Rapid,recurring, structured survey versus bioblitz for generating biodiversity data and analysis with a multispecies abundance model

A bioblitz inexpensively and quickly generates biodiversity data, but bioblitzes are often conducted with haphazard, unreplicated sampling. Results tend to be taxonomically, geographically, or temporally biased, lack metadata, and consist of lists of observed taxa that do not enable further analyses or correction for imperfect detection. A rapid, recurring, structured survey (RRSS) uses a structured sampling design and temporal and spatial replication to survey randomly selected sites on a conservation property. We participated in a loosely structured bioblitz and a subsequent RRSS at Big Canoe Creek Nature Preserve in Springville (St. Clair County), Alabama (USA) to compare observed richness derived from the 2 survey approaches. The RRSS data structure enabled us to fit models that accounted for imperfect detection to estimate abundances, occupancy probabilities, and habitat associations. The loosely structured bioblitz data could not be used in such models. We present a new integrated multispecies abundance model that we applied to avian RRSS data. Our model extension enables estimation for the community, employs data augmentation to estimate the number of undetected species, and incorporates covariates. The RRSS generated a more comprehensive and less biased list of observed taxonomic richness than the loosely structured bioblitz (e.g., 73 vs. 45 bird species and 104 vs. 63 insect families from the RRSS vs. loosely structured bioblitz, respectively). Models fit to the RRSS data identified seasonal patterns in avian community composition and allowed for estimation of habitat–occupancy relationships for insect taxa. The RRSS protocol has potential for broad transferability as a standardized, quick, and inexpensive way to inventory biodiversity and estimate ecological parameters while providing an outreach opportunity.     

Spatial Patterns of Breeding Success of Grizzly Bears Derived from Hierarchical Multistate Models

Conservation programs often manage populations indirectly through the landscapes in which they live. Empirically, linking reproductive success with landscape structure and anthropogenic change is a first step in understanding and managing the spatial mechanisms that affect reproduction, but this link is not sufficiently informed by data. Hierarchical multistate occupancy models can forge these links by estimating spatial patterns of reproductive success across landscapes. To illustrate, we surveyed the occurrence of grizzly bears (Ursus arctos) in the Canadian Rocky Mountains Alberta, Canada. We deployed camera traps for 6 weeks at 54 surveys sites in different types of land cover. We used hierarchical multistate occupancy models to estimate probability of detection, grizzly bear occupancy, and probability of reproductive success at each site. Grizzly bear occupancy varied among cover types and was greater in herbaceous alpine ecotones than in low‐elevation wetlands or mid‐elevation conifer forests. The conditional probability of reproductive success given grizzly bear occupancy was 30% (SE = 0.14). Grizzly bears with cubs had a higher probability of detection than grizzly bears without cubs, but sites were correctly classified as being occupied by breeding females 49% of the time based on raw data and thus would have been underestimated by half. Repeated surveys and multistate modeling reduced the probability of misclassifying sites occupied by breeders as unoccupied to <2%. The probability of breeding grizzly bear occupancy varied across the landscape. Those patches with highest probabilities of breeding occupancy—herbaceous alpine ecotones—were small and highly dispersed and are projected to shrink as treelines advance due to climate warming. Understanding spatial correlates in breeding distribution is a key requirement for species conservation in the face of climate change and can help identify priorities for landscape management and protection. Patrones Espaciales del Éxito Reproductivo de Osos Pardos, Derivados de Modelos Jerárquicos Multi‐Estado     

Community occupancy responses of small mammals to restoration treatments in ponderosa pine forests, northern Arizona, USA

In western North American conifer forests, wildfires are increasing in frequency and severity due to heavy fuel loads that have accumulated after a century of fire suppression. Forest restoration treatments (e.g., thinning and/or burning) are being designed and implemented at large spatial and temporal scales in an effort to reduce fire risk and restore forest structure and function. In ponderosa pine (Pinus ponderosa) forests, predominantly open forest structure and a frequent, low-severity fire regime constituted the evolutionary environment for wildlife that persisted for thousands of years. Small mammals are important in forest ecosystems as prey and in affecting primary production and decomposition. During 2006-2009, we trapped eight species of small mammals at 294 sites in northern Arizona and used occupancy modeling to determine community responses to thinning and habitat features. The most important covariates in predicting small mammal occupancy were understory vegetation cover, large snags, and treatment. Our analysis identified two generalist species found at relatively high occupancy rates across all sites, four open-forest species that responded positively to treatment, and two dense-forest species that responded negatively to treatment unless specific habitat features were retained. Our results indicate that all eight small mammal species can benefit from restoration treatments, particularly if aspects of their evolutionary environment (e.g., large trees, snags, woody debris) are restored. The occupancy modeling approach we used resulted in precise species-level estimates of occupancy in response to habitat attributes for a greater number of small mammal species than in other comparable studies. We recommend our approach for other studies faced with high variability and broad spatial and temporal scales in assessing impacts of treatments or habitat alteration on wildlife species. Moreover, since forest planning efforts are increasingly focusing on progressively larger treatment implementation, better and more efficiently obtained ecological information is needed to inform these efforts.     

Estimating population impacts via dynamic occupancy analysis of Before-After Control-Impact studies

Estimating environmental impacts on populations is one of the main goals of wildlife monitoring programs, which are often conducted in conjunction with management actions or following natural disturbances. In this study we investigate the statistical power of dynamic occupancy models to detect changes in local survival and colonization from detection-nondetection data, while accounting for imperfect detection probability, in a Before-After Control-Impact (BACI) framework. We simulated impacts on local survival and/or detection probabilities, and asked questions related to: (1) costs and benefits of different analysis models, (2) confounding changes in detection with changes in local survival, (3) sampling design trade-offs, and (4) species with low vs. high rates of turnover. Estimating seasonal effects on local survival and colonization, as opposed to estimating Before-After effects, had little effect on the power to detect changes in local survival. Estimating a parameter that accounted for pretreatment differences in local survival between Control and Impact sites decreased power by 50%, but it was critical to include when such differences existed. When the experimental treatment had a negative impact on species detectability but analysis assumed constant detection, the Type I error rates were dramatically inflated (0.20 0.33). In general, there was low power (< 0.5) to detect a 50% decrease in local survival for all combinations of sites (N = 50 vs. 100), seasons sampled (8 vs. 12), and visits per site per season (4 vs. 6). Unbalanced designs performed worse than balanced designs, with the exception of the case of treatments being implemented in different seasons at different sites. Adding more control sites improved the ability to detect changes in local survival. Surveying more seasons after impact resulted in modest power gains, but at least three seasons before impact were required to successfully implement BACI occupancy studies. Turnover rates had a low impact on power. Occupancy studies conducted in a BACI design offer the opportunity to detect environmental impacts on wildlife populations without the costs of intensive studies. However, given the low power to detect small changes (20%) in local survival, these studies should be used when researchers are confident that major treatment impacts will occur or very large sample sizes are obtainable.     

Spatially Explicit Power Analyses for Occupancy‐Based Monitoring of Wolverine in the U.S. Rocky Mountains

Conservation scientists and resource managers often have to design monitoring programs for species that are rare or patchily distributed across large landscapes. Such programs are frequently expensive and seldom can be conducted by one entity. It is essential that a prospective power analysis be undertaken to ensure stated monitoring goals are feasible. We developed a spatially based simulation program that accounts for natural history, habitat use, and sampling scheme to investigate the power of monitoring protocols to detect trends in population abundance over time with occupancy‐based methods. We analyzed monitoring schemes with different sampling efforts for wolverine (Gulo gulo) populations in 2 areas of the U.S. Rocky Mountains. The relation between occupancy and abundance was nonlinear and depended on landscape, population size, and movement parameters. With current estimates for population size and detection probability in the northern U.S. Rockies, most sampling schemes were only able to detect large declines in abundance in the simulations (i.e., 50% decline over 10 years). For small populations reestablishing in the Southern Rockies, occupancy‐based methods had enough power to detect population trends only when populations were increasing dramatically (e.g., doubling or tripling in 10 years), regardless of sampling effort. In general, increasing the number of cells sampled or the per‐visit detection probability had a much greater effect on power than the number of visits conducted during a survey. Although our results are specific to wolverines, this approach could easily be adapted to other territorial species. Poder de Análisis Espacialmente Explícito para el Monitoreo Basado en Ocupación del Glotón (Gulo gulo) en las Montañas Rocallosas de Estados Unidos     

Responses of pond-breeding amphibians to wildfire: short-term patterns in occupancy and colonization.

Wildland fires are expected to become more frequent and severe in many ecosystems, potentially posing a threat to many sensitive species. We evaluated the effects of a large, stand-replacement wildfire on three species of pond-breeding amphibians by estimating changes in occupancy of breeding sites during the three years before and after the fire burned 42 of 83 previously surveyed wetlands. Annual occupancy and colonization for each species was estimated using recently developed models that incorporate detection probabilities to provide unbiased parameter estimates. We did not find negative effects of the fire on the occupancy or colonization rates of the long-toed salamander (Ambystoma macrodactylum). Instead, its occupancy was higher across the study area after the fire, possibly in response to a large snowpack that may have facilitated colonization of unoccupied wetlands. Naive data (uncorrected for detection probability) for the Columbia spotted frog (Rana luteiventris) initially led to the conclusion of increased occupancy and colonization in wetlands that burned. After accounting for temporal and spatial variation in detection probabilities, however, it was evident that these parameters were relatively stable in both areas before and after the fire. We found a similar discrepancy between naive and estimated occupancy of A. macrodactylum that resulted from different detection probabilities in burned and control wetlands. The boreal toad (Bufo boreas) was not found breeding in the area prior to the fire but colonized several wetlands the year after they burned. Occupancy by B. boreas then declined during years 2 and 3 following the fire. Our study suggests that the amphibian populations we studied are resistant to wildfire and that B. boreas may experience short-term benefits from wildfire. Our data also illustrate how naive presence-non-detection data can provide misleading results.     

Landscape-level influences of terrestrial snake occupancy within the southeastern United States

Habitat loss and degradation are thought to be the primary drivers of species extirpations, but for many species we have little information regarding specific habitats that influence occupancy. Snakes are of conservation concern throughout North America, but effective management and conservation are hindered by a lack of basic natural history information and the small number of large-scale studies designed to assess general population trends. To address this information gap, we compiled detection/nondetection data for 13 large terrestrial species from 449 traps located across the southeastern United States, and we characterized the land cover surrounding each trap at multiple spatial scales (250-, 500-, and 1000-m buffers). We used occupancy modeling, while accounting for heterogeneity in detection probability, to identify habitat variables that were influential in determining the presence of a particular species. We evaluated 12 competing models for each species, representing various hypotheses pertaining to important habitat features for terrestrial snakes. Overall, considerable interspecific variation existed in important habitat variables and relevant spatial scales. For example, kingsnakes (Lampropeltis getula) were negatively associated with evergreen forests, whereas Louisiana pinesnake (Pituophis ruthveni) occupancy increased with increasing coverage of this forest type. Some species were positively associated with grassland and scrub/shrub (e.g., Slowinski's cornsnake, Elaphe slowinskii) whereas others, (e.g., copperhead, Agkistrodon contortrix, and eastern diamond-backed rattlesnake, Crotalus adamanteus) were positively associated with forested habitats. Although the species that we studied may persist in varied landscapes other than those we identified as important, our data were collected in relatively undeveloped areas. Thus, our findings may be relevant when generating conservation plans or restoration goals. Maintaining or restoring landscapes that are most consistent with the ancestral habitat preferences of terrestrial snake assemblages will require a diverse habitat matrix over large spatial scales.     

Using multilevel spatial models to understand salamander site occupancy patterns after wildfire

Studies of the distribution of elusive forest wildlife have suffered from the confounding of true presence with the uncertainty of detection. Occupancy modeling, which incorporates probabilities of species detection conditional on presence, is an emerging approach for reducing observation bias. However, the current likelihood modeling framework is restrictive for handling unexplained sources of variation in the response that may occur when there are dependence structures such as smaller sampling units that are nested within larger sampling units. We used multilevel Bayesian occupancy modeling to handle dependence structures and to partition sources of variation in occupancy of sites by terrestrial salamanders (family Plethodontidae) within and surrounding an earlier wildfire in western Oregon, USA. Comparison of model fit favored a spatial N-mixture model that accounted for variation in salamander abundance over models that were based on binary detection/non-detection data. Though catch per unit effort was higher in burned areas than unburned, there was strong support that this pattern was due to a higher probability of capture for individuals in burned plots. Within the burn, the odds of capturing an individual given it was present were 2.06 times the odds outside the burn, reflecting reduced complexity of ground cover in the burn. Ther was weak support that true occupancy was lower within the burned area. While the odds of occupancy in the burn were 0.49 times the odds outside the burn among the five species, the magnitude of variation attributed to the burn was small in comparison to variation attributed to other landscape variables and to unexplained, spatially autocorrelated random variation. While ordinary occupancy models may separate the biological pattern of interest from variation in detection probability when all sources of variation are known, the addition of random effects structures for unexplained sources of variation in occupancy and detection probability may often more appropriately represent levels of uncertainty.     

Estimating species occurrence, abundance, and detection probability using zero-inflated distributions

Researchers have developed methods to account for imperfect detection of species with either occupancy (presence absence) or count data using replicated sampling. We show how these approaches can be combined to simultaneously estimate occurrence, abundance, and detection probability by specifying a zero-inflated distribution for abundance. This approach may be particularly appropriate when patterns of occurrence and abundance arise from distinct processes operating at differing spatial or temporal scales. We apply the model to two data sets: (1) previously published data for a species of duck, Anas platyrhynchos, and (2) data for a stream fish species, Etheostoma scotti. We show that in these cases, an incomplete-detection zero-inflated modeling approach yields a superior fit to the data than other models. We propose that zero-inflated abundance models accounting for incomplete detection be considered when replicate count data are available.     

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.     

Efficient species-level monitoring at the landscape scale

Monitoring the population trends of multiple animal species at a landscape scale is prohibitively expensive. However, advances in survey design, statistical methods, and the ability to estimate species presence on the basis of detection-nondetection data have greatly increased the feasibility of species-level monitoring. For example, recent advances in monitoring make use of detection-nondetection data that are relatively inexpensive to acquire, historical survey data, and new techniques in genetic evaluation. The ability to use indirect measures of presence for some species greatly increases monitoring efficiency and reduces survey costs. After adjusting for false absences, the proportion of sample units in a landscape where a species is detected (occupancy) is a logical state variable to monitor. Occupancy monitoring can be based on real-time observation of a species at a survey site or on evidence that the species was at the survey location sometime in the recent past. Temporal and spatial patterns in occupancy data are related to changes in animal abundance and provide insights into the probability of a species' persistence. However, even with the efficiencies gained when occupancy is the monitored state variable, the task of species-level monitoring remains daunting due to the large number of species. We propose that a small number of species be monitored on the basis of specific management objectives, their functional role in an ecosystem, their sensitivity to environmental changes likely to occur in the area, or their conservation importance.     

Mechanistic understanding of human–wildlife conflict through a novel application of dynamic occupancy models

Crop and livestock depredation by wildlife is a primary driver of human–wildlife conflict, a problem that threatens the coexistence of people and wildlife globally. Understanding mechanisms that underlie depredation patterns holds the key to mitigating conflicts across time and space. However, most studies do not consider imperfect detection and reporting of conflicts, which may lead to incorrect inference regarding its spatiotemporal drivers. We applied dynamic occupancy models to elephant crop depredation data from India between 2005 and 2011 to estimate crop depredation occurrence and model its underlying dynamics as a function of spatiotemporal covariates while accounting for imperfect detection of conflicts. The probability of detecting conflicts was consistently <1.0 and was negatively influenced by distance to roads and elevation gradient, averaging 0.08–0.56 across primary periods (distinct agricultural seasons within each year). The probability of crop depredation occurrence ranged from 0.29 (SE 0.09) to 0.96 (SE 0.04). The probability that sites raided by elephants in primary period t would not be raided in primary period t + 1 varied with elevation gradient in different seasons and was influenced negatively by mean rainfall and village density and positively by distance to forests. Negative effects of rainfall variation and distance to forests best explained variation in the probability that sites not raided by elephants in primary period t would be raided in primary period t + 1. With our novel application of occupancy models, we teased apart the spatiotemporal drivers of conflicts from factors that influence how they are observed, thereby allowing more reliable inference on mechanisms underlying observed conflict patterns. We found that factors associated with increased crop accessibility and availability (e.g., distance to forests and rainfall patterns) were key drivers of elephant crop depredation dynamics. Such an understanding is essential for rigorous prediction of future conflicts, a critical requirement for effective conflict management in the context of increasing human–wildlife interactions.     

Impact of cattle grazing on the occupancy of a cryptic, threatened rail

Impacts of livestock grazing in arid and semiarid environments are often concentrated in and around wetlands where animals congregate for water, cooler temperatures, and green forage. We assessed the impacts of winter-spring (November-May) cattle grazing on marsh vegetation cover and occupancy of a highly secretive marsh bird that relies on dense vegetation cover, the California Black Rail (Laterallus jamaicensis coturniculus), in the northern Sierra Nevada foothills of California, U.S.A. Using detection-nondetection data collected during repeated call playback surveys at grazed vs. ungrazed marshes and a "random changes in occupancy" parameterization of a multi-season occupancy model, we examined relationships between occupancy and habitat covariates, while accounting for imperfect detection. Marsh vegetation cover was significantly lower at grazed marshes than at ungrazed marshes during the grazing season in 2007 but not in 2008. Winter-spring grazing had little effect on Black Rail occupancy at irrigated marshes. However, at nonirrigated marshes fed by natural springs and streams, grazed sites had lower occupancy than ungrazed sites. Black Rail occupancy was positively associated with marsh area, irrigation as a water source, and summer vegetation cover, and negatively associated with marsh isolation. Residual dry matter (RDM), a commonly used metric of grazing intensity, was significantly associated with summer marsh vegetation cover at grazed sites but not spring cover. Direct monitoring of marsh vegetation cover, particularly at natural spring- or stream-fed marshes, is recommended to prevent negative impacts to rails from overgrazing.     

Assessing hypotheses about nesting site occupancy dynamics

Hypotheses about habitat selection developed in the evolutionary ecology framework assume that individuals, under some conditions, select breeding habitat based on expected fitness in different habitat. The relationship between habitat quality and fitness may be reflected by breeding success of individuals, which may in turn be used to assess habitat quality. Habitat quality may also be assessed via local density: if high-quality sites are preferentially used, high density may reflect high-quality habitat. Here we assessed whether site occupancy dynamics vary with site surrogates for habitat quality. We modeled nest site use probability in a seabird subcolony (the Black-legged Kittiwake, Rissa tridactyla) over a 20-year period. We estimated site persistence (an occupied site remains occupied from time t to t+1) and colonization through two subprocesses: first colonization (site creation at the timescale of the study) and recolonization (a site is colonized again after being deserted). Our model explicitly incorporated site-specific and neighboring breeding success and conspecific density in the neighborhood. Our results provided evidence that reproductively "successful" sites have a higher persistence probability than "unsuccessful" ones. Analyses of site fidelity in marked birds and of survival probability showed that high site persistence predominantly reflects site fidelity, not immediate colonization by new owners after emigration or death of previous owners. There is a negative quadratic relationship between local density and persistence probability. First colonization probability decreases with density, whereas recolonization probability is constant. This highlights the importance of distinguishing initial colonization and recolonization to understand site occupancy. All dynamics varied positively with neighboring breeding success. We found evidence of a positive interaction between site-specific and neighboring breeding success. We addressed local population dynamics using a site occupancy approach integrating hypotheses developed in behavioral ecology to account for individual decisions. This allows development of models of population and metapopulation dynamics that explicitly incorporate ecological and evolutionary processes.     

Neighborhood and habitat effects on vital rates: expansion of the Barred Owl in the Oregon coast ranges

In this paper, we modify dynamic occupancy models developed for detection-nondetection data to allow for the dependence of local vital rates on neighborhood occupancy, where neighborhood is defined very flexibly. Such dependence of occupancy dynamics on the status of a relevant neighborhood is pervasive, yet frequently ignored. Our framework permits joint inference about the importance of neighborhood effects and habitat covariates in determining colonization and extinction rates. Our specific motivation is the recent expansion of the Barred Owl (Strix varia) in western Oregon, USA, over the period 1990-2010. Because the focal period was one of dramatic range expansion and local population increase, the use of models that incorporate regional occupancy (sources of colonists) as determinants of dynamic rate parameters is especially appropriate. We began our analysis of 21 years of Barred Owl presence/nondetection data in the Tyee Density Study Area (TDSA) by testing a suite of six models that varied only in the covariates included in the modeling of detection probability. We then tested whether models that used regional occupancy as a covariate for colonization and extinction outperformed models with constant or year-specific colonization or extinction rates. Finally we tested whether habitat covariates improved the AIC of our models, focusing on which habitat covariates performed best, and whether the signs of habitat effects are consistent with a priori hypotheses. We conclude that all covariates used to model detection probability lead to improved AIC, that regional occupancy influences colonization and extinction rates, and that habitat plays an important role in determining extinction and colonization rates. As occupancy increases from low levels toward equilibrium, colonization increases and extinction decreases, presumably because there are more and more dispersing juveniles. While both rates are affected, colonization increases more than extinction decreases. Colonization is higher and extinction is lower in survey polygons with more riparian forest. The effects of riparian forest on extinction rates are greater than on colonization rates. Model results have implications for management of the invading Barred Owl, both through habitat alteration and removal.     

Variance and Uncertainty in the Expected Number of Occurrences in Reserve Selection

Abstract:  Reserve selection often concerns the design of reserve networks for the long-term maintenance of biodiversity. We considered uncertainty in the context of three common reserve-selection formulations, the expected number of populations, proportional coverage of land-cover types, and the probability of having at least one population. By uncertainty, we mean variance in the outcome of any probability-based reserve selection formulation. A typical reserve-selection formulation might ask for the least expensive set of sites that contains n populations per species. It is implicit here that this requirement concerns the expected number of populations, which actually is obtained only with a 50% chance. If the requirement is changed to select the least expensive set of sites that gives n populations per species with a 95% probability, the number of sites required in the solution increases and the identity of the sites is changed toward sites that have high probabilities of persistence (or occurrence) and low associated binomial variance. Anthropogenic threat is one factor that may cause probabilistic uncertainty in the context of proportional area coverage.     

General methods for sensitivity analysis of equilibrium dynamics in patch occupancy models

Sensitivity analysis is a useful tool for the study of ecological models that has many potential applications for patch occupancy modeling. Drawing from the rich foundation of existing methods for Markov chain models, I demonstrate new methods for sensitivity analysis of the equilibrium state dynamics of occupancy models. Estimates from three previous studies are used to illustrate the utility of the sensitivity calculations: a joint occupancy model for a prey species, its predators, and habitat used by both; occurrence dynamics from a well-known metapopulation study of three butterfly species; and Golden Eagle occupancy and reproductive dynamics. I show how to deal efficiently with multistate models and how to calculate sensitivities involving derived state variables and lower-level parameters. In addition, I extend methods to incorporate environmental variation by allowing for spatial and temporal variability in transition probabilities. The approach used here is concise and general and can fully account for environmental variability in transition parameters. The methods can be used to improve inferences in occupancy studies by quantifying the effects of underlying parameters, aiding prediction of future system states, and identifying priorities for sampling effort.     

Dealing with the Clandestine Nature of Wildlife‐Trade Market Surveys

Abstract: Illegal international trade in wildlife (excluding fisheries and timber) has been valued at more than US$20 billion. A more precise figure has not been determined in part because of the clandestine nature of the trade, and for this same reason even regional and local levels of wildlife trade are difficult to assess. The application of recent developments in wildlife field‐survey methods (e.g., occupancy) now allows for a more‐accurate estimation of wildlife trade occurrence, including its hidden components at a variety of scales (e.g., regional, local) and periods (e.g., single season, 1 year, multiple years). Occupancy models have been applied in wildlife field studies to address the problem of false absences when conducting presence–absence surveys. Occupancy surveys differ from traditional presence–absence surveys because they incorporate repeat surveys, allowing for the likelihood of detecting a species (the probability of detection) to be estimated explicitly (in contrast to traditional surveys that often incorrectly treat this probability as close to one to allow for estimation of presence). Occupancy methods can be applied to a variety of wildlife‐trade surveys, including, for example, single‐species availability, links between two illegally traded species (i.e., co‐occurrence), and disease occurrence in live trade. In addition, free user‐friendly software (i.e., PRESENCE) allows even nonstatisticians to adequately address this issue. I simulated a hypothetical wildlife‐trade market survey that resulted in an apparent 20% decline in naïve occupancy (proportion of surveyed towns engaged in the trade) over 2 years, but when I accounted for change in probability of detection over the years the difference in occupancy was not statistically significant. As more sophisticated methods, such as occupancy, are applied to wildlife‐trade market surveys, results will be more robust and defensible and therefore, theoretically, more powerful when presented to conservation policy and decision makers.     

Metapopulation dynamics in the butterfly Hipparchia semele changed decades before occupancy declined in The Netherlands

The survival of many species in human-dominated, fragmented landscapes depends on metapopulation dynamics, i.e., on a dynamic equilibrium of extinctions and colonizations in patches of suitable habitat. To understand and predict distributional changes, knowledge of these dynamics can be essential, and for this, metapopulation studies are preferably based on long-time-series data from many sites. Alas, such data are very scarce. An alternative is to use opportunistic data (i.e., collected without applying standardized field methods), but these data suffer from large variations in field methods and search intensity between sites and years. Dynamic site-occupancy models offer a general approach to adjust for variable survey effort. These models extend classical metapopulation models to account for imperfect detection of species and yield estimates of the probabilities of occupancy, colonization, and survival of species at sites. By accounting for detection, they fully correct for among-year variability in search effort. As an illustration, we fitted a dynamic site-occupancy model to 60 years of presence-absence data (more precisely, detection-nondetection) of the heathland butterfly Hipparchia semele in The Netherlands. Detection records were obtained from a database containing volunteer-based data from 1950-2009, and nondetection records were deduced from database records of other butterfly species. Our model revealed that metapopulation dynamics of Hipparchia had changed decades before the species' distribution began to contract. Colonization probability had already started to decline from 1950 onward, but this was counterbalanced by an increase in the survival of existing populations, the result of which was a stable distribution. Only from 1990 onward was survival not sufficient to compensate for the further decrease of colonization, and occupancy started to decline. Hence, it appears that factors acting many decades ago triggered a change in the metapopulation dynamics of this species, which ultimately led to a severe decline in occupancy that only became apparent much later. Our study emphasizes the importance of knowledge of changes in survival and colonization of species in modern landscapes over a very long time scale. It also demonstrates the power of site-occupancy modeling to obtain important population dynamics information from databases containing opportunistic sighting records.     

Use of Multispecies Occupancy Models to Evaluate the Response of Bird Communities to Forest Degradation Associated with Logging

Forest degradation is arguably the greatest threat to biodiversity, ecosystem services, and rural livelihoods. Therefore, increasing understanding of how organisms respond to degradation is essential for management and conservation planning. We were motivated by the need for rapid and practical analytical tools to assess the influence of management and degradation on biodiversity and system state in areas subject to rapid environmental change. We compared bird community composition and size in managed (ejido, i.e., communally owned lands) and unmanaged (national park) forests in the Sierra Tarahumara region, Mexico, using multispecies occupancy models and data from a 2‐year breeding bird survey. Unmanaged sites had on average higher species occupancy and richness than managed sites. Most species were present in low numbers as indicated by lower values of detection and occupancy associated with logging‐induced degradation. Less than 10% of species had occupancy probabilities >0.5, and degradation had no positive effects on occupancy. The estimated metacommunity size of 125 exceeded previous estimates for the region, and sites with mature trees and uneven‐aged forest stand characteristics contained the highest species richness. Higher estimation uncertainty and decreases in richness and occupancy for all species, including habitat generalists, were associated with degraded young, even‐aged stands. Our findings show that multispecies occupancy methods provide tractable measures of biodiversity and system state and valuable decision support for landholders and managers. These techniques can be used to rapidly address gaps in biodiversity information, threats to biodiversity, and vulnerabilities of species of interest on a landscape level, even in degraded or fast‐changing environments. Moreover, such tools may be particularly relevant in the assessment of species richness and distribution in a wide array of habitats. Uso de Modelos de Ocupación para Múltiples Especies para Evaluar la Respuesta de las Comunidades de Aves a la Degradación de Bosques Asociada con la Tala