Multi‐scale occupancy estimation and modelling using multiple detection methods

Occupancy models estimate distributions of imperfectly detected species, but violations of the closure assumption can bias results. However, researchers working with mobile animals may find it impossible to eliminate such violations. Here, we tested the hypothesis that occupancy models fit to realistic sampling data can generate unbiased occupancy estimates for an itinerant Wood Thrush (Hylocichla mustelina) population. In 2013 and 2014, we tracked movements of 41 breeding Wood Thrush males. We modelled territory shift probabilities using logistic exposure models and within‐territory movements using continuous‐time stochastic process models. We then constructed an individual‐based model, simulated (1000 iterations) spatiotemporal locations for individuals and simulated sampling these populations using 162 different point count protocols with variable spatial (sampling radius and point placement method), and temporal (survey length, between‐survey intervals and number of surveys) characteristics. We compared occupancy estimates with true values of instantaneous, daily and seasonal occupancy from the simulations. We parameterized continuous time stochastic process models based on movements within 34 unique territories and estimated a daily territory shift probability of 0.0099 (95% CI: 0.0060, 0.0152). Simulated data indicated that estimates of occupancy ranged from 0.18 (0.06, 1.00) to 0.80 (0.71, 0.89) depending on protocol characteristics. Occupancy estimates increased with increasing survey radius, survey length and between‐survey interval. Protocols using shorter surveys and between‐survey intervals were good estimators for instantaneous occupancy (low bias and mean‐squared error) but poor estimators for daily and seasonal occupancy; longer surveys and intervals generated unbiased estimators of daily occupancy but underestimated seasonal occupancy. Logistic regression models that ignored imperfect detection outperformed occupancy models for estimating instantaneous occupancy but not daily or seasonal occupancy. For mobile animals, occupancy of sampling sites changes in space and time. Consequently, the spatial and temporal aspects of a sampling protocol have strong, but predictable, effects on occupancy model parameter estimates. Our results demonstrate that how these factors interact is critical for designing surveys that produce occupancy estimates representative of the biological process of interest to a researcher.

Few studies link habitat to grizzly bear Ursus arctos abundance and these have not accounted for the variation in detection or spatial autocorrelation. We collected and genotyped bear hair in and around Glacier National Park in northwestern Montana during the summer of 2000. We developed a hierarchical Markov chain Monte Carlo model that extends the existing occupancy and count models by accounting for (1) spatially explicit variables that we hypothesized might influence abundance; (2) separate sub-models of detection probability for two distinct sampling methods (hair traps and rub trees) targeting different segments of the population; (3) covariates to explain variation in each sub-model of detection; (4) a conditional autoregressive term to account for spatial autocorrelation; (5) weights to identify most important variables. Road density and per cent mesic habitat best explained variation in female grizzly bear abundance; spatial autocorrelation was not supported. More female bears were predicted in places with lower road density and with more mesic habitat. Detection rates of females increased with rub tree sampling effort. Road density best explained variation in male grizzly bear abundance and spatial autocorrelation was supported. More male bears were predicted in areas of low road density. Detection rates of males increased with rub tree and hair trap sampling effort and decreased over the sampling period. We provide a new method to (1) incorporate multiple detection methods into hierarchical models of abundance; (2) determine whether spatial autocorrelation should be included in final models. Our results suggest that the influence of landscape variables is consistent between habitat selection and abundance in this system.

Occupancy‐based monitoring has become an important tool in wildlife conservation and management. Nonetheless, meeting occupancy modeling assumptions and providing biologically accurate information are difficult tasks over long time periods, large areas, or when monitoring multiple species. In occupancy modeling frameworks, derived grids are commonly used to divide landscapes into discrete units. Grid sizes that match the home range size of the species of interest are considered optimal, but this practice is complicated as home range size may vary by sex, habitat quality, or among species. Additionally, studies often assume their survey methods sample an entire grid cell when the actual effective sampling area may be much smaller. The effect of reduced effective sampling area on occupancy estimation has received little attention to date, despite being flagged as a critical issue. In this study, we assessed (1) how the relationship between effective area, home range size, and grid size affects power to detect trends in occupancy; (2) how varying the sampling design factors of effective area, duration, detection probability, and resurvey interval influence monitoring efficiency; and (3) determine whether a single sampling design can simultaneously detect declines in two species with different home range sizes. We used a spatially explicit simulation framework to create biologically realistic declining populations over 10 yr and assessed statistical power to detect known declines using occupancy modeling. We found that effective area and detection probability had the greatest influence on statistical power. We could not reliably detect declines when detection probability was low or when effective sampling area was <1/4 cell. We conclude that failing to account for effective area less than the cell size will result in overestimation of statistical power. Our simulations suggest occupancy models can detect declines for two species with different home range sizes using the same grid cell size under certain conditions, for instance, surveying >25% of the landscape, ≥25% effective area, and fixed sampling locations. Further, increasing resampling interval greatly increased monitoring efficiency. Our results show monitoring planning requires explicit consideration of effective sampling area and methods with sufficient detectability to detect population declines.

Spatial and temporal variation in planktons and water quality parameters were investigated in order to determine the effects of seasonal water use on reservoir water quality and planktons’ diversity in Kulekhani Multipurpose Reservoir, Nepal. This study also focuses on interactions among various water quality parameters with planktons and how such interactions can affect the second major utility, the fish farming in the reservoir. The analyses of seasonal water samples collected from three different sampling locations in the reservoir showed that select water quality parameters varied significantly (P < 0.05) with sampling seasons (transparency: 30 - 250 cm, pH: 7 - 7.5, alkalinity: 30 - 120 mg/L, DO: 6 - 11.5 mg/L, CO2: 0.1 - 1.1 mg/L) and sampling locations (phosphate: 0.1 - 0.25 mg/L, nitrate 0.01 - 0.19 mg/L) in the reservoir. Three groups of zooplankton and four classes of phytoplankton, respectively with eleven and twelve genera, were identified and quantified in the reservoir. Among them, Cyclops, Asplanchana, and Keratella were most dominant zooplanktons while Synedra, Melosira and Peridinum were the most dominant phytoplankton in the reservoir water. The abundance of select zooplanktons (Cyclops, Keratella, Polyanthra), and phytoplankton (Navicula, Melosira, Amphora, Chroococcus, Staurastrum, Scendesmus) showed significant interaction between sampling sites and sampling seasons, while the other varied only with sampling seasons and/or sites. These results showed that seasonal water level fluctuations, along with the variation of water quality parameters, change the abundance and diversity of planktons’ in the reservoir. Such changes can negatively impact the fish in cage culture, affecting the livelihood of people extensively relying on these fish farming.

This study focused on the structure of the aquatic insect community in spatial and temporal scales in the Esparza River. The river was sampled for one full year throughout 2007. During the dry season low flow months, five sampling points were selected in two different habitats (currents and pools), with five replicates per sample site. During the wet season with peak rain, only the data in the "current habitat" were sampled at each site. Specimens present in the different substrates were collected and preserved in situ. A nested ANOVA was then applied to the data to determine richness and density as the response variables. The variations in temporal and spatial scales were analyzed using width, depth and discharge of the river, and then analyzed using a nested ANOVA. Only a correlation of 51% similarity in richness was found, while in spatial scale, richness showed significant variation between sampling sites, but not between habitats. However, the temporal scale showed significant differences between habitats. Density showed differences between sites and habitats during the dry season in the spatial scale, while in the temporal scale significant variation was found between sampling sites. Width varied between habitats during the dry season, but not between sampling points. Depth showed differences between sampling sites and season. This work studies the importance of community structure of aquatic insects in rivers, and its relevance for the quality of water in rivers and streams.

Using occupancy models of forest breeding birds to prioritize conservation planning

ABSTRACTMammalian communities living in tropical forests, particularly those in ecological transition zones, are under constant threat from human activities. In many regions, baseline data on mammal richness, occupancy, detection probability and the environmental factors that influence these metrics are lacking. As a key metric for guiding conservation decisions, species richness can be underestimated due to varying detection probabilities, leading to species being overlooked. Advances in technology and methodology have revolutionised wildlife monitoring, fostering the increase of multi‐species occupancy models (MSOMs) for efficient studies of community, shifting focus from single species to entire communities. MSOMs, hierarchical models that share information across species via random effects, address imperfect detection to provide accurate and unbiased species richness estimates. To fill this information gap, we used cameras trap data from Mpem and Djim National Park, Central Cameroun. We used generalised linear models and a model selection approach to evaluate factors affecting species detection events. Similarly, we used MSOMs within a Bayesian hierarchical framework to evaluate our initial species richness estimate at each camera trap location and to understand the influence of environmental covariates on the occupancy and detection probability of 19 vertebrates recorded in the area to inform management decisions for these species. From 915 independent photographic events obtained over 1700 days of capture, the study results highlight the importance of habitat, distance to river, normalised difference vegetation index and topographic position index (TPI) in explaining patterns of detection events. We found that forest (GLM: B 0.31, IRR 1.36, CI 0.14–0.48, p &lt; 0.001), NDVI (GLM: B 0.31, IRR 1.36, CI 0.21–0.41, p &lt; 0.001) and TPI (GLM: B 0.17, IRR 1.19, CI 0.08–0.26, p &lt; 0.001) were positively associated with species detection events, whereas distance to river (GLM: B −0,19, IRR 0.83, CI 0.0.27 to (−0.11), p &lt; 0.001) was negatively associated with species detection events. However, the mean probability of community occupancy was 0.33 ± 0.10 [2.5%–97.5% CI: 0.17, 0.54], while the mean probability of community detection was 0.07 ± 0.02 [2.5%–95% CI: 0.04, 0.12], indicating that, on average, approximately 33% of the sites are likely to be occupied by the community of interest, with a 7% probability of detection at occupied sites. After accounting for imperfect detection, the maximum occupancy and detection probability estimated from the MSOMs were 0.88 ± 0.07 (2.5%–97.5% CI: 0.71–0.98) and 0.22 ± 0.2 (2.5%–97.5% CI: 0.18–0.27) for Philantomba monticola, respectively. Globally, the community responses were close to zero and relatively weak, probably due to mixed responses at the species level. Despite their weak effect, distance to road (β: −1.53 ± 1.97 [2.5%–97.5% CI: −1.72–5.62]) and NDVI (β: −0.09 ± 0.22 [2.5%–97.5% CI: −0.50–0.38]) had a negative significant effect on occupancy. However, there were significant responses at the species level with Cephalophus nigrifrons, for example, exhibiting a strong response to NDVI. This study contributes to baseline information on the ecology of mammal communities in Central Cameroon and supports the need for future multi‐season surveys to understand the influence of temporal factors on community occupancy and richness in the area.

Satellite Navigation Receiver Autonomous Integrity Monitoring (RAIM) is developed for detection and recognition of satellite faults using the redundant observation values of receiver itself. This article first makes a comparative analysis to the conventional and recently proposed RAIM fault detection and identification methods, and then points out that the conventional fault identification methods have such weakness as low fault detection probability and high false alarm probability in the situation of simultaneous multiple faulty satellites, while the recently proposed multi-satellite fault identification methods require excessive computation work though enjoy a higher detection probability. To solve these problems, a new RAIM multiple satellite fault detection and recognition method is proposed in this paper, which is characterized by high detection probability, low false alarm probability and low computation load, practicable for project implementation. Based on a simulation analysis, it turns out that this method has a higher detection probability without false alarm at all. This method requires so low computation work that it only needs 2 m times of least squares residuals fault detection and m times of fault identification in m (m > 0) faulty satellites situation, showing a very promising application value.

Occupancy models are used in statistical ecology to estimate species dispersion. The two components of an occupancy model are the detection and occupancy probabilities, with the main interest being in the occupancy probabilities. We show that for the homogeneous occupancy model there is an orthogonal transformation of the parameters that gives a natural two-stage inference procedure based on a conditional likelihood. We then extend this to a partial likelihood that gives explicit estimators of the model parameters. By allowing the separate modeling of the detection and occupancy probabilities, the extension of the two-stage approach to more general models has the potential to simplify the computational routines used there.

Sign surveys are commonly used to study and monitor wildlife species but may be flawed when surveys are conducted only once and cover short distances, which can lead to a lack of accountability for false absences. Multiple observers surveyed for river otter (Lontra canadensis) scat and tracks along stream and reservoir shorelines at 110 randomly selected sites in eastern Kansas from January to April 2008 and 2009 to determine if detection probability differed among substrates, sign types, observers, survey lengths, and near access points. We estimated detection probabilities (p) of river otters using occupancy models in Program PRESENCE. Mean detection probability for a 400‐m survey was highest in mud substrates (p = 0.60) and lowest in snow (p = 0.18) and leaf litter substrates (p = 0.27). Scat had a higher detection probability (p = 0.53) than tracks (p = 0.18), and experienced observers had higher detection probabilities (p &gt; 0.71) than novice observers (p &lt; 0.55). Detection probabilities increased almost 3‐fold as survey length increased from 200 m to 1,000 m, and otter sign was not concentrated near access points. After accounting for imperfect detection, our estimates of otter site occupancy based on a 400‐m survey increased &gt;3‐fold, providing further evidence of the potential negative bias that can occur in estimates from sign surveys when imperfect detection is not addressed. Our study identifies areas for improvement in sign survey methodologies and results are applicable for sign surveys commonly used for many species across a range of habitats. © 2010 The Wildlife Society

The river otter ( Lontra canadensis ) was extirpated from Nebraska, USA, in the early 1900s and reintroduced starting in 1986. Information is needed regarding the distribution of river otters in Nebraska before decisions can be made regarding its conservation status. Understanding distribution of a species is critically important for effective management. We investigated river otter distribution in Nebraska with occupancy modeling and maximum entropy (Maxent) modeling using 190 otter sign observations on Nebraska's navigable rivers and 380 historical otter records from November 1977 to April 2014. Both methods identified the Platte River, Elkhorn River, central and eastern Niobrara River, and southern Loup River system as core areas within the distribution of otters in Nebraska. The Maxent model provided more liberal estimates of site occupancy and identified some smaller rivers as being within the distribution of otters in Nebraska, which were not identified using occupancy modeling. We recommend that multiple data sets and analysis methods be used to estimate species distribution because this allows for the broadest geographical coverage and decreases the likelihood of overlooking areas with fewer animal records. If further reintroduction efforts or translocation efforts are to take place in the future, we recommend focusing on areas with high modeled occupancy but few historical and survey records. © 2018 The Wildlife Society.

Distribution models are increasingly being used to understand how landscape and climatic changes are affecting the processes driving spatial and temporal distributions of plants and animals. However, many modeling efforts ignore the dynamic processes that drive distributional patterns at different scales, which may result in misleading inference about the factors influencing species distributions. Current occupancy models allow estimation of occupancy at different scales and, separately, estimation of immigration and emigration. However, joint estimation of local extinction, colonization, and occupancy within a multi‐scale model is currently unpublished. We extended multi‐scale models to account for the dynamic processes governing species distributions, while concurrently modeling local‐scale availability. We fit the model to data for lark buntings and chestnut‐collared longspurs in the Great Plains, USA, collected under the Integrated Monitoring in Bird Conservation Regions program. We investigate how the amount of grassland and shrubland and annual vegetation conditions affect bird occupancy dynamics and local vegetation structure affects fine‐scale occupancy. Buntings were prevalent and longspurs rare in our study area, but both species were locally prevalent when present. Buntings colonized sites with preferred habitat configurations, longspurs colonized a wider range of landscape conditions, and site persistence of both was higher at sites with greener vegetation. Turnover rates were high for both species, quantifying the nomadic behavior of the species. Our model allows researchers to jointly investigate temporal dynamics of species distributions and hierarchical habitat use. Our results indicate that grassland birds respond to different covariates at landscape and local scales suggesting different conservation goals at each scale. High turnover rates of these species highlight the need to account for the dynamics of nomadic species, and our model can help inform how to coordinate management efforts to provide appropriate habitat configurations at the landscape scale and provide habitat targets for local managers.

Spread spectrum communication is a typical scheme for covert communication because of its low detectability and antijam characteristic. However, the associated design concerns multiple factors, such as cochannel multiple access interference (MAI) and spread spectrum gain. In this paper, the lattice reduction theory is applied to MAI cancellation of spread spectrum communication and a novel lattice reduction aided multiple user detection method is proposed. The near maximum likelihood (ML) performance of MAI resistance is verified by simulation and theoretical analysis. The superiority of detection performance in strong MAI scenarios is especially addressed. Based on the algorithm, a collaborative covert communication system design is proposed. Low-power covert signals can be transmitted at a higher bit rate with the same coverage as more high-power cochannel signals. The covert transmission performance can be improved significantly compared to traditional designs.

Abstract: Northern spotted owls (Strix occidentalis caurina) have received intense research and management interest since their listing as a threatened species by the United States Fish and Wildlife Service in 1990. Several spotted owl (Strix occidentalis) response variables have been examined in various investigations, but recent advances in statistical modeling permit evaluations of temporal and spatial variability in site occupancy, local‐extinction, and colonization probabilities while incorporating imperfect detection probabilities. Following recent work by other researchers on site occupancy dynamics of spotted owls in Oregon, USA, we evaluated temporal variability of detection, occupancy, local‐extinction, and colonization probabilities for spotted owls, as well as potential influences of barred owl (Strix varia) presence on these parameters. We used spotted owl survey data collected from 1990 to 2003 on a study area in the eastern Cascades Mountains, Washington, USA, to compare competing occupancy models from Program PRESENCE using Akaike's Information Criterion. Detection probabilities for individual spotted owls ranged from 0.54 to 0.80 if barred owls were not detected during the survey season and from 0.19 to 0.71 if barred owls were detected during the survey season. Pair detection probabilities ranged from 0.27 to 0.67 if barred owls were not detected during an individual survey and from 0.09 to 0.36 if barred owls were detected during an individual survey. During the study, site occupancy probabilities for spotted owl pairs declined by approximately 50%. For all spotted owls, both singles and pairs, site occupancy probabilities declined moderately during the study. Barred owl presence was negatively associated with spotted owl detection probabilities, and it had a positive association with local‐extinction probabilities for all spotted owls, both singles and pairs. Given that our study area has supported higher densities of barred owls for longer periods than other study areas, our results may provide insight into how barred owls have influenced spotted owl site occupancy dynamics in adjacent British Columbia, Canada, or will influence spotted owl site occupancy dynamics in Oregon and California, USA, in the future.

Motion-activated cameras are a versatile tool that wildlife biologists can use for sampling wild animal populations to estimate species occurrence. Occupancy modelling provides a flexible framework for the analysis of these data; explicitly recognizing that given a species occupies an area the probability of detecting it is often less than one. Despite the number of studies using camera data in an occupancy framework, there is only limited guidance from the scientific literature about survey design trade-offs when using motion-activated cameras. A fuller understanding of these trade-offs will allow researchers to maximise available resources and determine whether the objectives of a monitoring program or research study are achievable. We use an empirical dataset collected from 40 cameras deployed across 160 km2 of the Western Slope of Colorado, USA to explore how survey effort (number of cameras deployed and the length of sampling period) affects the accuracy and precision (i.e., error) of the occupancy estimate for ten mammal and three virtual species. We do this using a simulation approach where species occupancy and detection parameters were informed by empirical data from motion-activated cameras. A total of 54 survey designs were considered by varying combinations of sites (10–120 cameras) and occasions (20–120 survey days). Our findings demonstrate that increasing total sampling effort generally decreases error associated with the occupancy estimate, but changing the number of sites or sampling duration can have very different results, depending on whether a species is spatially common or rare (occupancy = ψ) and easy or hard to detect when available (detection probability = p). For rare species with a low probability of detection (i.e., raccoon and spotted skunk) the required survey effort includes maximizing the number of sites and the number of survey days, often to a level that may be logistically unrealistic for many studies. For common species with low detection (i.e., bobcat and coyote) the most efficient sampling approach was to increase the number of occasions (survey days). However, for common species that are moderately detectable (i.e., cottontail rabbit and mule deer), occupancy could reliably be estimated with comparatively low numbers of cameras over a short sampling period. We provide general guidelines for reliably estimating occupancy across a range of terrestrial species (rare to common: ψ = 0.175–0.970, and low to moderate detectability: p = 0.003–0.200) using motion-activated cameras. Wildlife researchers/managers with limited knowledge of the relative abundance and likelihood of detection of a particular species can apply these guidelines regardless of location. We emphasize the importance of prior biological knowledge, defined objectives and detailed planning (e.g., simulating different study-design scenarios) for designing effective monitoring programs and research studies.