Linking occupancy surveys with habitat characteristics to estimate abundance and distribution in an endangered cryptic bird

Accurate estimates of the distribution and abundance of endangered species are crucial to determine their status and plan recovery options, but such estimates are often difficult to obtain for species with low detection probabilities or that occur in inaccessible habitats. The Puaiohi (Myadestes palmeri) is a cryptic species endemic to Kaua?i, Hawai‘i, and restricted to high elevation ravines that are largely inaccessible. To improve current population estimates, we developed an approach to model distribution and abundance of Puaiohi across their range by linking occupancy surveys to habitat characteristics, territory density, and landscape attributes. Occupancy per station ranged from 0.17 to 0.82, and was best predicted by the number and vertical extent of cliffs, cliff slope, stream width, and elevation. To link occupancy estimates with abundance, we used territory mapping data to estimate the average number of territories per survey station (0.44 and 0.66 territories per station in low and high occupancy streams, respectively), and the average number of individuals per territory (1.9). We then modeled Puaiohi occupancy as a function of two remote-sensed measures of habitat (stream sinuosity and elevation) to predict occupancy across its entire range. We combined predicted occupancy with estimates of birds per station to produce a global population estimate of 494 (95% CI 414–580) individuals. Our approach is a model for using multiple independent sources of information to accurately track population trends, and we discuss future directions for modeling abundance of this, and other, rare species.     

Evaluating abundance estimates and evidence of breeding for Bobolinks from transect and point-count surveys

Estimating the abundance and breeding success of territorial songbirds is challenging. Various types of surveys and analyses are available, but all receive some criticism in the literature, and most methods are rarely compared with results obtained using intensive monitoring efforts. We assessed the efficacy of transect and point-count surveys to estimate the abundance of male Bobolinks (Dolichonyx oryzivorus) and detect evidence of nesting and fledging by comparing the results of those surveys to results from more intensive monitoring (i.e., spot mapping and nest monitoring). We monitored 36 fields (254 ha) of late-harvest hay, restored grassland, and fallow fields in the Luther Marsh Wildlife Management Area and on four farms in southern Ontario, Canada, in 2018. Compared to the number of territories identified based on spot mapping (197), distance sampling analysis of transect survey data provided a more accurate estimate of the abundance of male Bobolinks (230, 95% CI: 187, 282) than N-mixture models of transect (668, 95% CI: 332, 1342) and point-count (337, 95% CI: 203, 559) data. Three visits to survey transects and five to point counts did not effectively detect evidence of Bobolink breeding (i.e., nesting or fledging) in fields compared to spot mapping and nest monitoring. Distance sampling analysis of transect data appears promising for estimating the number of Bobolink territories in an area, e.g., those impacted by conservation programs. If estimates of the number of nesting Bobolinks and frequency of fledging are of interest, spot mapping and nest monitoring could be implemented at a subset of sampled fields. Our results suggest that additional studies to evaluate model-based estimates of abundance with the best available information (e.g., from spot mapping of marked or unmarked populations and nest monitoring) would be useful to ensure that robust estimates are provided to support population estimates and conservation actions.     

Potentiality and limitations of N-mixture and Royle-Nichols models to estimate animal abundance based on noninstantaneous point surveys

Reliable and accurate information on animal abundance is fundamental for the conservation and management of wildlife. Recently, a number of innovative devices (such as camera traps) have been widely used in field surveys and have largely improved survey efficiency. However, these devices often constitute noninstantaneous point surveys, resulting in the multiple counts of the same animal individuals within a single sampling occasion (i.e., false-positive errors). Many commonly-used statistical models do not explicitly account for the false-positive error, with its effects on estimates being poorly understood. Here, I tested the performance of the commonly-used Poisson-binomial N-mixture and the Royle-Nichols model in the presence of both false-positive and negative errors (i.e., individuals in a population might not be detected). I also implemented the Poisson-Poisson mixture model in the Bayesian framework to evaluate its reliability. The results of the simulation using random walks based on Ornstein-Uhlenbeck processes showed that the Poisson-binomial model was not robust to false-positive errors. In comparison, the Royle-Nichols and Poisson-Poisson models provided reasonable estimates of the number of animals whose home range included the survey point. However, the number of animals whose home range included the survey point is inherently influenced by the size of animal home ranges, and thus cannot be used as a surrogate of animal density. Although the N-mixture and Royle-Nichols models are widely used, their utility might be restricted by this limitation. In conclusion, studies should clearly define the objective of surveys and carefully consider whether the models used are valid.     

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.     

Optimal design of butterfly occupancy surveys and testing if occupancy converts to abundance for sparse populations

Occupancy has several important advantages over abundance methods and may be the best choice for monitoring sparse populations. Here we use simulations to evaluate competing designs (number of sites vs. number of surveys) for occupancy monitoring, with emphasis on sparse populations of the endangered Karner blue butterfly (Lycaeides melissa samuelis Nabokov). Because conservation planning is usually abundance-based, we also ask whether detection/non-detection data may reliably convert to abundance, hypothesizing that occupancy provides a more dependable shortcut when populations are sparse. Count-index and distance sampling were conducted across 50 habitat patches containing variably sparse Karner blue populations. We used occupancy-detection model estimates as simulation inputs to evaluate primary replication tradeoffs, and used peak counts and population densities to evaluate the occupancy-abundance relationship. Detection probability and therefore optimal design of occupancy monitoring was strongly temperature dependent. Assuming a quality threshold of 0.075 root-mean square error for the occupancy estimator, the minimum allowable effort was 360 (40 sites?×?9 surveys) for spring generation and 200 (20 sites?×?10 surveys) for summer generation. A mixture model abundance estimator for repeated detection/non-detection data was biased low for high-density and low-density populations, suggesting that occupancy may not provide a reliable shortcut in abundance-based conservation planning for sparse butterfly populations.     

Assessing breeding success in common woodland birds using a novel method

Capsule Repeated counts of fledged broods can provide a useful estimate of breeding success for most common woodland birds.

Aims To assess the efficacy of comparing fledged-brood survey data with territory mapping using simple mark–recapture analysis techniques to provide an estimate of breeding success for common woodland birds that does not involve finding nests.

Methods Three observers undertook territory mapping surveys of adults, followed by counts of fledged broods four times a week during May–July 2007 in two 15 ha woods each, both in southern England. Using known fledging to maturity periods, these counts were used to calculate daily detection probabilities for broods of ubiquitous species. These enabled fledged brood territory occupancy probabilities (i.e. brood to territory ratios) to be estimated that take account of the possibility that broods were present but missed by surveys.

Results Of the 19 species found in all six woods, mean daily detection probability estimates for fledged broods of 17 species ranged from 0.17 to 0.50 with significant variation between woods for 12 species, but within region/observer for four species. The mean probability of detecting a brood at least once was over 75% using four visits per week and over 50% using two visits. Only for Great Spotted Woodpeckers Dendrocopos major and Garden Warblers Sylvia borin was the fledging period too short and the daily detection probability too low to provide a reasonable estimate of the territory occupancy probability.

Conclusion Daily detection probabilities for fledged broods of most common woodland birds were sufficiently high to enable useable estimates of fledged-brood territory occupancy probabilities to be made based on a survey programme involving two or three visits per week between late May and the end June. The method used may have application as a means of providing a relatively easily derived productivity index for woodland bird monitoring programmes or for research studies.     

Comparison of occupancy modeling and radiotelemetry to estimate ungulate population dynamics

Radiotelemetry and unmarked occupancy modeling have been used to estimate animal population growth, but have not been compared for ungulates. We compared white-tailed deer (Odocoileus virginianus) population growth estimates from radiomarked individuals and occupancy modeling of unmarked individuals and evaluated advantages and disadvantages of each method. Estimates of population growth were obtained using remote camera (N = 54/year) detection/non-detection occupancy surveys of unmarked deer and from survival and recruitment data of radiomarked adult females (N = 87) and neonate fawns (N = 127) in a predominantly forested region of the Upper Peninsula of Michigan, USA, 2009–2011. We hypothesized that occupancy models and radiotelemetry data would have similar population growth trends because both methods sampled the same temporally closed population. Percent changes in camera trap data generally reflected finite population growth (λ) of radiomarked deer which increased (λ = 1.10 ± 0.01) from 2009 to 2010, but decreased (λ = 0.87 ± 0.02) from 2010 to 2011. Also, unmarked adult female abundance and fawn:adult female ratios generally reflected trends in radiomarked deer survival and recruitment. Royle–Nichols occupancy model abundance estimates had wide confidence intervals, which may preclude using this method from accurately estimating deer population growth. Radiotelemetry provided more precise population growth estimates, while allowing collection of vital rates and location data. However, the Royle–Nichols occupancy model may be preferred to radiotelemetry because it reflected yearly variation in population growth with reduced labor and no invasive marking. Researchers should consider the objectives and logistics of their study when choosing a specific method.     

Models for Estimating Abundance from Repeated Counts of an Open Metapopulation

Summary Using only spatially and temporally replicated point counts, Royle (2004b, Biometrics 60, 108–115) developed an N ‐mixture model to estimate the abundance of an animal population when individual animal detection probability is unknown. One assumption inherent in this model is that the animal populations at each sampled location are closed with respect to migration, births, and deaths throughout the study. In the past this has been verified solely by biological arguments related to the study design as no statistical verification was available. In this article, we propose a generalization of the N ‐mixture model that can be used to formally test the closure assumption. Additionally, when applied to an open metapopulation, the generalized model provides estimates of population dynamics parameters and yields abundance estimates that account for imperfect detection probability and do not require the closure assumption. A simulation study shows these abundance estimates are less biased than the abundance estimate obtained from the original N ‐mixture model. The proposed model is then applied to two data sets of avian point counts. The first example demonstrates the closure test on a single‐season study of Mallards (Anas platyrhynchos), and the second uses the proposed model to estimate the population dynamics parameters and yearly abundance of American robins (Turdus migratorius) from a multi‐year study.     

Hierarchical multi-scale occupancy estimation for monitoring wildlife populations

Occupancy estimation is an effective analytic framework, but requires repeated surveys of a sample unit to estimate the probability of detection. Detection rates can be estimated from spatially replicated rather than temporally replicated surveys, but this may violate the closure assumption and result in biased estimates of occupancy. We present a new application of a multi-scale occupancy model that permits the simultaneous use of presence–absence data collected at 2 spatial scales and uses a removal design to estimate the probability of detection. Occupancy at the small scale corresponds to local territory occupancy, whereas occupancy at the large scale corresponds to regional occupancy of the sample units. Small-scale occupancy also corresponds to a spatial availability or coverage parameter where a species may be unavailable for sampling at a fraction of the survey stations. We applied the multi-scale occupancy model to a hierarchical sample design for 2 bird species in the Black Hills National Forest: brown creeper (Certhia americana) and lark sparrow (Chondestes grammacus). Our application of the multi-scale occupancy model is particularly well suited for hierarchical sample designs, such as spatially replicated survey stations within sample units that are typical of avian monitoring programs. The model appropriately accounts for the non-independence of the spatially replicated survey stations, addresses the closure assumption for the spatially replicated survey stations, and is useful for decomposing the observation process into detection and availability parameters. This analytic approach is likely to be useful for monitoring at local and regional scales, modeling multi-scale habitat relationships, and estimating population state variables for rare species of conservation concern. © 2011 The Wildlife Society.     

Study of biological communities subject to imperfect detection: bias and precision of community <Emphasis Type="Italic">N</Emphasis>-mixture abundance models in small-sample situations

Community N-mixture abundance models for replicated counts provide a powerful and novel framework for drawing inferences related to species abundance within communities subject to imperfect detection. To assess the performance of these models, and to compare them to related community occupancy models in situations with marginal information, we used simulation to examine the effects of mean abundance \((\bar{\lambda }\): 0.1, 0.5, 1, 5), detection probability \((\bar{p}\): 0.1, 0.2, 0.5), and number of sampling sites (n _site : 10, 20, 40) and visits (n _visit : 2, 3, 4) on the bias and precision of species-level parameters (mean abundance and covariate effect) and a community-level parameter (species richness). Bias and imprecision of estimates decreased when any of the four variables \((\bar{\lambda }\), \(\bar{p}\), n _site , n _visit ) increased. Detection probability \(\bar{p}\) was most important for the estimates of mean abundance, while \(\bar{\lambda }\) was most influential for covariate effect and species richness estimates. For all parameters, increasing n _site was more beneficial than increasing n _visit . Minimal conditions for obtaining adequate performance of community abundance models were n _site  ≥ 20, \(\bar{p}\) ≥ 0.2, and \(\bar{\lambda }\) ≥ 0.5. At lower abundance, the performance of community abundance and community occupancy models as species richness estimators were comparable. We then used additive partitioning analysis to reveal that raw species counts can overestimate β diversity both of species richness and the Shannon index, while community abundance models yielded better estimates. Community N-mixture abundance models thus have great potential for use with community ecology or conservation applications provided that replicated counts are available.     

Long-term demographic surveys reveal a consistent relationship between average occupancy and abundance within local populations of a butterfly metapopulation

Species distribution models are the tool of choice for large-scale population monitoring, environmental association studies and predictions of range shifts under future environmental conditions. Available data and familiarity of the tools rather than the underlying population dynamics often dictate the choice of specific method – especially for the case of presence–absence data. Yet, for predictive purposes, the relationship between occupancy and abundance embodied in the models should reflect the actual population dynamics of the modelled species. To understand the relationship of occupancy and abundance in a heterogeneous landscape at the scale of local populations, we built a spatio-temporal regression model of populations of the Glanville fritillary butterfly Melitaea cinxia in a Baltic Sea archipelago. Our data comprised nineteen years of habitat surveys and snapshot data of land use in the region. We used variance partitioning to quantify relative contributions of land use, habitat quality and metapopulation covariates. The model revealed a consistent and positive, but noisy relationship between average occupancy and mean abundance in local populations. Patterns of abundance were highly variable across years, with large uncorrelated random variation and strong local population stochasticity. In contrast, the spatio-temporal random effect, habitat quality, population connectivity and patch size explained variation in occupancy, vindicating metapopulation theory as the basis for modelling occupancy patterns in fragmented landscapes. Previous abundance was an important predictor in the occupancy model, which points to a spillover of abundance into occupancy dynamics. While occupancy models can successfully model large-scale population structure and average occupancy, extinction probability estimates for local populations derived from occupancy-only models are overconfident, as extinction risk is dependent on actual, not average, abundance.     

Modelling occurrence and abundance of species when detection is imperfect

Relationships between species abundance and occupancy are of considerable interest in metapopulation biology and in macroecology. Such relationships may be described concisely using probability models that characterize variation in abundance of a species. However, estimation of the parameters of these models in most ecological problems is impaired by imperfect detection. When organisms are detected imperfectly, observed counts are biased estimates of true abundance, and this induces bias in stated occupancy or occurrence probability. In this paper we consider a class of models that enable estimation of abundance/occupancy relationships from counts of organisms that result from surveys in which detection is imperfect. Under such models, parameter estimation and inference are based on conventional likelihood methods. We provide an application of these models to geographically extensive breeding bird survey data in which alternative models of abundance are considered that include factors that influence variation in abundance and detectability. Using these models, we produce estimates of abundance and occupancy maps that honor important sources of spatial variation in avian abundance and provide clearly interpretable characterizations of abundance and occupancy adjusted for imperfect detection.     

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

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

Optimizing surveys for marsh songbirds: does timing matter?

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

New technologies in the mix: Assessing N‐mixture models for abundance estimation using automated detection data from drone surveys

1. Reliable estimates of abundance are critical in effectively managing threatened species, but the feasibility of integrating data from wildlife surveys completed using advanced technologies such as remotely piloted aircraft systems (RPAS) and machine learning into abundance estimation methods such as N‐mixture modeling is largely unknown due to the unique sources of detection errors associated with these technologies.
2. We evaluated two modeling approaches for estimating the abundance of koalas detected automatically in RPAS imagery: (a) a generalized N‐mixture model and (b) a modified Horvitz–Thompson (H‐T) estimator method combining generalized linear models and generalized additive models for overall probability of detection, false detection, and duplicate detection. The final estimates from each model were compared to the true number of koalas present as determined by telemetry‐assisted ground surveys.
3. The modified H‐T estimator approach performed best, with the true count of koalas captured within the 95% confidence intervals around the abundance estimates in all 4 surveys in the testing dataset (n = 138 detected objects), a particularly strong result given the difficulty in attaining accuracy found with previous methods.
4. The results suggested that N‐mixture models in their current form may not be the most appropriate approach to estimating the abundance of wildlife detected in RPAS surveys with automated detection, and accurate estimates could be made with approaches that account for spurious detections.

     

Territory occupancy by common loons in response to disturbance,habitat, and intraspecific relationships

Structure and distribution of animal territories are driven by a variety of environmental and demographic factors. A peninsular population of common loons (Gavia immer) nests on lakes in northwestern Montana, but does not occupy all apparently suitable breeding territories, suggesting unexplained limitations on population growth. To evaluate territorial dynamics of breeding loons in Montana, we created and tested occupancy models that evaluated the hypothesized effects of disturbance, habitat, and intraspecific relationships on territory occupancy by common loons in Montana from 2003 to 2007. Model-averaged results indicated that the abundance of feeding lakes within 10 km (i.e., forage quality) and the number of territorial pairs within 10 km (i.e., density of loons) were equally supported and related to probabilities of occupancy. We found substantial support that the population was in a state of equilibrium, with the numbers of occupied territories stable in time, but not space. We also found that density of territorial pairs was related to the likelihood that an existing territory would be abandoned, but did not influence the establishment of new territories, suggesting the presence of territorial pairs could be a stronger indicator of territory quality to loons than physical lake characteristics. Our index of human disturbance was not well-supported compared to other factors. Our results suggest management for stable or growing loon populations could be achieved using long-term monitoring and protection of occupied territorial lakes and nearby feeding lakes, because these factors most influenced the probability of occupancy of surrounding lakes. © 2011 The Wildlife Society.     

Multistate Models Reveal Long-Term Trends of Northern Spotted Owls in the Absence of a Novel Competitor

Quantifying spatial and temporal variability in population trends is a critical aspect of successful management of imperiled species. We evaluated territory occupancy dynamics of northern spotted owls (Strix occidentalis caurina), California, USA, 1990–2014. The study area possessed two unique aspects. First, timber management has occurred for over 100 years, resulting in dramatically different forest successional and structural conditions compared to other areas. Second, the barred owl (Strix varia), an exotic congener known to exert significant negative effects on spotted owls, has not colonized the study area. We used a Bayesian dynamic multistate model to evaluate if territory occupancy of reproductive spotted owls has declined as in other study areas. The state-space approach for dynamic multistate modeling imputes the number of territories for each nesting state and allows for the estimation of longer-term trends in occupied or reproductive territories from longitudinal studies. The multistate approach accounts for different detection probabilities by nesting state (to account for either inherent differences in detection or for the use of different survey methods for different occupancy states) and reduces bias in state assignment. Estimated linear trends in the number of reproductive territories suggested an average loss of approximately one half territory per year (-0.55, 90% CRI: -0.76, -0.33), in one management block and a loss of 0.15 per year (-0.15, 90% CRI: -0.24, -0.07), in another management block during the 25 year observation period. Estimated trends in the third management block were also negative, but substantial uncertainty existed in the estimate (-0.09, 90% CRI: -0.35, 0.17). Our results indicate that the number of territories occupied by northern spotted owl pairs remained relatively constant over a 25 year period (-0.07, 90% CRI: -0.20, 0.05; -0.01, 90% CRI: -0.19, 0.16; -0.16, 90% CRI: -0.40, 0.06). However, we cannot exclude small-to-moderate declines or increases in paired territory numbers due to uncertainty in our estimates. Collectively, we conclude spotted owl pair populations on this landscape managed for commercial timber production appear to be more stable and do not show sharp year-over-year declines seen in both managed and unmanaged landscapes with substantial barred owl colonization and persistence. Continued monitoring of reproductive territories can determine whether recent declines continue or whether trends reverse as they have on four previous occasions. Experimental investigations to evaluate changes to spotted owl occupancy dynamics when barred owl populations are reduced or removed entirely can confirm the generality of this conclusion.     

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.     

Estimating breeding season abundance of golden-cheeked warblers in Texas,USA

Population abundance estimates using predictive models are important for describing habitat use and responses to population-level impacts, evaluating conservation status of a species, and for establishing monitoring programs. The golden-cheeked warbler (Setophaga chrysoparia) is a neotropical migratory bird that was listed as federally endangered in 1990 because of threats related to loss and fragmentation of its woodland habitat. Since listing, abundance estimates for the species have mainly relied on localized population studies on public lands and qualitative-based methods. Our goal was to estimate breeding population size of male warblers using a predictive model based on metrics for patches of woodland habitat throughout the species' breeding range. We first conducted occupancy surveys to determine range-wide distribution. We then conducted standard point-count surveys on a subset of the initial sampling locations to estimate density of males. Mean observed patch-specific density was 0.23 males/ha (95% CI = 0.197–0.252, n = 301). We modeled the relationship between patch-specific density of males and woodland patch characteristics (size and landscape composition) and predicted patch occupancy. The probability of patch occupancy, derived from a model that used patch size and landscape composition as predictor variables while addressing effects of spatial relatedness, best predicted patch-specific density. We predicted patch-specific densities as a function of occupancy probability and estimated abundance of male warblers across 63,616 woodland patches accounting for 1.678 million ha of potential warbler habitat. Using a Monte Carlo simulation, our approach yielded a range-wide male warbler population estimate of 263,339 (95% CI: 223,927–302,620). Our results provide the first abundance estimate using habitat and count data from a sampling design focused on range-wide inference. Managers can use the resulting model as a tool to support conservation planning and guide recovery efforts. © 2012 The Wildlife Society.     

Assessing the effectiveness of long-term monitoring of the Broad-toothed Rat in the Barrington Tops National Park,Australia

Biodiversity monitoring is crucial for effective conservation efforts. Effective monitoring allows managers to determine the status and trends of biodiversity, as well as the success of conservation actions. The population of the Broad-toothed Rats (Mastacomys fuscus) in the Barrington Tops National Park New South Wales, Australia has been monitored since 1999 via scat and live-trapping surveys. We reviewed the methods used and analysed the data produced with the aim of describing patterns of population change over time using a range of outcome variables and identifying different climate correlates. A secondary aim was to explore the use of population statistics that account for imperfect detection by comparing naïve occupancy, with an index of relative abundance based on trap effort, the latency to find scats during scat surveys and an occupancy model based on trapping surveys. Neither of these three methods accounts for detectability variation. Naïve occupancy decreased slightly over time, while the relative abundance based on trap effort revealed no evidence of change. Additionally, naïve occupancy decreased with increasing temperature while temperature had no clear impact on relative abundance. Finally, precipitation had no impact on either naïve occupancy or relative abundance. We found no evidence of a relationship between the latency to find scats and the index of relative abundance, suggesting that one or neither is related to actual abundance. Finally, a multi-season occupancy model found occupancy probability to be 0.78 ± 0.23 (standard error); detection probability as 0.51 ± 0.06; seasonal colonisation rate as 0.36 ± 0.13 and seasonal extinction rate at 0.44 ± 0.13. We conclude that despite significant investment in monitoring, this historical data set does not allow managers to ascertain whether population change has occurred and to identify potential drivers of change. Careful consideration of future methods, in particular, whether there is imperfect detection in scat surveys, will help to inform future monitoring.