Predicted distribution of plains spotted skunk in Arkansas and Missouri

The plains spotted skunk (Spilogale interrupta) is of conservation concern because of widespread population declines and is being considered for listing under the United States Endangered Species Act. Although the taxon is relatively rare and difficult to study, recent research and reports provided some information about spotted skunks in Arkansas and Missouri, USA. Using this information, we employed full and simplified multivariate species distribution models to evaluate cover type associations and identify regions of high predicted presence for plains spotted skunks. The simple model contained percent forest and percent development within 5 km, land cover category, and distance to water. Percent forest within 5 km was the most important variable based on permutation importance in both models, indicating that plains spotted skunk habitat may persist in contiguous forest at the landscape scale. Regions predicted to have high presence occurred in northern, western, and southern Arkansas and southern Missouri, totaling >300,000 ha. The resulting plains spotted skunk distribution map can be used for research and management efforts in areas of high probability of occurrence, and future statewide survey efforts may validate our results.     

Estimating site occupancy rates for aquatic plants using spatial sub-sampling designs when detection probabilities are less than one

Estimation of site occupancy rates when detection probabilities are <1 is well established in wildlife science. Data from multiple visits to a sample of sites are used to estimate detection probabilities and the proportion of sites occupied by focal species. In this article we describe how site occupancy methods can be applied to estimate occupancy rates of plants and other sessile organisms. We illustrate this approach and the pitfalls of ignoring incomplete detection using spatial data for 2 aquatic vascular plants collected under the Upper Mississippi River's Long Term Resource Monitoring Program (LTRMP). Site occupancy models considered include: a naïve model that ignores incomplete detection, a simple site occupancy model assuming a constant occupancy rate and a constant probability of detection across sites, several models that allow site occupancy rates and probabilities of detection to vary with habitat characteristics, and mixture models that allow for unexplained variation in detection probabilities. We used information theoretic methods to rank competing models and bootstrapping to evaluate the goodness-of-fit of the final models. Results of our analysis confirm that ignoring incomplete detection can result in biased estimates of occupancy rates. Estimates of site occupancy rates for 2 aquatic plant species were 19–36% higher compared to naive estimates that ignored probabilities of detection <1. Simulations indicate that final models have little bias when 50 or more sites are sampled, and little gains in precision could be expected for sample sizes >300. We recommend applying site occupancy methods for monitoring presence of aquatic species.     

占域模型的原理及在野生动物红外相机研究中的应用案例

占域模型解决了探测率常常被忽略的问题, 所需数据要求简单, 具有数据来源广泛、经济高效等优点, 越来越多地应用在野生动物监测和研究中。本文综述了占域模型的概念、基本原理及其近年来在国内外的应用进展, 并以红外相机数据为基础, 总结了占域模型所要求的数据采集、处理和分析等流程和基本原则, 同时以广东车八岭国家级自然保护区的花面狸(Paguma larvata)作为研究案例, 展示了占域模型的数据分析过程。最后根据近年来的发展和应用, 总结了占域模型的应用范围和发展趋势以及研究中存在的主要问题。     

Spatial and temporal patterns in age structure of Golden Eagles wintering in eastern North America

The behavior of wildlife varies seasonally, and that variation can have substantial demographic consequences. This is especially true for long-distance migrants where the use of landscapes varies by season and, sometimes, age cohort. We tested the hypothesis that distributional patterns of Golden Eagles (Aquila chrysaetos) wintering in eastern North America are age-structured (i.e., birds of similar ages winter together) through the analysis of 370,307 images collected by motion-sensitive trail cameras set over bait during the winters of 2012–2013 and 2013–2014. At nine sites with sufficient data for analysis, we documented 145 eagle visits in 2012–2013 and 146 in 2013–2014. We found significant between-year variation in age structure of wintering eastern Golden Eagles, driven largely by annual differences in the proportion of first-winter birds. However, although many other species show spatial structure in wintering behavior, our analysis revealed no latitudinal organization among age cohorts of wintering eastern Golden Eagles. The lack of age-related latitudinal segregation in wintering behavior does not exclude the possibility that these eagles have sex-based or other types of dominance hierarchies that could result in spatial or temporal segregation. Alternatively, other mechanisms such as food availability or habitat structure may determine the distribution and abundance of Golden Eagles in winter.     

Effects of point‐count duration on estimated detection probabilities and occupancy of breeding birds

Increasingly, point‐count data are used to estimate occupancy, the probability that a species is present at a given location; occupancy accounts for imperfect detection, the probability that a species is detected given that it is present. To our knowledge, effects of sampling duration on inferences from models of bird occupancy have not been evaluated. Our objective was to determine whether changing count duration from 5 to 8 min affected inferences about the occupancy of birds sampled in the Chesapeake Bay Lowlands (eastern United States) and the central and western Great Basin (western United States) in 2012 and 2013. We examined the proportion of species (two doves, one cuckoo, two swifts, five hummingbirds, 11 woodpeckers, and 122 passerines) for which estimates of detection probability were ≥ 0.3. For species with single‐season detection probabilities ≥ 0.3, we compared occupancy estimates derived from 5‐ and 8‐min counts. We also compared estimates for three species sampled annually for 5 yr in the central Great Basin. Detection probabilities based on both the 5‐ and 8‐min counts were ≥ 0.3 for 40% ± 3% of the species in an ecosystem. Extending the count duration from 5 to 8 min increased the detection probability to ≥ 0.3 for 5% ± 0.5% of the species. We found no difference in occupancy estimates that were based on 5‐ versus 8‐min counts for species sampled over two or five consecutive years. However, for 97% of species sampled over 2 yr, precision of occupancy estimates that were based on 8‐min counts averaged 12% ± 2% higher than those based on 5‐min counts. We suggest that it may be worthwhile to conduct a pilot season to determine the number of locations and surveys needed to achieve detection probabilities that are sufficiently high to estimate occupancy for species of interest.     

红外相机技术在我国野生动物监测中的应用: 问题与限制

红外相机(camera traps)作为对野生动物进行“非损伤”性采样的技术, 已成为研究动物多样性、种群生态学及行为学的常用手段之一。其发展和普及为中国野生动物多样性和物种保育研究带来了诸多机会。如今, 国内大多数自然保护区都在运用红外相机技术开展物种监测工作。本文结合20年来已发表的相关研究, 从内容、实验设计以及发展趋势方面, 总结了目前红外相机技术在应用过程中出现的共性问题; 并就相机对动物的干扰性、影像识别、研究的适用范围及安全保障四个方面, 对该项技术在实践中存在的限制进行了探讨。最后结合红外相机技术未来的发展方向, 提出了建立技术规范、数据集成和共享、影像数据版权维护、提高监测效率等问题。     

Evaluating nest supplementation as a recovery strategy for the endangered rodents of the Florida Keys

The Key Largo woodrat (Neotoma floridana smalli) and Key Largo cotton mouse (Peromyscus gossypinus allapaticola) are federally endangered subspecies endemic to the tropical hardwood hammocks of Key Largo, Florida. Woodrats are considered generalists in habitat and diet, yet a steady decline in natural stick nests and capture rates over the past several decades suggests that they are limited by the availability of nesting habitat due to habitat loss and fragmentation. The more specialized Key Largo cotton mouse appears to rely on old growth hammock, a habitat type that is rare following past land clearing. In 2004, the U.S. Fish and Wildlife Service started building supplemental nest structures to restore habitat quality and connectivity for these endangered rodents, but nest use requires evaluation. We used camera traps and occupancy models to evaluate the factors influencing woodrat and cotton mouse use of the supplemental nests. We detected woodrats at 65 and cotton mice at 175 of 284 sampled nest structures, with co‐occurrence at 38 nests. Woodrat nest use followed a gradient from low nest use in the north to high nest use in the south, which might relate to the proximity of free‐ranging domestic cat (Felis catus) colonies in residential developments. Cotton mouse nest use, however, was related positively to mature hammock and related negatively to disturbed areas (e.g. scarified lands). The two species occurred independently of each other. Stick‐stacking behavior was observed at supplemental nests and, although it was correlated with detection of woodrats, it was not a strong predictor of their occurrence. We suggest that nest supplementation can be an important tool for species recovery as habitat quality continues to improve with succession.     

野生动物监测技术和方法应用进展与展望

野生动物是生态系统研究和保护管理的重要生物类群, 对调控生态系统结构和功能, 维持生态系统健康平衡具有显著作用。科学监测数据是野生动物研究、保护、管理决策的前提基础, 但由于传统监测技术的限制, 野生动物的多样性特征及其与环境、生态系统的平衡机制未得到充分关注和研究。随着自动化、智能化、信息化技术的发展及应用, 野生动物监测技术和方法出现较大突破和变革。该文论述了近年来野生动物监测研究领域的重要新技术, 包括红外相机技术、全球定位系统(GPS)追踪技术、DNA条形码技术和高通量测序技术等。通过综合介绍相关的基础概念、基本原理, 该文总结了新技术的应用优势和重大应用进展, 同时探讨了新技术应用中存在的问题, 并对未来野生动物监测技术的发展趋势进行了展望。     

Habitat Selection and Home Range Dynamics of Eastern Spotted Skunks in the Ouachita Mountains,Arkansas, USA

ABSTRACT Since the 1940s, eastern spotted skunks (Spilogale putorius) have declined dramatically throughout the Midwest. One hypothesis for the decline is the loss of suitable habitat, although little is known about the ecological requirements of this species. To elucidate seasonal home range and habitat selection by eastern spotted skunks, we conducted telemetry-based field work in the Ouachita Mountains of western Arkansas, USA. During 2 years of field work, we collected day- and nighttime radiolocations for 33 eastern spotted skunks. We used kernel-based utilization distributions, volume of intersection indices, and weighted compositional analysis to evaluate seasonal home range dynamics and habitat selection. Although we found moderate adult male site fidelity, there were large seasonal differences in home range size, with ranges of between 76 ha and 175 ha (± 22–62 SE) during summer, fall, and winter, and home ranges of 866 ha (± 235 SE) during spring. Male home range increases in the spring were likely caused by questing behavior in search of reproductive females. Females maintained home ranges of 54 ha to 135 ha (± 7–30 SE) and moderate site fidelity during all seasons. During each season, we observed selection of young shortleaf pine (Pinus echinata) and hardwood stands over other available cover types, likely due to a preference for a dense, complex understory and a closed canopy overstory to reduce predation risk. Most habitats in the study region were managed for an herbaceous understory and an older, more open canopy, in part to benefit red-cockaded woodpecker (Picoides borealis) populations. Thus, if simultaneous management for these 2 vertebrates is a goal, a balance of early and late successional habitat should be reached.     

Density‐dependent space use affects interpretation of camera trap detection rates

Camera traps (CTs) are an increasingly popular tool for wildlife survey and monitoring. Estimating relative abundance in unmarked species is often done using detection rate as an index of relative abundance, which assumes that detection rate has a positive linear relationship with true abundance. This assumption may be violated if movement behavior varies with density, but the degree to which movement behavior is density‐dependent across taxa is unclear. The potential confounding of population‐level relative abundance indices by movement would depend on how regularly, and by what magnitude, movement rate and home‐range size vary with density. We conducted a systematic review and meta‐analysis to quantify relationships between movement rate, home‐range size, and density, across terrestrial mammalian taxa. We then simulated animal movements and CT sampling to test the effect of contrasting movement scenarios on CT detection rate indices. Overall, movement rate and home‐range size were negatively correlated with density and positively correlated with one another. The strength of the relationships varied significantly between taxa and populations. In simulations, detection rates were related to true abundance but underestimated change, particularly for slower moving species with small home ranges. In situations where animal space use changes markedly with density, we estimate that up to thirty percent of a true change in relative abundance may be missed due to the confounding effect of movement, making trend estimation more difficult. The common assumption that movement remains constant across densities is therefore violated across a wide range of mammal species. When studying unmarked species using CT detection rates, researchers and managers should explicitly consider that such indices of relative abundance reflect both density and movement. Practitioners interpreting changes in camera detection rates should be aware that observed differences may be biased low relative to true changes in abundance. Further information on animal movement, or methods that do not depend on assumptions of density‐independent movement, may be required to make robust inferences on population trends.     

Automated location invariant animal detection in camera trap images using publicly available data sources

1. A time‐consuming challenge faced by camera trap practitioners is the extraction of meaningful data from images to inform ecological management. An increasingly popular solution is automated image classification software. However, most solutions are not sufficiently robust to be deployed on a large scale due to lack of location invariance when transferring models between sites. This prevents optimal use of ecological data resulting in significant expenditure of time and resources to annotate and retrain deep learning models.
2. We present a method ecologists can use to develop optimized location invariant camera trap object detectors by (a) evaluating publicly available image datasets characterized by high intradataset variability in training deep learning models for camera trap object detection and (b) using small subsets of camera trap images to optimize models for high accuracy domain‐specific applications.
3. We collected and annotated three datasets of images of striped hyena, rhinoceros, and pigs, from the image‐sharing websites FlickR and iNaturalist (FiN), to train three object detection models. We compared the performance of these models to that of three models trained on the Wildlife Conservation Society and Camera CATalogue datasets, when tested on out‐of‐sample Snapshot Serengeti datasets. We then increased FiN model robustness by infusing small subsets of camera trap images into training.
4. In all experiments, the mean Average Precision (mAP) of the FiN trained models was significantly higher (82.33%–88.59%) than that achieved by the models trained only on camera trap datasets (38.5%–66.74%). Infusion further improved mAP by 1.78%–32.08%.
5. Ecologists can use FiN images for training deep learning object detection solutions for camera trap image processing to develop location invariant, robust, out‐of‐the‐box software. Models can be further optimized by infusion of 5%–10% camera trap images into training data. This would allow AI technologies to be deployed on a large scale in ecological applications. Datasets and code related to this study are open source and available on this repository: https://doi.org/10.5061/dryad.1c59zw3tx.

     

Designing occupancy surveys and interpreting non‐detection when observations are imperfect

Aim Conservation practitioners use biological surveys to ascertain whether or not a site is occupied by a particular species. Widely used statistical methods estimate the probability that a species will be detected in a survey of an occupied site. However, these estimates of detection probability are alone not sufficient to calculate the probability that a species is present given that it was not detected. The aim of this paper is to demonstrate methods for correctly calculating (1) the probability a species occupies a site given one or more non‐detections, and (2) the number of sequential non‐detections necessary to assert, with a pre‐specified confidence, that a species is absent from a site. Location Occupancy data for a tree frog in eastern Australia serve to illustrate methods that may be applied anywhere species’ occupancy data are used and detection probabilities are < 1. Methods Building on Bayesian expressions for the probability that a site is occupied by a species when it is not detected, and the number of non‐detections necessary to assert absence with a pre‐specified confidence, we estimate occupancy probabilities across tree frog survey locations, drawing on information about where and when the species was detected during surveys. Results We show that the number of sequential non‐detections necessary to assert that a species is absent increases nonlinearly with the prior probability of occupancy, the probability of detection if present, and the desired level of confidence about absence. Main conclusions If used more widely, the Bayesian analytical approaches illustrated here would improve collection and interpretation of biological survey data, providing a coherent way to incorporate detection probability estimates in the design of minimum survey requirements for monitoring, impact assessment and distribution modelling.     

A tiger cannot change its stripes: using a three-dimensional model to match images of living tigers and tiger skins

The tiger is one of many species in which individuals can be identified by surface patterns. Camera traps can be used to record individual tigers moving over an array of locations and provide data for monitoring and studying populations and devising conservation strategies. We suggest using a combination of algorithms to calculate similarity scores between pattern samples scanned from the images to automate the search for a match to a new image. We show how using a three-dimensional surface model of a tiger to scan the pattern samples allows comparison of images that differ widely in camera angles and body posture. The software, which is free to download, considerably reduces the effort required to maintain an image catalogue and we suggest it could be used to trace the origin of a tiger skin by searching a central database of living tigers'' images for matches to an image of the skin.     

Abundance,density and activity of Salvator merianae (Reptilia: Teiidae) and the effect of poaching on the site occupancy by the lizard in an Atlantic Forest Reserve,Brazil

We aimed to estimate the density, occupancy and detectability of Salvator merianae (Tegu) in one of the largest Atlantic rainforest remnants in Espírito Santo, Brazil, the VNR. Species patch occupancy was modelled and used to predict the response direction of six covariates based on prior knowledge of the Tegu's ecology. A priori, we expected that the covariates measured should represent key habitat features for the species (i.e. temperature, forest edge, open habitats) or elements possibly avoided by the species, based on the hypothesis that poaching would have a negative effect on patch occupancy. We used line‐transect surveys to estimate density and abundance. Camera‐traps were used to estimate patch occupancy by the Tegu. Estimated density for S. merianae was 0.21 ± 0.02 Tegus/ha and estimated population size was 4990 ± 521 individuals. Patch occupancy was best described by two covariates: poaching intensity and distance to the forest edge. Detectability was affected by three covariates: poaching intensity, tree density and temperature. Our study presents robust information on abundance and density, habitat use, and activity of S. merianae in the VNR and is the first study providing data on the effects that poaching has on patch occupancy of this lizard. The data indicated that the occupancy and detectability of this species were influenced by a set of factors, providing information that can be useful in management plans in areas where this species can potentially decline and in areas where it may be introduced.     

完达山东部欧亚水獭种群数量及出现频次影响因素

水獭作为淡水生态系统健康的指示种和旗舰物种,在维持水生生态系统平衡与稳定中发挥着重要作用。然而目前对于完达山东部地区水獭种群数量、分布及其生境选择的影响因素的研究较为匮乏,严重影响了对该物种的野外保护与管理工作。于2021年12月至2022年4月冬季河流封冻期,采用沿河随机样线调查和红外相机监测相结合的方法对完达山东部地区8条主河和23条支流内的欧亚水獭(Lutra lutra)种群数量、分布现状进行了调查,并利用广义可加模型探究水獭出现频次与环境因子的关系。研究结果表明:(1)欧亚水獭在完达山东部水獭种群数量为571-661只,水獭种群密度为沿河(0.5559±0.2898)只/km,呈现中间高,四周低的趋势;(2)环境因子对水獭出现频次影响分析表明,河流深度、距农田距离、距居民区距离和距道路距离是影响水獭出现频次的关键因素。水獭出现频次与河流深度(0-40 cm)呈线性正相关,当河流深度达到40-50 cm时,水獭出现频次最高,之后随着河流深度的增加,出现频次降低;水獭出现频次与距农田距离(0-1.5 km)呈非线性正相关,在距农田距离为1.5-2 km范围内,水獭出现频次最高,之后降低;水獭出现频次与距居民区距离呈线性正相关;水獭出现频次与距道路距离呈非线性关系,当距离>7 km时,二者之间呈现为正相关,反之呈现为负相关。因此,水獭选择栖息地偏向于河流深(40-50 cm),远离农田(1.5-2 km)、居民区和道路(>7 km)的水域。研究为完达山东部欧亚水獭物种保护提供了基础数据和理论依据,建议通过河岸土地覆盖类型的管理、在河岸周围建立森林缓冲区及加强水獭保护宣传力度等措施实现对水獭物种及其栖息地的维持和保护。