Estimating species richness: calibrating a large avian monitoring programme

Survey effort and targeted landbird community metrics at Indiana lowland forest restoration sites

Many sampling and analytical methods can estimate the abundance, distributions, and diversity of birds and other wildlife. However, challenges with sample size and analytical capacity can make these methods difficult to implement for resource-limited monitoring programs. To apprise efficient and attainable sampling designs for landbird monitoring programs with limited observational data, we used breeding season bird point survey data collected in 2016 at four forest restoration sites in Indiana, USA. We evaluated three subsets of observed species richness, total possibly breeding landbirds, Partners in Flight Regional Conservation Concern (PIF RCC) landbirds, and interior forest specialists (IFSs). Simulated surveys based on field data were used to conduct Bayesian Michaelis-Menten curve analyses estimating observed species as a function of sampling effort. On comparing simulated survey sets with multiple habitat types versus those with one habitat, we found that those with multiple habitat types had estimated 39%-83% greater observed PIF RCC species richness and required 41%-55% fewer visits per point to observe an equivalent proportion of PIF RCC species. Even with multiple habitats in a survey set, the number of visits per point required to detect 50% of observable species was 30% higher for PIF RCC species than for total breeding landbird species. Low detection rates of IFS species at two field sites made precise estimation of required effort to observe these species difficult. However, qualitatively, we found that only sites containing mature forest fragments had detections of several bird species designated as high-confidence IFS species. Our results suggest that deriving specialized species diversity metrics from point survey data can add value when interpreting those data. Additionally, designing studies to collect these metrics may require explicitly planning to visit multiple habitat types at a monitoring site and increasing the number of visits per survey point. Integr Environ Assess Manag 2024;20:1954-1968. © 2024 The Author(s). Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Comparing avian species richness estimates from structured and semi-structured citizen science data

Citizen science, including structured and semi-structured forms, has become a powerful tool to collect biodiversity data. However, semi-structured citizen science data have been criticized for higher variability in quality, including less information to adjust for imperfect detection and uneven duration that bias the estimates of species richness. Species richness estimators may quantify bias in estimates. Here, we test the effectiveness of Chao1 estimator in eBird (semi-structured) by comparing it to averaged species richness in Breeding Bird Survey Taiwan, BBS (structured) and quantifying bias. We then fit a power function to compare bias while controlling for differences in count duration. The Chao1 estimator increased the species richness estimates of eBird data from 56 to 69% of the average observed BBS and from 47 to 59% of the average estimated BBS. Effects of incomplete short duration samples and variability in detectability skills of observers can lead to biased estimates. Using the Chao1 estimator improved estimates of species richness from semi-structured and structured data, but the strong effect of singleton species on bias, especially in short duration counts, should be evaluated in advance to reduce the uncertainty of estimation processes.

Functional diversity of Himalayan bat communities declines at high elevation without the loss of phylogenetic diversity

Species richness exhibits well-known patterns across elevational gradients in various taxa, but represents only one aspect of quantifying biodiversity patterns. Functional and phylogenetic diversity have received much less attention, particularly for vertebrate taxa. There is still a limited understanding of how functional, phylogenetic and taxonomic diversity change in concert across large gradients of elevation. Here, we focused on the Himalaya-representing the largest elevational gradients in the world-to investigate the patterns of taxonomic, functional and phylogenetic diversity in a bat assemblage. Combining field data on species occurrence, relative abundance, and functional traits with measures of phylogenetic diversity, we found that bat species richness and functional diversity declined at high elevation but phylogenetic diversity remained unchanged. At the lowest elevation, we observed low functional dispersion despite high species and functional richness, suggesting a niche packing mechanism. The decline in functional richness, dispersion, and divergence at the highest elevation is consistent with patterns observed due to environmental filtering. These patterns are driven by the absence of rhinolophid bats, four congeners with extreme trait values. Our data, some of the first on mammals from the Himalayan region, suggest that in bat assemblages with relatively high species diversity, phylogenetic diversity may not be a substitute to measure functional diversity.

Can dynamic occupancy models improve predictions of species' range dynamics? A test using Swiss birds

Predictions of species' current and future ranges are needed to effectively manage species under environmental change. Species ranges are typically estimated using correlative species distribution models (SDMs), which have been criticized for their static nature. In contrast, dynamic occupancy models (DOMs) explicitily describe temporal changes in species' occupancy via colonization and local extinction probabilities, estimated from time series of occurrence data. Yet, tests of whether these models improve predictive accuracy under current or future conditions are rare. Using a long-term data set on 69 Swiss birds, we tested whether DOMs improve the predictions of distribution changes over time compared to SDMs. We evaluated the accuracy of spatial predictions and their ability to detect population trends. We also explored how predictions differed when we accounted for imperfect detection and parameterized models using calibration data sets of different time series lengths. All model types had high spatial predictive performance when assessed across all sites (mean AUC > 0.8), with flexible machine learning SDM algorithms outperforming parametric static and DOMs. However, none of the models performed well at identifying sites where range changes are likely to occur. In terms of estimating population trends, DOMs performed best, particularly for species with strong population changes and when fit with sufficient data, while static SDMs performed very poorly. Overall, our study highlights the importance of considering what aspects of performance matter most when selecting a modelling method for a particular application and the need for further research to improve model utility. While DOMs show promise for capturing range dynamics and inferring population trends when fitted with sufficient data, computational constraints on variable selection and model fitting can lead to reduced spatial accuracy of predictions, an area warranting more attention.

Using species traits to predict detectability of animals on aerial surveys

In animal surveys, detectability can vary widely across species. We hypothesized that detectability of animals should be a function of species traits such as mass, color, and mean herd size. We also hypothesized that models of detectability based on species traits can be used to predict detectability for new species not in the original data set, leading to substantial benefits for ecology and conservation. We tested these hypotheses with double-observer aerial surveys of 10 mammal species in northern Botswana. We combined all 10 species and modeled their detectability with species traits (mass, mean herd size, color) as predictors while controlling for observer effects, vegetation, and herd size. We found support for effects of mass and an interaction between herd size and mean herd size on detectability. This model accurately predicted the ratio of herds detected by two observers vs. one observer for 8 of 10 species. To test whether a model based on species traits could be applied to a new species, we serially deleted each species from the data set, fit a trait-based model to the remaining nine species, and used this model to predict detectability for the deleted species. The model was able to reproduce the species-trait model for seven species and accurately predicted the ratio of detections by one or two observers for a different set of seven species; the model was successful by both measures for five species. To our knowledge, this represents the first time that a mechanistic model for detectability of animals has been used to predict detectability for new species. Prediction failed for species with extreme values of traits, suggesting that predicting detectability is not possible near or beyond the boundaries of one's data set. The approach taken in this paper can potentially be used with a variety of taxa and may provide new opportunities to apply detectability corrections where they have not been possible before.

Drivers of Bird Species Richness within Moist High-Altitude Grasslands in Eastern South Africa

Moist high-altitude grasslands in South Africa are renowned for high avifaunal diversity and are priority areas for conservation. Conservation management of these areas conflicts with management for other uses, such as intensive livestock agriculture, which requires annual burning and leads to heavy grazing. Recently the area has become target for water storage schemes and renewable electricity energy projects. There is therefore an urgent need to investigate environmental factors and habitat factors that affect bird species richness in order to optimise management of those areas set aside for conservation. A particularly good opportunity to study these issues arose at Ingula in the eastern South African high-altitude grasslands. An area that had been subject to intense grazing was bought by the national power utility that constructed a pumped storage scheme on part of the land and set aside the rest for bird conservation. Since the new management took over in 2005 the area has been mostly annually burned with relatively little grazing. The new management seeks scientific advice on how to maintain avian species richness of the study area. We collected bird occurrence and vegetation data along random transects between 2006 and 2010 to monitor the impact of the new management, and to study the effect of the habitat changes on bird species richness. To achieve these, we convert bird transect data to presence only data to investigate how bird species richness were related to key transect vegetation attributes under this new grassland management. First we used generalised linear mixed models, to examine changes in vegetation grass height and cover and between burned and unburned habitats. Secondly, we examined how total bird species richness varied across seasons and years. And finally we investigated which habitat vegetation attributes were correlated with species richness of a group of grassland depended bird species only. Transects that were burned showed a larger decrease in vegetation cover compared to transects that were not burned. Grass height increased over time. Bird species richness was highest in summer compared to other seasons and increased over time. Overall bird species richness increased over the three summer surveys but species richness of birds that prefer heavily grazed habitat showed little change over the three years. Changes in bird species richness were best explained by the model with grass height for combined species richness of grassland depended birds but also for birds that prefer heavy grazing when treated alone. On one hand birds that prefer moderate grazing were best explained by a null model. However, overall bird species richness was better positively correlated to grass height than grass cover or dead grass. We conclude that frequent burning alone with relatively reduced grazing led to higher but less dense grass, which benefited some species and disadvantaged others. We suggest that management of this grassland use combination of fire and grazing and leave some areas unburned to accommodates birds of various habitat needs.

Evaluating species richness: Biased ecological inference results from spatial heterogeneity in detection probabilities

Accurate estimates of species richness are necessary to test predictions of ecological theory and evaluate biodiversity for conservation purposes. However, species richness is difficult to measure in the field because some species will almost always be overlooked due to their cryptic nature or the observer's failure to perceive their cues. Common measures of species richness that assume consistent observability across species are inviting because they may require only single counts of species at survey sites. Single-visit estimation methods ignore spatial and temporal variation in species detection probabilities related to survey or site conditions that may confound estimates of species richness. We used simulated and empirical data to evaluate the bias and precision of raw species counts, the limiting forms of jackknife and Chao estimators, and multispecies occupancy models when estimating species richness to evaluate whether the choice of estimator can affect inferences about the relationships between environmental conditions and community size under variable detection processes. Four simulated scenarios with realistic and variable detection processes were considered. Results of simulations indicated that (1) raw species counts were always biased low, (2) single-visit jackknife and Chao estimators were significantly biased regardless of detection process, (3) multispecies occupancy models were more precise and generally less biased than the jackknife and Chao estimators, and (4) spatial heterogeneity resulting from the effects of a site covariate on species detection probabilities had significant impacts on the inferred relationships between species richness and a spatially explicit environmental condition. For a real data set of bird observations in northwestern Alaska, USA, the four estimation methods produced different estimates of local species richness, which severely affected inferences about the effects of shrubs on local avian richness. Overall, our results indicate that neglecting the effects of site covariates on species detection probabilities may lead to significant bias in estimation of species richness, as well as the inferred relationships between community size and environmental covariates.

Effects of growth rate, size, and light availability on tree survival across life stages: a demographic analysis accounting for missing values and small sample sizes

Background

Plant survival is a key factor in forest dynamics and survival probabilities often vary across life stages. Studies specifically aimed at assessing tree survival are unusual and so data initially designed for other purposes often need to be used; such data are more likely to contain errors than data collected for this specific purpose.

Results

We investigate the survival rates of ten tree species in a dataset designed to monitor growth rates. As some individuals were not included in the census at some time points we use capture-mark-recapture methods both to allow us to account for missing individuals, and to estimate relocation probabilities. Growth rates, size, and light availability were included as covariates in the model predicting survival rates. The study demonstrates that tree mortality is best described as constant between years and size-dependent at early life stages and size independent at later life stages for most species of UK hardwood. We have demonstrated that even with a twenty-year dataset it is possible to discern variability both between individuals and between species.

Conclusions

Our work illustrates the potential utility of the method applied here for calculating plant population dynamics parameters in time replicated datasets with small sample sizes and missing individuals without any loss of sample size, and including explanatory covariates.

Electronic supplementary material

The online version of this article (doi:10.1186/s12898-015-0038-8) contains supplementary material, which is available to authorized users.

Optimizing passive acoustic sampling of bats in forests

Passive acoustic methods are increasingly used in biodiversity research and monitoring programs because they are cost-effective and permit the collection of large datasets. However, the accuracy of the results depends on the bioacoustic characteristics of the focal taxa and their habitat use. In particular, this applies to bats which exhibit distinct activity patterns in three-dimensionally structured habitats such as forests. We assessed the performance of 21 acoustic sampling schemes with three temporal sampling patterns and seven sampling designs. Acoustic sampling was performed in 32 forest plots, each containing three microhabitats: forest ground, canopy, and forest gap. We compared bat activity, species richness, and sampling effort using species accumulation curves fitted with the clench equation. In addition, we estimated the sampling costs to undertake the best sampling schemes. We recorded a total of 145,433 echolocation call sequences of 16 bat species. Our results indicated that to generate the best outcome, it was necessary to sample all three microhabitats of a given forest location simultaneously throughout the entire night. Sampling only the forest gaps and the forest ground simultaneously was the second best choice and proved to be a viable alternative when the number of available detectors is limited. When assessing bat species richness at the 1-km² scale, the implementation of these sampling schemes at three to four forest locations yielded highest labor cost-benefit ratios but increasing equipment costs. Our study illustrates that multiple passive acoustic sampling schemes require testing based on the target taxa and habitat complexity and should be performed with reference to cost-benefit ratios. Choosing a standardized and replicated sampling scheme is particularly important to optimize the level of precision in inventories, especially when rare or elusive species are expected.

Estimating unbiased phenological trends by adapting site-occupancy models

As a response to climate warming, many animals and plants have been found to shift phenologies, such as appearance in spring or timing of reproduction. However, traditional measures for shifts in phenology that are based on observational data likely are biased due to a large influence of population size, observational effort, starting date of a survey, or other causes that may affect the probability of detecting a species. Understanding phenological responses of species to climate change, however, requires a robust measure that could be compared among studies and study years. Here, we developed a new method for estimating arrival and departure dates based on site-occupancy models. Using simulated data, we show that our method provided virtually unbiased estimates of phenological events even if detection probability or the number of sites occupied by the species is changing over time. To illustrate the flexibility of our method, we analyzed spring arrival of two long-distance migrant songbirds and the length of the flight period of two butterfly species, using data from a long-term biodiversity monitoring program in Switzerland. In contrast to many birds that migrate short distances, the two long-distance migrant songbirds tended to postpone average spring arrival by -0.5 days per year between 1995 and 2012. Furthermore, the flight period of the short-distance-flying butterfly species apparently became even shorter over the study period, while the flight period of the longer-distance-flying butterfly species remained relatively stable. Our method could be applied to temporally and spatially extensive data from a wide range of monitoring programs and citizen science projects, to help unravel how species and communities respond to global warming.

Selection of multiple umbrella species for functional and taxonomic diversity to represent urban biodiversity

Surrogates, such as umbrella species, are commonly used to reduce the complexity of quantifying biodiversity for conservation purposes. The presence of umbrella species is often indicative of high taxonomic diversity; however, functional diversity is now recognized as an important metric for biodiversity and thus should be considered when choosing umbrella species. We identified umbrella species associated with high taxonomic and functional biodiversity in urban areas in Switzerland. We analyzed 39,752 individuals of 574 animal species from 96 study plots and 1397 presences of 262 plant species from 58 plots. Thirty-one biodiversity measures of 7 taxonomic groups (plants, spiders, bees, ground beetles, lady bugs, weevils and birds) were included in within- and across-taxa analyses. Sixteen measures were taxonomical (species richness and species diversity), whereas 15 were functional (species traits including mobility, resource use, and reproduction). We used indicator value analysis to identify umbrella species associated with single or multiple biodiversity measures. Many umbrella species were indicators of high biodiversity within their own taxonomic group (from 33.3% in weevils to 93.8% in birds), to a lesser extent they were indicators across taxa. Principal component analysis revealed that umbrella species for multiple measures of biodiversity represented different aspects of biodiversity, especially with respect to measures of taxonomic and functional diversity. Thus, even umbrella species for multiple measures of biodiversity were complementary in the biodiversity aspects they represented. Thus, the choice of umbrella species based solely on taxonomic diversity is questionable and may not represent biodiversity comprehensively. Our results suggest that, depending on conservation priorities, managers should choose multiple and complementary umbrella species to assess the state of biodiversity.

Can temperature explain the latitudinal gradient of ulcerative colitis? Cohort of Norway

Background

Incidence and prevalence of ulcerative colitis follow a north–south (latitudinal) gradient and increases northwards at the northern hemisphere or southwards at the southern hemisphere. The disease has increased during the last decades. The temporal trend has been explained by the hygiene hypothesis, but few parallel explanations exist for the spatial variability. Many factors are linked to latitude such as climate. Our purpose was to investigate the association between variables governing the climate and prospectively identified patients.

Methods

In this study, we used a subset of the population-based Cohort of Norway (n = 80412) where 370 prevalent cases of ulcerative colitis were identified through self-reported medication. The meteorological and climatic variables temperature, precipitation, and altitude were recorded from weather stations of the Norwegian Meteorological Institute. Summer temperature was used to capture environmental temperature.

Results

Summer temperature was significantly related to the prevalence of ulcerative colitis. For each one-degree increase in temperature the odds for ulcerative colitis decreased with about 9% (95% CI: 3%-15%). None of the other climatic factors were significantly associated to the risk of ulcerative colitis. Contextual variables did not change the association to the prevalence of ulcerative colitis.

Conclusions

The present results show that the prevalence of ulcerative colitis is associated to summer temperature. Our speculation is that summer temperature works as an instrumental variable for the effect of microbial species richness on the development of ulcerative colitis. Environmental temperature is one of the main forces governing microbial species richness and the microbial composition of the commensal gut flora is known to be an important part in the process leading to ulcerative colitis.

Site-occupancy distribution modeling to correct population-trend estimates derived from opportunistic observations

Species' assessments must frequently be derived from opportunistic observations made by volunteers (i.e., citizen scientists). Interpretation of the resulting data to estimate population trends is plagued with problems, including teasing apart genuine population trends from variations in observation effort. We devised a way to correct for annual variation in effort when estimating trends in occupancy (species distribution) from faunal or floral databases of opportunistic observations. First, for all surveyed sites, detection histories (i.e., strings of detection-nondetection records) are generated. Within-season replicate surveys provide information on the detectability of an occupied site. Detectability directly represents observation effort; hence, estimating detectability means correcting for observation effort. Second, site-occupancy models are applied directly to the detection-history data set (i.e., without aggregation by site and year) to estimate detectability and species distribution (occupancy, i.e., the true proportion of sites where a species occurs). Site-occupancy models also provide unbiased estimators of components of distributional change (i.e., colonization and extinction rates). We illustrate our method with data from a large citizen-science project in Switzerland in which field ornithologists record opportunistic observations. We analyzed data collected on four species: the widespread Kingfisher (Alcedo atthis) and Sparrowhawk (Accipiter nisus) and the scarce Rock Thrush (Monticola saxatilis) and Wallcreeper (Tichodroma muraria). Our method requires that all observed species are recorded. Detectability was <1 and varied over the years. Simulations suggested some robustness, but we advocate recording complete species lists (checklists), rather than recording individual records of single species. The representation of observation effort with its effect on detectability provides a solution to the problem of differences in effort encountered when extracting trend information from haphazard observations. We expect our method is widely applicable for global biodiversity monitoring and modeling of species distributions.

Modeling the effects of environmental disturbance on wildlife communities: avian responses to prescribed fire

Prescribed fire is a management tool used to reduce fuel loads on public lands in forested areas in the western United States. Identifying the impacts of prescribed fire on bird communities in ponderosa pine (Pinus ponderosa) forests is necessary for providing land management agencies with information regarding the effects of fuel reduction on sensitive, threatened, and migratory bird species. Recent developments in occupancy modeling have established a framework for quantifying the impacts of management practices on wildlife community dynamics. We describe a Bayesian hierarchical model of multi-species occupancy accounting for detection probability, and we demonstrate the model's usefulness for identifying effects of habitat disturbances on wildlife communities. Advantages to using the model include the ability to estimate the effects of environmental impacts on rare or elusive species, the intuitive nature of the modeling, the incorporation of detection probability, the estimation of parameter uncertainty, the flexibility of the model to suit a variety of experimental designs, and the composite estimate of the response that applies to the collection of observed species as opposed to merely a small subset of common species. Our modeling of the impacts of prescribed fire on avian communities in a ponderosa pine forest in Washington indicate that prescribed fire treatments result in increased occupancy rates for several bark-insectivore, cavity-nesting species including a management species of interest, Black-backed Woodpeckers (Picoides arcticus). Three aerial insectivore species, and the ground insectivore, American Robin (Turdus migratorius), also responded positively to prescribed fire, whereas three foliage insectivores and two seed specialists, Clark's Nutcracker (Nucifraga columbiana) and the Pine Siskin (Carduelis pinus), declined following treatments. Land management agencies interested in determining the effects of habitat manipulations on wildlife communities can use these methods to provide guidance for future management activities.

Social information and community dynamics: nontarget effects from simulating social cues for management

Artificially creating social stimuli may be an effective tool for facilitating settlement by rare and/or declining species into suitable habitat. However, the potential consequences for other community members have not been explored and should be considered when evaluating the overall utility of using such management strategies. I report on nontarget, community-wide effects that occurred when manipulating social cues of two competitors that are species of concern in the western United States, the dominant Least Flycatcher (Empidonax minimus) and the subordinate American Redstart (Setophaga ruticilla). The experiment consisted of surveying birds during a pretreatment year, which allows for the control of baseline communities, and a treatment year, in which treatments were applied just prior to settlement by migratory birds. Treatments included broadcasting songs of flycatchers and redstarts and were compared to controls. While the addition of redstart cues did not significantly influence community structure, the addition of flycatcher cues reduced species richness of migratory birds by approximately 30%. This pattern was driven by an absence of local colonizations of small-bodied migrants to sites with added flycatcher cues, rather than by local extinctions occurring from manipulations. The artificial flycatcher stimuli were more responsible for declines in species richness than were changes in actual flycatcher densities. I conclude by identifying some fundamental issues that managers and conservation practitioners should weigh when considering simulating social cues for species conservation prior to implementation.

Effects of sample standardization on mean species detectabilities and estimates of relative differences in species richness among assemblages

Ecological surveys provide the basic information needed to estimate differences in species richness among assemblages. Comparable estimates of the differences in richness between assemblages require equal mean species detectabilities across assemblages. However, mean species detectabilities are often unknown, typically low, and potentially different from one assemblage to another. As a result, inferences regarding differences in species richness among assemblages can be biased. We evaluated how well three methods used to produce comparable estimates of species richness achieved equal mean species detectabilities across diverse assemblages: rarefaction, statistical estimators, and standardization of sampling effort on mean taxonomic similarity among replicate samples (MRS). We used simulated assemblages to mimic a wide range of species-occurrence distributions and species richness to compare the performance of these three methods. Inferences regarding differences in species richness based on rarefaction were highly biased when richness estimates were compared among assemblages with distinctly different species-occurrence distributions. Statistical estimators only marginally reduced this bias. Standardization on MRS yielded the most comparable estimates of differences in species richness. These findings have important implications for our understanding of species-richness patterns, inferences drawn from biological monitoring data, and planning for biodiversity conservation.

A BAYESIAN STATE-SPACE FORMULATION OF DYNAMIC OCCUPANCY MODELS

Species occurrence and its dynamic components, extinction and colonization probabilities, are focal quantities in biogeography and metapopulation biology, and for species conservation assessments. It has been increasingly appreciated that these parameters must be estimated separately from detection probability to avoid the biases induced by non-detection error. Hence, there is now considerable theoretical and practical interest in dynamic occupancy models that contain explicit representations of metapopulation dynamics such as extinction, colonization, and turnover as well as growth rates. We describe a hierarchical parameterization of these models that is analogous to the state-space formulation of models in time series, where the model is represented by two components, one for the partially observable occupancy process and another for the observations conditional on that process. This parameterization naturally allows estimation of all parameters of the conventional approach to occupancy models, but in addition, yields great flexibility and extensibility, e.g., to modeling heterogeneity or latent structure in model parameters. We also highlight the important distinction between population and finite sample inference; the latter yields much more precise estimates for the particular sample at hand. Finite sample estimates can easily be obtained using the state-space representation of the model but are difficult to obtain under the conventional approach of likelihood-based estimation. We use R and WinBUGS to apply the model to two examples. In a standard analysis for the European Crossbill in a large Swiss monitoring program, we fit a model with year-specific parameters. Estimates of the dynamic parameters varied greatly among years, highlighting the irruptive population dynamics of that species. In the second example, we analyze route occupancy of Cerulean Warblers in the North American Breeding Bird Survey (BBS) using a model allowing for site-specific heterogeneity in model parameters. The results indicate relatively low turnover and a stable distribution of Cerulean Warblers which is in contrast to analyses of counts of individuals from the same survey that indicate important declines. This discrepancy illustrates the inertia in occupancy relative to actual abundance. Furthermore, the model reveals a declining patch survival probability, and increasing turnover, toward the edge of the range of the species, which is consistent with metapopulation perspectives on the genesis of range edges. Given detection/non-detection data, dynamic occupancy models as described here have considerable potential for the study of distributions and range dynamics.