urbanisation effects on population response may be compromised unless urban populations are compared to populations in control sites, especially for species in which populations fluctuate.     

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.