Foraging habitat selection of shrubland bird community in tropical dry forest

Habitat loss due to increasing anthropogenic disturbance is the major driver for bird population declines across the globe. Within the Eastern Ghats of India, shrubland bird communities are threatened by shrinking of suitable habitats due to increased anthropogenic disturbance and climate change. The development of an effective habitat management strategy is hampered by the absence of data for this bird community. To address this knowledge gap, we examined foraging sites for 14 shrubland bird species, including three declining species, in three study areas representing the shrubland type of forest community in the Eastern Ghats. We recorded microhabitat features within an 11 m radius of observed foraging points and compared these data with similar data from random plots. We used chi-square to test the association between plant species and bird species for sites where they were observed foraging. We observed significant differences between foraging sites of all the study species and random plots, thus indicating selection for foraging habitat. Using linear discriminant analysis, we found that the microhabitat features important for the bird species were shrub density, vegetational height, vertical foliage stratification, grass height, and percent rock cover. Our results show that diet guild and foraging strata influence the foraging microhabitat selection of a species (e.g., ground-foraging species differed significantly from other species). Except for two species, all focal birds were associated with at least one plant species. The plant-bird association was based on foraging, structural, or behavioral preferences. Several key factors affecting foraging habitat such as shrub density can be actively managed at the local scale. Strategic and selective harvesting of forest products and a spatially and temporally controlled livestock grazing regime may allow regeneration of scrubland and create conditions favorable to birds

Climatic and geographic predictors of life history variation in Eastern Massasauga (Sistrurus catenatus): A range-wide synthesis

Elucidating how life history traits vary geographically is important to understanding variation in population dynamics. Because many aspects of ectotherm life history are climate-dependent, geographic variation in climate is expected to have a large impact on population dynamics through effects on annual survival, body size, growth rate, age at first reproduction, size-fecundity relationship, and reproductive frequency. The Eastern Massasauga (Sistrurus catenatus) is a small, imperiled North American rattlesnake with a distribution centered on the Great Lakes region, where lake effects strongly influence local conditions. To address Eastern Massasauga life history data gaps, we compiled data from 47 study sites representing 38 counties across the range. We used multimodel inference and general linear models with geographic coordinates and annual climate normals as explanatory variables to clarify patterns of variation in life history traits. We found strong evidence for geographic variation in six of nine life history variables. Adult female snout-vent length and neonate mass increased with increasing mean annual precipitation. Litter size decreased with increasing mean temperature, and the size-fecundity relationship and growth prior to first hibernation both increased with increasing latitude. The proportion of gravid females also increased with increasing latitude, but this relationship may be the result of geographically varying detection bias. Our results provide insights into ectotherm life history variation and fill critical data gaps, which will inform Eastern Massasauga conservation efforts by improving biological realism for models of population viability and climate change

SNAPSHOT USA 2019 : a coordinated national camera trap survey of the United States

This article is protected by copyright. All rights reserved.With the accelerating pace of global change, it is imperative that we obtain rapid inventories of the status and distribution of wildlife for ecological inferences and conservation planning. To address this challenge, we launched the SNAPSHOT USA project, a collaborative survey of terrestrial wildlife populations using camera traps across the United States. For our first annual survey, we compiled data across all 50 states during a 14-week period (17 August - 24 November of 2019). We sampled wildlife at 1509 camera trap sites from 110 camera trap arrays covering 12 different ecoregions across four development zones. This effort resulted in 166,036 unique detections of 83 species of mammals and 17 species of birds. All images were processed through the Smithsonian's eMammal camera trap data repository and included an expert review phase to ensure taxonomic accuracy of data, resulting in each picture being reviewed at least twice. The results represent a timely and standardized camera trap survey of the USA. All of the 2019 survey data are made available herein. We are currently repeating surveys in fall 2020, opening up the opportunity to other institutions and cooperators to expand coverage of all the urban-wild gradients and ecophysiographic regions of the country. Future data will be available as the database is updated at eMammal.si.edu/snapshot-usa, as well as future data paper submissions. These data will be useful for local and macroecological research including the examination of community assembly, effects of environmental and anthropogenic landscape variables, effects of fragmentation and extinction debt dynamics, as well as species-specific population dynamics and conservation action plans. There are no copyright restrictions; please cite this paper when using the data for publication.Publisher PDFPeer reviewe

Data from: Nine-banded Armadillo (Dasypus novemcinctus) occupancy and density across an urban to rural gradient

<p>The nine-banded Armadillo (<em>Dasypus novemcinctus</em>) is the only species of Armadillo in the United States and alters ecosystems by excavating extensive burrows used by many other wildlife species. Relatively little is known about its habitat use or population densities, particularly in developed areas, which may be key to facilitating its range expansion. We evaluated Armadillo occupancy and density in relation to anthropogenic and landcover variables in the Ozark Mountains of Arkansas along an urban to rural gradient. Armadillo detection probability was best predicted by temperature (positively) and precipitation (negatively). Contrary to expectations, occupancy probability of Armadillos was best predicted by slope (negatively) and elevation (positively) rather than any landcover or anthropogenic variables. Armadillo density varied considerably between sites (ranging from a mean of 4.88 – 46.20 Armadillos per km<sup>2</sup>) but was not associated with any environmental or anthropogenic variables.</p><p>Funding provided by: Arkansas Game and Fish*<br>Crossref Funder Registry ID: <br>Award Number: 1434-04HQRU1567</p><p>Site Selection</p> <p>Our study took place in Northwest Arkansas, USA, in the greater Fayetteville metropolitan area. We deployed trail cameras (Spypoint Force Dark (Spypoint Inc, Victoriaville, Quebec, Canada) and Browning Strikeforce XD cameras (Browning, Morgan, Utah, USA) over the course of two winter seasons, December 2020-March 2021, and November 2021-March 2022. We sampled 10 study sites in year one, and 12 study sites in year two. All study sites were located in the Ozark Mountains ecoregion in Northwest Arkansas. Sites were all Oak Hickory dominated hardwood forests at similar elevation (213.6 – 541 m). Devils Eyebrow and ONSC are public natural areas managed by the Arkansas Natural heritage Commission (ANHC). Devil's Den and Hobbs are managed by the Arkansas state park system. Markham Woods (Markham), Ninestone Land Trust (Ninestone) and Forbes, are all privately owned, though Markham has a publicly accessible trail system throughout the property. Lake Sequoyah, Mt. Sequoyah Woods, Kessler Mountain, Lake Fayetteville, and Millsaps Mountain are all city parks and managed by the city of Fayetteville. Lastly, both Weddington and White Rock are natural areas within Ozark National Forest and managed by the U.S. Forest Service.  We sampled 5 sites in both years of the study including Devils Eyebrow, Markham Hill, Sequoyah Woods, Ozark Natural Science Center (ONSC), and Kessler Mountain. We chose our study sites to represent a gradient of human development, based primarily on Anthropogenic noise values (Buxton et al. 2017, Mennitt and Fristrup 2016). We chose open spaces that were large enough to accommodate camera trap research, as well as representing an array of anthropogenic noise values. Since anthropogenic noise is able to permeate into natural areas within the urban interface, introducing human disturbance that may not be detected by other layers such as impervious surface and housing unit density (Buxton et al. 2017), we used dB values for each site as an indicator of the level of urbanization.</p> <p>Camera Placement</p> <p>We sampled ten study sites in the first winter of the study. At each of the 10 study sites, we deployed anywhere between 5 and 15 cameras. Larger study areas received more cameras than smaller sites because all cameras were deployed a minimum of 150m between one another. We avoided placing cameras on roads, trails, and water sources to artificially bias wildlife detections. We also avoided placing cameras within 15m of trails to avoid detecting humans.</p> <p>At each of the 12 study areas we surveyed in the second winter season, we deployed 12 to 30 cameras. At each study site, we used ArcGIS Pro (Esri Inc, Redlands, CA) to delineate the trail systems and then created a 150m buffer on each side of the trail. We then created random points within these buffered areas to decide where to deploy cameras. Each random point had to occur within the buffered areas and be a minimum of 150m from the next nearest camera point, thus the number of cameras at each site varied based upon site size. We placed all cameras within 50m of the random points to ensure that cameras were deployed on safe topography and with a clear field of view, though cameras were not set in locations that would have increased animal detections (game trails, water sources, burrows etc.). Cameras were rotated between sites after 5 or 10 week intervals to allow us to maximize camera locations with a limited number of trail cameras available to us. Sites with more than 25 cameras were active for 5 consecutive weeks while sites with fewer than 25 cameras were active for 10 consecutive weeks. We placed all cameras on trees or tripods 50cm above ground and at least 15m from trails and roads. We set cameras to take a burst of three photos when triggered. We used Timelapse 2.0 software (Greenberg et al. 2019) to extract metadata (date and time) associated with all animal detections. We manually identified all species occurring in photographs and counted the number of individuals present. Because density estimation requires the calculation of detection rates (number of Armadillo detections divided by the total sampling period), we wanted to reduce double counting individuals. Therefore, we grouped photographs of Armadillos into "episodes" of 5 minutes in length to reduce double counting individuals that repeatedly triggered cameras (DeGregorio et al. 2021, Meek et al. 2014). A 5 min threshold is relatively conservative with evidence that even 1-minute episodes adequately reduces double counting (Meek et al. 2014).</p> <p>Landcover Covariates</p> <p>To evaluate occupancy and density of Armadillos based on environmental and anthropogenic variables, we used ArcGIS Pro to extract variables from 500m buffers placed around each camera (Table 2). This spatial scale has been shown to hold biological meaning for Armadillos and similarly sized species (DeGregorio et al. 2021, Fidino et al. 2016, Gallo et al. 2017, Magle et al. 2016). At each camera, we extracted elevation, slope, and aspect from the base ArcGIS Pro map. We extracted maximum housing unit density (HUD) using the SILVIS housing layer (Radeloff et al. 2018, Table 2). We extracted anthropogenic noise from the layer created by Mennitt and Fristrup (2016, Buxton et al. 2017, Table 2) and used the "L50" anthropogenic sound level estimate, which was calculated by taking the difference between predicted environmental noise and the calculated noise level. Therefore, we assume that higher levels of L50 sound corresponded to higher human presence and activity (i.e. voices, vehicles, and other sources of anthropogenic noise; Mennitt and Fristrup 2016). We derived the area of developed open landcover, forest area, and distance to forest edge from the 2019 National Land Cover Database (NLDC, Dewitz 2021, Table 2). Developed open landcover refers to open spaces with less than 20% impervious surface such as residential lawns, cemeteries, golf courses, and parks and has been shown to be important for medium-sized mammals (Gallo et al. 2017, Poessel et al. 2012). Forest area was calculated by combing all forest types within the NLCD layer (deciduous forest, mixed forest, coniferous forest), and summarizing the total area (km2) within the 500m buffer. Distance to forest edge was derived by creating a 30m buffer on each side of all forest boundaries and calculating the distance from each camera to the nearest forest edge. We calculated distance to water by combining the waterbody and flowline features in the National Hydrogeography Dataset (U.S. Geological Survey) for the state of Arkansas to capture both permanent and ephemeral water sources that may be important to wildlife. We measured the distance to water and distance to forest edge using the geoprocessing tool "near" in ArcGIS Pro which calculates the Euclidean distance between a point and the nearest feature. We extracted Average Daily Traffic (ADT) from the Arkansas Department of Transportation database (Arkansas GIS Office). The maximum value for ADT was calculated using the Summarize Within tool in ArcGIS Pro.</p> <p>We tested for correlation between all covariates using a Spearman correlation matrix and removed any variable with correlation greater than 0.6. Pairwise comparisons between distance to roads and HUD and between distance to forest edge and forest area were both correlated above 0.6; therefore, we dropped distance to roads and distance to forest edge from analyses as we predicted that HUD and forest area would have larger biological impacts on our focal species (Kretser et al. 2008).</p> <p>Occupancy Analysis</p> <p>In order to better understand habitat associations while accounting for imperfect detection of Armadillos, we used occupancy modeling (Mackenzie et al. 2002). We used a single-species, single-season occupancy model (Mackenzie et al. 2002) even though we had two years of survey data at 5 of the study sites. We chose to do this rather than using a multi-season dynamic occupancy model because most sites were not sampled during both years of the study. Even for sites that were sampled in both years, cameras were not placed in the same locations each year. We therefore combined all sampling into one single-season model and created unique site by year combinations as our sampling locations and we used year as a covariate for analysis to explore changes in occupancy associated with the year of study.</p> <p>For each sampling location, we created a detection history with 7 day sampling periods, allowing presence/absence data to be recorded at each site for each week of the study. This allowed for 16 survey periods between 01 December 2020, and 11 March 2021 and 22 survey periods between 01 November 2021 and 24 March 2022. We treated each camera as a unique survey site, resulting in a total of 352 sites. Because not all cameras were deployed at the same time and for the same length of time, we used a staggered entry approach.</p> <p>We used a multi-stage fitting approach in which we used Akaike's Information Criterion (AIC) to select for the best detection covariate. We modeled the survey period (to allow detection to vary over time), year (to evaluate detection across the two years of the study), weekly mean precipitation (to evaluate if precipitation influenced Armadillo activity and thus detection), and weekly mean temperature (to evaluate if temperature influenced Armadillo activity and thus detection) as covariates for detection against null occupancy parameters and selected the top covariate model with lowest AIC score. The top-ranked detection covariate(s) was then used in all subsequent analyses of occupancy. We acquired temperature and precipitation data from the NOAA weather station closest to each site for each detection date.</p> <p>For occupancy covariates, we used distance to the nearest water source, distance to the nearest road, elevation, slope, aspect, maximum ADT, maximum anthropogenic noise, developed open space, area of forest, and maximum housing unit density. We then evaluated all single variable models using an AIC approach with an apriori cutoff of 2 ∆AIC (Burnham and Anderson 2002).</p> <p>Density Estimation</p> <p>To calculate Armadillo density at each of the study sites, we used the Random Encounter Model (REM). The REM was developed to estimate density of unmarked animals through camera trap data (Rowcliffe et al. 2008). The three assumptions of the REM are: 1) that animals move randomly throughout their environment and thus cameras are not set on any features that might increase their detection probability (e.g., trails, roads, bait etc.), 2) detection episodes are of individual animals, and 3) that the study population is closed (Rowcliffe et al. 2008). We used the REM equation to calculate Armadillo density at each camera using Microsoft Excel (Microsoft corporation). </p> <p>In the REM, the y represents the total detections of Armadillo at each camera. Total trap nights in hours (the measure of trapping effort) is represented by t. V refers to the day range (how far an individual travels in a 24-hour period) of the Armadillo. We derived a mean day range for Armadillo from day ranges reported in the literature (Table 3). Detection radius (r) and detection angle (θ) were measured at each camera in the field through walk tests. The walk tests involved walking directly towards each camera to calculate the detection radius and from each side at 5m from the camera to calculate the detection angle in degrees. Detection was determined by whether or not the detection light on the camera was triggered during the walk test. We then converted the detection angle to radians for density calculations (Caravaggi et al. 2015, Rowcliffe et al. 2008). We were not able to calculate the detection angle and radius at 14 of the cameras due to camera malfunction (no detection light during walk test), and so we used the average detection angle for the given camera model (Schaus et al. 2020).</p> <p>We evaluated if Armadillo density correlated to anthropogenic or landcover variables. We used linear models in R, using the packages "lme4" and "AIC modavg". We only included data from the second year of sampling in our density calculations as detection radius and angle were not collected during the first year of the study. We evaluated Armadillo density against HUD, anthropogenic noise, distance to water, forest area, development, and ADT. We modeled all single- and two-way combinations of these variables. However, we did not include ADT and HUD in the same models due to high correlation between these covariates. Thus, we evaluated 22 candidate models including the null and global models. In each model, we included site as a random effect. We considered models within 2 ∆AIC to be competitive (Burnham and Anderson 2002).</p&gt

Wildlife associates of nine-banded armadillo (Dasypus novemcinctus) burrows in Arkansas

The Nine-banded Armadillo (Dasypus novemcinctus) is a widespread burrowing species with an expanding geographic range across the southeastern and midwestern United States. Armadillos dig numerous, large burrows within their home ranges and these burrows are likely used by a diverse suite of wildlife species as has been reported for other burrowing ecosystem engineers such as Gopher Tortoises (Gopherus polyphemus), Desert Tortoises (Gopherus agassizi), and Black-tailed Prairie Dogs (Cynomys ludovicianus). We used motion-triggered game cameras at 35 armadillo burrows in 4 ecoregions of Arkansas and documented 19 species of mammals, 4 species of reptile, 1 species of amphibian, and 40 species of bird interacting with burrows. Bobcat (Lynx rufus), Coyote (Canis latrans), Eastern Cottontail (Sylvilagus floridanus), Gray Fox (Urocyon cinereoargenteus), Gray Squirrel (Sciurus carolinensis), Northern Raccoon (Procyon lotor), Virginia Opossum (Didelphis virginiana), and unidentified rodents (mice and rats) were documented using burrows in all four ecoregions. We documented wildlife hunting, seeking shelter, rearing young in, and taking over and modifying armadillo burrows. The rate of use was highest in the Mississippi Alluvial Valley, a landscape dominated by agriculture, where natural refugia may be limited and rodents are abundant. Armadillo burrows are clearly visited and used by numerous wildlife species to fulfill various life stage requirements, and this list will likely expand if more attention is devoted to understanding the role of armadillos burrows. Armadillos are important ecosystem engineers, and their ecological role warrants more investigation and attention as opposed to only being viewed and managed as agricultural and garden pests

Tradeoffs with Growth and Behavior for Captive Box Turtles Head-Started with Environmental Enrichment

Head-starting is a conservation strategy that entails releasing captive-reared animals into nature at sizes large enough to better resist post-release predation. However, efforts to maximize growth in captivity may jeopardize development of beneficial behaviors. Environmental enrichment can encourage natural behaviors before release but potentially comes with a tradeoff of reduced growth in complex enclosures. We compared growth and behavior of enriched and unenriched captive-born juvenile box turtles (Terrapene carolina). Enriched turtles grew slower than unenriched turtles during the first eight months in captivity, although growth rates did not differ between treatments from 9–20 months old. After five months post-hatching, unenriched turtles became and remained larger overall than enriched turtles. During two foraging tasks, unenriched turtles consumed more novel prey than enriched turtles. In a predator recognition test, eight-month-old enriched turtles avoided raccoon (Procyon lotor) urine more than unenriched turtles of the same age, but this difference was not apparent one year later. The odds of turtles emerging from a shelter did not differ between treatments regardless of age. Although our results suggest turtles raised in unenriched environments initially grew faster and obtained larger overall sizes than those in enriched conditions, tradeoffs with ecologically-relevant behaviors were either absent or conditional