Abundance Of Wildlife Populations Research Articles

Factors affecting spatiotemporal patterns of nest site selection and abundance in diamondback terrapins.

Determining what factors influence the distribution and abundance of wildlife populations is crucial for implementing effective conservation and management actions. Yet, for species with dynamic seasonal, sex-, and age-specific spatial ecology, like the diamondback terrapin (Malaclemys terrapin; DBT), doing so can be challenging. Moreover, environmental factors that influence the distribution and abundance of DBT in their northernmost range have not been quantitatively characterized. We investigated proximity to nesting habitat as one potential driver of spatiotemporal variation in abundance in a three-step analytical approach. First, we used a scale selection resource selection function (RSF) approach based on landcover data from the National Landcover Database (NLCD) to identify the scale at which DBT are selecting for (or avoiding) landcover types to nest. Next, we used RSF to predict areas of suitable nesting habitat and created an index of nest suitability (NSI). Finally, analyzing visual count data using a generalized linear mixed model (GLMM), we investigate spatiotemporal drivers of relative abundance, with a specific focus on whether similar factors affect offshore abundance and onshore nest site selection. We found the scale of selection for developed and saltmarsh land use classes to be 550 and 600 m and open water land use classes to be 100. Selection was positive for nesting areas proximal to saltmarsh habitat and negative for developed and open water. Expected relative abundance was best explained by the interaction between NSI and day of season, where expected relative abundance was greater within high NSI areas during the nesting season (2.20 individuals, CI: 1.19-3.93) compared to areas of low NSI (1.84 individuals, CI: 1.10-3.10). Our results provide evidence that inferred spatial patterns of suitable nesting habitats explain spatiotemporal patterns of terrapin movement and abundance.

From species detection to population size indexing: the use of sign surveys for monitoring a rare and otherwise elusive small mammal

Monitoring the occupancy and abundance of wildlife populations is key to evaluate their conservation status and trends. However, estimating these parameters often involves time and resource-intensive techniques, which are logistically challenging or even unfeasible for rare and elusive species that occur patchily and in small numbers. Hence, surveys based on field identification of signs (e.g. faeces, footprints) have long been considered a cost-effective alternative in wildlife monitoring, provided they produce reliable detectability and meaningful indices of population abundance. We tested the use of sign surveys for monitoring rare and otherwise elusive small mammals, focusing on the Cabrera vole (Microtus cabrerae) in Portugal. We asked how sampling intensity affects true positive detection of the species, and whether sign abundance is related to population size. We surveyed Cabrera voles’ latrines in 20 habitat patches known to be occupied, and estimated ‘true’ population size at each patch using DNA-based capture-recapture techniques. We found that a searching rate of ca. 3 min/250m2 of habitat based on adaptive guided transects was sufficient to provide true positive detection probabilities > 0.85. Sign-based abundance indices were at best moderately correlated with estimates of ‘true’ population size, and even so only for searching rates > 12 min/250m2. Our study suggests that surveys based on field identification of signs should provide a reliable option to estimate occupancy of Cabrera voles, and possibly for other rare or elusive small mammals, but cautions should be exercised when using this approach to infer population size. In case of practical constraints to the use of more accurate methods, a considerable sampling intensity is needed to reliably index Cabrera voles’ abundance from sign surveys.

Joint analysis of structured and semi-structured community science data improves precision of relative abundance but not trends in birds

Estimating absolute and relative abundance of wildlife populations is critical to addressing ecological questions and conservation needs, yet obtaining reliable estimates can be challenging because surveys are often limited spatially or temporally. Community science (i.e., citizen science) provides opportunities for semi-structured data collected by the public (e.g., eBird) to improve capacity of relative abundance estimation by complementing structured survey data collected by trained observers (e.g., North American breeding bird survey [BBS]). We developed two state-space models to estimate relative abundance and population trends: one using BBS data and the other jointly analyzing BBS and eBird data. We applied these models to seven bird species with diverse life history characteristics. Joint analysis of eBird and BBS data improved precision of mean and year-specific relative abundance estimates for all species, but the BBS-only model produced more precise trend estimates compared to the joint model for most species. The relative abundance estimates of the joint model were particularly more precise than the BBS-only estimates in areas where species detectability was low resulting from either low BBS survey effort or low abundance. These results suggest that community science data can be a valuable resource for cost-effective improvement in wildlife abundance estimation.

Using transcriptomics to predict and visualize disease status in bighorn sheep (Ovis canadensis).

Increasing risk of pathogen spillover coupled with overall declines in wildlife population abundance in the Anthropocene make infectious disease a relevant concern for species conservation worldwide. While emerging molecular tools could improve our diagnostic capabilities and give insight into mechanisms underlying wildlife disease risk, they have rarely been applied in practice. Here, employing a previously reported gene transcription panel of common immune markers to track physiological changes, we present a detailed analysis over the course of both acute and chronic infection in one wildlife species where disease plays a critical role in conservation, bighorn sheep (Ovis canadensis). Differential gene transcription patterns distinguished between infection statuses over the course of acute infection and differential correlation (DC) analyses identified clear changes in gene co-transcription patterns over the early stages of infection, with transcription of four genes-TGFb, AHR, IL1b and MX1-continuing to increase even as transcription of other immune-associated genes waned. In a separate analysis, we considered the capacity of the same gene transcription panel to aid in differentiating between chronically infected animals and animals in other disease states outside of acute disease events (an immediate priority for wildlife management in this system). We found that this transcription panel was capable of accurately identifying chronically infected animals in the test dataset, though additional data will be required to determine how far this ability extends. Taken together, our results showcase the successful proof of concept and breadth of potential utilities that gene transcription might provide to wildlife disease management, from direct insight into mechanisms associated with differential disease response to improved diagnostic capacity in the field.

Congruence of local ecological knowledge (LEK)‐based methods and line‐transect surveys in estimating wildlife abundance in tropical forests

Abstract Effective estimation of wildlife population abundance is an important component of population monitoring, and ultimately essential for the development of conservation actions. Diurnal line‐transect surveys are one of the most applied methods for abundance estimations. Local ecological knowledge (LEK) is empirically acquired through the observation of ecological processes by local people. LEK‐based methods have only been recognized as valid scientific methods for surveying fauna abundance in the last three decades. However, the agreement between both methods has not been extensively analysed. We compared concomitant abundance data for 91 wild species (mammals, birds and tortoises) from diurnal line transects (9,221 km of trails) and a LEK‐based method (291 structured interviews) at 18 sites in Central and Western Amazonia. We used biological and socioecological factors to assess the agreements and divergences between abundance indices obtained from both methods. We found a significant agreement of population abundance indices for diurnal and game species. This relationship was also positive regardless of species sociality (solitary or social), body size and locomotion mode (terrestrial and arboreal); and of sampled forest type (upland and flooded forests). Conversely, we did not find significant abundance covariances for nocturnal and non‐game species. Despite the general agreement between methods, line transects were not effective at surveying many species occurring in the area, with 40.2% and 39.8% of all species being rarely and never detected in at least one of the survey sites. On the other hand, these species were widely reported by local informants to occur at intermediate to high abundances. Although LEK‐based methods have been long neglected by ecologists, our comparative study demonstrated their effectiveness for estimating vertebrate abundance of a wide diversity of taxa and forest environments. This can be used simultaneously with line‐transect surveys to calibrate abundance estimates and record species that are rarely sighted during surveys on foot, but that are often observed by local people during their daily extractive activities. Thus, the combination of local and scientific knowledge is a potential tool to improve our knowledge of tropical forest species and foster the development of effective strategies to meet biodiversity conservation goals.

Quantifying the causes and consequences of variation in satellite‐derived population indices: a case study of emperor penguins

AbstractVery high‐resolution satellite (VHR) imagery is a promising tool for estimating the abundance of wildlife populations, especially in remote regions where traditional surveys are limited by logistical challenges. Emperor penguins Aptenodytes forsteri were the first species to have a circumpolar population estimate derived via VHR imagery. Here we address an untested assumption from Fretwell et al. (2012) that a single image of an emperor penguin colony is a reasonable representation of the colony for the year the image was taken. We evaluated satellite‐related and environmental variables that might influence the calculated area of penguin pixels to reduce uncertainties in satellite‐based estimates of emperor penguin populations in the future. We focused our analysis on multiple VHR images from three representative colonies: Atka Bay, Stancomb‐Wills (Weddell Sea sector) and Coulman Island (Ross Sea sector) between September and December during 2011. We replicated methods in Fretwell et al. (2012), which included using supervised classification tools in ArcGIS 10.7 software to calculate area occupied by penguins (hereafter referred to as ‘population indices’) in each image. We found that population indices varied from 2 to nearly 6‐fold, suggesting that penguin pixel areas calculated from a single image may not provide a complete understanding of colony size for that year. Thus, we further highlight the important roles of: (i) sun azimuth and elevation through image resolution and (ii) penguin patchiness (aggregated vs. distributed) on the calculated areas. We found an effect of wind and temperature on penguin patchiness. Despite intra‐seasonal variability in population indices, simulations indicate that reliable, robust population trends are possible by including satellite‐related and environmental covariates and aggregating indices across time and space. Our work provides additional parameters that should be included in future models of population size for emperor penguins.

Estimating Abundance of an Unmarked, Low‐Density Species using Cameras

ABSTRACTEstimating abundance of wildlife populations can be challenging and costly, especially for species that are difficult to detect and that live at low densities, such as cougars (Puma concolor). Remote, motion‐sensitive cameras are a relatively efficient monitoring tool, but most abundance estimation techniques using remote cameras rely on some or all of the population being uniquely identifiable. Recently developed methods estimate abundance from encounter rates with remote cameras and do not require identifiable individuals. We used 2 methods, the time‐to‐event and space‐to‐event models, to estimate the density of 2 cougar populations in Idaho, USA, over 3 winters from 2016–2019. We concurrently estimated cougar density using the random encounter model (REM), an existing camera‐based method for unmarked populations, and genetic spatial capture recapture (SCR), an established method for monitoring cougar populations. In surveys for which we successfully estimated density using the SCR model, the time‐to‐event estimates were more precise and showed comparable variation between survey years. The space‐to‐event estimates were less precise than the SCR estimates and were more variable between survey years. Compared to REM, time‐to‐event was more precise and consistent, and space‐to‐event was less precise and consistent. Low sample sizes made the space‐to‐event and SCR models inconsistent from survey to survey, and non‐random camera placement may have biased both of the camera‐based estimators high. We show that camera‐based estimators can perform comparably to existing methods for estimating abundance in unmarked species that live at low densities. With the time‐ and space‐to‐event models, managers could use remote cameras to monitor populations of multiple species at broader spatial and temporal scales than existing methods allow. © 2020 The Wildlife Society.

Declining Ungulate Populations in an African Rainforest: Evidence From Local Knowledge, Ecological Surveys, and Bushmeat Records

Human activities alter the composition and abundance of wildlife populations. A common awareness of these changes by stakeholders, including local people is essential for sustainable management. Hunters (n= 255) from two adjacent community hunting zones (CHZs) in Southeast Cameroon were interviewed about the species composition of the animals caught in snare traps during their beginnings in hunting and in 2015- 2016. The comparison of the trends in game composition to those from transect surveys and bushmeat records conducted in the area since the nineties revealed the followings: (1) hunters are aware of the coarse changes in prey abundance, even for the species for which population density is difficult to estimate using classic survey methods; (2) in southeast Cameroon and in forests regions with similar fauna, the bay duiker and the Peter’s duiker are clearly more abundant than the white-bellied and black fronted duiker; (3) the two sites surveyed are under different stages of prey depletion trajectory, and (4) the ranking of prey abundance by local hunters is consistent with village-based bushmeat records and is likely to reflect more the species compositions in anthropogenic forest mosaics, where hunting pressure is higher. Hunters’ interviews constitute a valuable means to rapidly assess the trends in animal populations. However, the discrepancies between perceptions and prey composition in remote forest areas draw attention to the need of caution when using local knowledge to generalize trends in fauna assemblages over large geographical scales.

A Comparison of the Population Genetic Structure and Diversity between a Common (Chrysemys p. picta) and an Endangered (Clemmys guttata) Freshwater Turtle

The northeastern United States has experienced dramatic alteration to its landscape since the time of European settlement. This alteration has had major impacts on the distribution and abundance of wildlife populations, but the legacy of this landscape change remains largely unexplored for most species of freshwater turtles. We used microsatellite markers to characterize and compare the population genetic structure and diversity between an abundant generalist, the eastern painted turtle (Chrysemys p. picta), and the rare, more specialized, spotted turtle (Clemmys guttata) in Rhode Island, USA. We predicted that because spotted turtles have disproportionately experienced the detrimental effects of habitat loss and fragmentation associated with landscape change, that these effects would manifest in the form of higher inbreeding, less diversity, and greater population genetic structure compared to eastern painted turtles. As expected, eastern painted turtles exhibited little population genetic structure, showed no evidence of inbreeding, and little differentiation among sampling sites. For spotted turtles, however, results were consistent with certain predictions and inconsistent with others. We found evidence of modest inbreeding, as well as tentative evidence of recent population declines. However, genetic diversity and differentiation among sites were comparable between species. As our results do not suggest any major signals of genetic degradation in spotted turtles, the southern region of Rhode Island may serve as a regional conservation reserve network, where the maintenance of population viability and connectivity should be prioritized.

Generalized spatial mark-resight models with incomplete identification: An application to red fox density estimates.

The estimation of abundance of wildlife populations is an essential part of ecological research and monitoring. Spatially explicit capture–recapture (SCR) models are widely used for abundance and density estimation, frequently through individual identification of target species using camera‐trap sampling.Generalized spatial mark–resight (Gen‐SMR) is a recently developed SCR extension that allows for abundance estimation when only a subset of the population is recognizable by artificial or natural marks. However, in many cases, it is not possible to read the marks in camera‐trap pictures, even though individuals can be recognized as marked. We present a new extension of Gen‐SMR that allows for this type of incomplete identification.We used simulation to assess how the number of marked individuals and the individual identification rate influenced bias and precision. We demonstrate the model's performance in estimating red fox (Vulpes vulpes) density with two empirical datasets characterized by contrasting densities and rates of identification of marked individuals. According to the simulations, accuracy increases with the number of marked individuals (m), but is less sensitive to changes in individual identification rate (δ). In our case studies of red fox density estimation, we obtained a posterior mean of 1.60 (standard deviation SD: 0.32) and 0.28 (SD: 0.06) individuals/km2, in high and low density, with an identification rate of 0.21 and 0.91, respectively.This extension of Gen‐SMR is broadly applicable as it addresses the common problem of incomplete identification of marked individuals during resighting surveys.

Point process models for spatio-temporal distance sampling data from a large-scale survey of blue whales

Distance sampling is a widely used method for estimating wildlife population abundance. The fact that conventional distance sampling methods are partly design-based constrains the spatial resolution at which animal density can be estimated using these methods. Estimates are usually obtained at survey stratum level. For an endangered species such as the blue whale, it is desirable to estimate density and abundance at a finer spatial scale than stratum. Temporal variation in the spatial structure is also important. We formulate the process generating distance sampling data as a thinned spatial point process and propose model-based inference using a spatial log-Gaussian Cox process. The method adopts a flexible stochastic partial differential equation (SPDE) approach to model spatial structure in density that is not accounted for by explanatory variables, and integrated nested Laplace approximation (INLA) for Bayesian inference. It allows simultaneous fitting of detection and density models and permits prediction of density at an arbitrarily fine scale. We estimate blue whale density in the Eastern Tropical Pacific Ocean from thirteen shipboard surveys conducted over 22 years. We find that higher blue whale density is associated with colder sea surface temperatures in space, and although there is some positive association between density and mean annual temperature, our estimates are consistent with no trend in density across years. Our analysis also indicates that there is substantial spatially structured variation in density that is not explained by available covariates.

Accounting for uncertainty in duplicate identification and group size judgements in mark–recapture distance sampling

AbstractMark–recapture distance sampling (MRDS) surveys with two independent observers are widely used to estimate wildlife population abundance. The analysis relies on accurate identification of duplicate sightings common to both observers, and correct judgements of group size, both of which are hard to achieve for species that exhibit complex grouping patterns.In this paper, we examine the impact of these sources of uncertainty on bias and precision of abundance estimates, using a case study of 22 aerial surveys of common dolphins (Delphinus delphis) in the Hauraki Gulf, New Zealand. We develop a novel probabilistic method to identify duplicate observations, and account for various sources of uncertainty using a simulation‐intensive approach.For our case study, identifying duplicates using reasonable but arbitrary thresholds of time and angle discrepancies created a range of abundance estimates differing by up to 20%, whereas our novel threshold‐free probabilistic analysis returned an estimate roughly central to this range. Uncertainty in group size made a smaller impact of up to 5% on abundance estimates. All analysis choices returned similar values for the coefficient of variation, from 20 to 23%.Generating robust estimates of abundance, and accounting for all associated sources of uncertainty, is critical for informing conservation management. Our novel approach provides a way to eliminate arbitrary decisions associated with MRDS, and account for a wider range of uncertainties. Our method allows for the reliable application of MRDS to a wider range of terrestrial and marine species, and will be a useful tool for producing robust abundance estimates for species that exhibit complex grouping patterns.

Examining disease prevalence for species of conservation concern using non‐invasive spatial capture–recapture techniques

Summary Non‐invasive techniques have long been used to estimate wildlife population abundance and density. However, recent technological breakthroughs have facilitated non‐invasive estimation of the proportion of animal populations with certain diseases. Giraffes Giraffa camelopardalis are increasingly becoming recognized as a species of conservation concern with decreasing population trajectories across their range in Africa. Diseases may be an important component impacting giraffe population declines, and the emerging ‘giraffe skin disease’ (GSD), characterized by the appearance of wrinkled skin and alopecic lesions on the limbs, neck and chest of infected giraffe, may hinder movement causing increased susceptibility to predation. We examined the prevalence of GSD in Tanzania's Ruaha National Park over a 4‐month period in 2015, using photographic capture–recapture surveys via road‐based transects. We divided the study area into five circuitous survey units, each approximately 100 km in length ( = 99·22 km, SD = 3·72), and surveyed for giraffes for 4 months. From these surveys, we developed a data base of spatially explicit giraffe photographs. We processed these photographs for individual identification and fitted spatial capture–recapture models to predict the spatial configuration of giraffe abundance and GSD prevalence within the study area. Our results indicated that >86% of the giraffe population showed signs of GSD and that the disease was more prevalent in the northern and north‐eastern portion of Ruaha National Park. Synthesis and applications. Our research shows that data from non‐invasive surveys can be used in spatial capture–recapture (SCR) models to estimate the proportion of a population affected by a visible disease. Researchers and conservationists can use SCR models to better examine the variation in parameters associated with these populations such as sex, age class, movement, and encounter rate, which may be linked to the prevalence of the disease, while incorporating broad spatial and temporal dimensions of the population in such areas. We discuss the implications of this research for conservation of threatened species with an emphasis on disease ecology and vulnerability to predations, and more broadly, for wildlife conservation.

Site-Specific Assessments of the Abundance of Three Inshore Dolphin Species to Inform Conservation and Management

Assessing the abundance of wildlife populations is essential to their effective conservation and management. Concerns have been raised over the vulnerability of tropical inshore dolphins in waters off northern Australia to anthropogenic impacts on local populations, yet a lack of abundance data precludes assessment of their conservation status and the management of threats. Using small vessels as cost-effective research platforms, photo-identification surveys and capture-recapture models were applied to provide the first quantitative abundance data for Australian snubfin (Orcaella heinsohni), Australian humpback (Sousa sahulensis), and Indo-Pacific bottlenose dolphins (Tursiops aduncus) at five sites in the Kimberley region of north-western Australia. The abundance of each species was highly variable between different sites, likely reflecting species-specific habitat preferences. Within the c. 130 km2 study sites, the estimated abundance of most species was ≤ 60 individuals (excluding calves), and fewer than 20 humpback dolphins were identified at each site in any one 3-5 week sampling period. However, larger estimates of c. 130 snubfin and c. 160 bottlenose dolphins were obtained at two different sites. Several local populations showed evidence of site fidelity, particularly snubfin dolphins. By implementing a standardized, multi-site approach, data on local populations were provided within a broader, regional context, and indicated that each species is patchily distributed in the region. This highlights the need for site-specific baseline data collection using appropriate survey techniques to quantitatively assess the potential impacts of threatening activities to local populations. These findings further illustrate the need to gain a greater understanding of known and potential threats to inshore dolphin populations, their relative impacts, and to mitigate where necessary. In particular, the level of interactions with inshore gillnet fisheries requires urgent assessment. An ideal candidate site for a long-term study of snubfin dolphin population dynamics is identified, where trends in abundance and their influencing factors could be investigated. The methods employed herein provide an example of rigorous, site-specific population assessments of inshore dolphins that are broadly applicable to such studies elsewhere.

Use of small rodents for the surveillance of agents and vectors of tick-borne zoonoses in the northern Apennines, Italy

Vector borne zoonoses are emerging threats in Europe. Data collection from animals may be useful to evaluate their occurrence and intensity of transmission, and to detect their introduction into previously free geographic areas. Indeed, vertebrates serve as hosts for pathogens and for arthropod vectors, although their ecological role can vary according to the diseases. Data collection on small rodents was used to study the eco-epidemiology of two tick-borne pathogens, Rickettsia slovaca (agent of tick-borne lymphadenopathy, transmitted by Dermacentor marginatus) and Borrelia burgdorferi sensu lato (agent of Lyme Borreliosis, transmitted by Ixodes ricinus), in the Apennine mountains, Tuscany (Italy), where human cases of tick-borne diseases were reported. Small rodents are preferential hosts for the immature stages of the two tick vectors and are involved in the transmission cycle of both diseases. In the summers from 2009 to 2012, we live trapped Apodemus spp. and Myodes glareolus from 1100 to 1650 m above the sea level (a.s.l.). Rodents were found infested by immature I. ricinus and D. marginatus. The monthly activities of these two tick species on the same hosts were different, reflecting differences in their life cycles. Although few individuals were co-infested, both tick species tended to aggregate on the same Apodemus spp. males. R. slovaca and B. burgdorferi s.l. were detected in rodent ear biopsies and attached ticks up to 1650 m a.s.l. In our study area, rodents might play a role as amplifiers of R. slovaca infection; even in the absence of the host's systemic infection, tick aggregation on the same individuals might favour the transmission of the pathogen through co-feeding. While D. marginatus had been found at the same location in studies carried out in 1994, I.ricinus was very rare or absent. Data collection on small rodents thus highlighted the recent range expansion of I. ricinus and B. burgdorferi s.l. in a previously unoccupied area. Major land use changes, the increased abundance of wildlife populations, as well as a general climate warming in the Mediterranean area, might be interacting factors affecting the altitudinal range expansion of I. ricinus in the Northern Apennines, and in Italy in general.

Reducing the uncertainty of wildlife population abundance: model‐based versus design‐based estimates

Knowledge of population sizes in delimited spatial regions is crucial for most ecological research. Data from population surveys are collected with strip, line, or point transects sampling. These data are positively skewed and spatially autocorrelated, which makes estimation of uncertainty in the population size difficult. Thus, we propose a novel spatial‐based estimator from a hierarchical spatial model for count data where the inhomogeneous animal density is decomposed into a deterministic trend related to potential habitat and a stationary latent field modeled by geostatistics. An empirical estimate of the latent variable is obtained including corrective terms for non‐stationarity and variance resulting from a Poisson distribution of sightings. A novel block kriging estimator that takes into account both non‐stationarity and the nature of count data is derived to obtain a spatial estimate of animal total abundance and variance of errors of prediction. From spatial simulated count data and real count data of common guillemot wintering in the Bay of Biscay (France), we compare mean population size and variance estimates obtained from our model‐based approach to the design‐based estimator (i.e., block bootstrap). The novel Poisson block kriging estimates greatly reduces uncertainty of population size estimates while block bootstrap provides larger uncertainties. Copyright © 2013 John Wiley & Sons, Ltd.

A new method for identifying rapid decline dynamics in wild vertebrate populations

Tracking trends in the abundance of wildlife populations is a sensitive method for assessing biodiversity change due to the short time-lag between human pressures and corresponding shifts in population trends. This study tests for proposed associations between different types of human pressures and wildlife population abundance decline-curves and introduces a method to distinguish decline trajectories from natural fluctuations in population time-series. First, we simulated typical mammalian population time-series under different human pressure types and intensities and identified significant distinctions in population dynamics. Based on the concavity of the smoothed population trend and the algebraic function which was the closest fit to the data, we determined those differences in decline dynamics that were consistently attributable to each pressure type. We examined the robustness of the attribution of pressure type to population decline dynamics under more realistic conditions by simulating populations under different levels of environmental stochasticity and time-series data quality. Finally, we applied our newly developed method to 124 wildlife population time-series and investigated how those threat types diagnosed by our method compare to the specific threatening processes reported for those populations. We show how wildlife population decline curves can be used to discern between broad categories of pressure or threat types, but do not work for detailed threat attributions. More usefully, we find that differences in population decline curves can reliably identify populations where pressure is increasing over time, even when data quality is poor, and propose this method as a cost-effective technique for prioritizing conservation actions between populations.

Effects of landscape‐scale forest change on the range contraction of ruffed grouse in New York State, USA

AbstractIn New York State, USA, the abundance of ruffed grouse (Bonasa umbellus) has declined >75% since the 1960s. We hypothesized that range contraction of grouse in New York State was associated with broad‐scale spatial patterns relating to forest maturation and that these patterns would be evident at the landscape scale. We evaluated data available from New York Breeding Bird Atlases conducted in the early 1980s and again in the early 2000s, which included surveys of >5,000, 25‐km2 sample blocks across a landscape of 125,384 km2. We related detection and non‐detection of grouse within Breeding Bird Atlas blocks to forest amount, landscape configuration, and presence of neighboring grouse populations via autologistic regression. Loss of early successional forest was inversely related to the continued persistence of grouse (R2 = 0.61). Models showed that ruffed grouse were no longer detected in marginal areas of their distribution, blocks with low amounts of forest, and blocks with landscape configurations potentially advantageous to nest predators. We conclude that declines in both habitat quality and quantity have contributed to range contraction within New York State and that the factors affecting grouse populations have acted at broader spatial scales than are typically considered in habitat evaluation. Our work demonstrates that large landscape‐scale data sets are valuable in assessing changes in distribution and abundance of wildlife populations. Landscape analyses at large geographic scales (>100,000 km2) are of particular importance to managers because they represent ways of quantifying the influence of economic incentives and land‐use policy on habitats across large regions. © The Wildlife Society, 2012

An evaluation of monitoring methods for the endangered giant kangaroo rat

AbstractAccurate, reliable, and efficient monitoring methods for detecting changes in the distribution and abundance of wildlife populations are the cornerstone of effective management. Aerial surveys of active burrow sites and ground counts of open burrows have been used to estimate distribution and abundance, respectively, of a number of rodent species. We compared the efficacy of these and other methods for estimating distribution, abundance, and population growth of the endangered giant kangaroo rat (Dipodomys ingens) to determine the best practices for monitoring. Specifically, we compared aerial surveys, rapid expert assessments, and live‐trapping for estimating giant kangaroo rat range, and burrow counts and live‐trapping for estimating abundance and growth. We carried out the study in the Carrizo Plain National Monument, California, USA, from 2007 to 2011. Expert rapid assessment of sites performed nearly as well as trapping in determining range extent, while aerial surveys provided estimates of total range extent but with less precision. Active burrow counts were adequate to determine relative abundance averaged over multiple years, but were not reliable as an estimate of annual population size or growth. © 2012 The Wildlife Society.

Striped Skunk Relative Abundance in Flagstaff, Arizona: Implications for Rabies Spread and the Current TVR Program

Author(s): Maestas, Jesse M.; Theimer, Tad C.; Hausig, Krista M.; Bergman, David L. | Abstract: Potentially fatal wildlife diseases like rabies are of increasing concern, due to human effects on the environment that could alter wildlife behavior and population dynamics in ways that increase disease prevalence. Wildlife population abundance is a key factor, affecting both disease outbreak and rate of disease spread, and understanding how population abundance changes across landscapes is crucial for developing predictive models to control and manage wildlife diseases. We investigated how urbanization in Flagstaff, AZ influenced skunk population abundance by simultaneously trapping 6 pairs of suburban and wildland study sites for 200 trap-nights between June and September 2011. The number of unique skunks captured at the 6 suburban locations ranged from 3-14 (mean = 6.5) while the number trapped at the 6 wildland sites ranged from 0-2 (mean = 0.5). We also reviewed data gathered as part of the trap-vaccinate-release (TVR) program carried out from 2004-2010 by USDA APHIS Wildlife Services to estimate population sizes and the percentage of the population vaccinated. Our estimates of the percentage of the population vaccinated in any one year ranged from 11%-23%, below percentages reported in the literature as necessary to prevent disease spread. Overall, these data indicate that the potential for disease spread was greatest within the suburban matrix and relatively low in surrounding wildlands and that intensified annual TVR programs would be necessary to maintain high enough percentages of immunized animals to achieve effective herd immunity.