Hierarchical Distance Sampling Models Research Articles

Reassessment of French breeding bird population sizes using citizen science and accounting for species detectability.

Higher efficiency in large-scale and long-term biodiversity monitoring can be obtained through the use of Essential Biodiversity Variables, among which species population sizes provide key data for conservation programs. Relevant estimations and assessment of actual population sizes are critical for species conservation, especially in the current context of global biodiversity erosion. However, knowledge on population size varies greatly, depending on species conservation status and ranges. While the most threatened or restricted-range species generally benefit from exhaustive counts and surveys, monitoring common and widespread species population size tends to be neglected or is simply more challenging to achieve. In such a context, citizen science (CS) is a powerful tool for the long-term monitoring of common species through the engagement of various volunteers, permitting data acquisition on the long term and over large spatial scales. Despite this substantially increased sampling effort, detectability issues imply that even common species may remain unnoticed at suitable sites. The use of structured CS schemes, including repeated visits, enables to model the detection process, permitting reliable inferences of population size estimates. Here, we relied on a large French structured CS scheme (EPOC-ODF) comprising 27,156 complete checklists over 3,873 sites collected during the 2021-2023 breeding seasons to estimate the population size of 63 common bird species using hierarchical distance sampling (HDS). These population size estimates were compared to the previous expert-based French breeding bird atlas estimations, which did not account for detectability issues. We found that population size estimates from the former French breeding bird atlas were lower than those estimated using HDS for 65% of species. Such a prevalence of lower estimations is likely due to more conservative estimates inferred from semi-quantitative expert-based assessments used for the previous atlas. We also found that species with long-range songs such as the Common Cuckoo (Cuculus canorus), Eurasian Hoopoe (Upupa epops) or the Eurasian Blackbird (Turdus merula) had, in contrast, higher estimated population sizes in the previous atlas than in our HDS models. Our study highlights the need to rely on sound statistical methodology to ensure reliable ecological inferences with adequate uncertainty estimation and advocates for a higher reliance on structured CS in support of long-term biodiversity monitoring.

Biotic and abiotic drivers of harbor seal (Phoca vitulina) fine‐scale densities in the Salish Sea

AbstractUnderstanding relationships between environmental characteristics and variation in species occurrence and density can provide information for managing human activities, protected species, and species of commercial importance in a dynamic system. To identify environmental drivers associated with variation in harbor seal (Phoca vitulina) densities in the Salish Sea, Washington, we analyzed 20 years of boat‐based survey data and environmental covariates using a hierarchical distance sampling model. We included spatial, temporal, and spatiotemporal environmental covariates in our model and produced fine‐scale predictive maps displaying in‐water estimated densities from our model results. We found that spatial covariates were the strongest predictors for harbor seal densities in the Salish Sea. Harbor seals were more abundant closer to major river mouths, near shore, in shallower waters, and in areas with more haul‐out sites. Additionally, harbor seal density varied with shoreline type. Changes in predicted harbor seal spatial use of the Salish Sea varied but with little difference between breeding/molting and nonbreeding/nonmolting seasons. Our results revealed spatiotemporal variation in harbor seal fine‐scale density in the Salish Sea, which are particularly important for conservation planning, as spatiotemporal variation in harbor seal density can exert heterogenous top‐down effects on prey species populations, some of which are threatened.

Functional responses in habitat selection as a management tool to evaluate agri-environment schemes for farmland birds

Agri-environment schemes (AES), as part of the European Union's Common Agricultural Policy, are intended to help prevent the decline of farmland biodiversity. Nevertheless, the ecological effectiveness of AES in supporting farmland bird populations remains inconclusive across studies. This inconsistency highlights a research gap: What behavioral mechanisms contribute to the variation in farmland bird populations? This variability may arise because farmland birds alter their habitat selection in response to available habitat—a phenomenon known as functional responses in habitat selection. Here, we examined the effects of AES and non-AES land-use variables on habitat selection of farmland birds, taking into account the species-specific functional response to availability. We built two types of hierarchical distance sampling models to analyze observational data of four farmland bird species from line-transect surveys during the breeding season in Saxony, Germany. First, we built mixed-effects models to estimate the marginal effects of AES and non-AES land-use variables on the occurrence of farmland birds. Second, we integrated linear models into the distance sampling model to relate habitat selection estimates to habitat availability. Results from the first mixed-effects model showed positive and negative effects of AES on habitat selection. In the second model, we observed inverse relationships between habitat selection and availability for both AES and non-AES variables. These results support the hypothesis of negative functional responses, as we found a decrease in the tendency of farmland birds to select both AES and non-AES land uses as their availability increased. Our findings suggest that the varying effects of AES on bird occurrences reported in the literature may depend on cross-study differences in AES availability. We propose that functional responses in habitat selection should be considered as a phenomenon in future AES research. Our study also highlights the importance of optimal AES provision and their spatial allocation in the agricultural landscape.

SpAbundance: An R package for single‐species and multi‐species spatially explicit abundance models

Abstract Numerous modelling techniques exist to estimate abundance of plant and animal populations. The most accurate methods account for multiple complexities found in ecological data, such as observational biases, spatial autocorrelation, and species correlations. There is, however, a lack of user‐friendly and computationally efficient software to implement the various models, particularly for large data sets. We developed the spAbundance R package for fitting spatially explicit Bayesian single‐species and multi‐species hierarchical distance sampling models, N‐mixture models, and generalized linear mixed models. The models within the package can account for spatial autocorrelation using Nearest Neighbour Gaussian Processes and accommodate species correlations in multi‐species models using a latent factor approach, which enables model fitting for data sets with large numbers of sites and/or species. We provide three vignettes and three case studies that highlight spAbundance functionality. We used spatially explicit multi‐species distance sampling models to estimate density of 16 bird species in Florida, USA, an N‐mixture model to estimate black‐throated blue warbler (Setophaga caerulescens) abundance in New Hampshire, USA, and a spatial linear mixed model to estimate forest above‐ground biomass across the continental USA. spAbundance provides a user‐friendly, formula‐based interface to fit a variety of univariate and multivariate spatially explicit abundance models. The package serves as a useful tool for ecologists and conservation practitioners to generate improved inference and predictions on the spatial drivers of abundance in populations and communities.

Clark's nutcracker forest community visitation: Whitebark pine maintains a keystone seed disperser.

Clark's nutcrackers (Nucifraga columbiana) are obligate seed dispersers for whitebark pine (Pinus albicaulis), but they frequently use other conifer seed resources because of annual variability in cone production or geographic variation in whitebark pine availability. Whitebark pine is declining from several threats including white pine blister rust, leading to potential population declines in the nutcracker and the pine. We hypothesize that where there are few additional seed resources, whitebark pine becomes the key and limiting resource supporting nutcracker populations. We investigated how nutcrackers use coniferous forest community types within Yellowstone National Park to determine potential seed resources and the importance of whitebark pine. We established sites representing five forest community types, including whitebark pine, lodgepole pine (P. contorta), Engelmann spruce (Picea engelmannii), limber pine (P. flexilis), and Douglas-fir (Pseudotsuga menziesii). Each transect annually generated nutcracker point counts, conifer cone production indices, community composition data, and seed resource use observations. We compared hierarchical distance sampling models, estimating nutcracker density and its relationship to forest community type, seed harvesting time-period, year, study site, and cone seed energy. We found cone production varied across years indicating annual variability in energy availability. Nutcracker density was best predicted by forest community type and survey time-period and was highest in whitebark pine stands during the mid-harvesting season. Nutcracker density was comparatively low for all other forest community types. This finding underscores the importance of whitebark pine as a key seed resource for Clark's nutcracker in Yellowstone National Park. The decline of whitebark pine potentially leads to a downward spiral in nutcrackers and whitebark pine, arguing for continued monitoring of nutcrackers and implementation of restoration treatments for whitebark pine.

Trends and population estimate of the threatened Buff-breasted SandpiperCalidris subruficolliswintering in coastal grasslands of southern Brazil

SummaryInformation about population sizes, trends, and habitat use is key for species conservation and management. The Buff-breasted SandpiperCalidris subruficollis(BBSA) is a long-distance migratory shorebird that breeds in the Arctic and migrates to south-eastern South America, wintering in the grasslands of southern Brazil, Uruguay, and Argentina. Most studies of Nearctic migratory species occur in the Northern Hemisphere, but monitoring these species at non-breeding areas is crucial for conservation during this phase of the annual cycle. Our first objective was to estimate trends of BBSA at four key areas in southern Brazil during the non-breeding season. We surveyed for BBSA and measured vegetation height in most years from 2008/09 to 2019/20. We used hierarchical distance sampling models in which BBSA abundance and density were modelled as a function of vegetation height and corrected for detectability. Next, we used on-the-ground surveys combined with satellite imagery and habitat classification models to estimate BBSA population size in 2019/20 at two major non-breeding areas. We found that abundance and density were negatively affected by increasing vegetation height. Abundance fluctuated five- to eight-fold over the study period, with peaks in the middle of the study (2014/15). We estimated the BBSA wintering population size as 1,201 (95% credible interval [CI]: 637–1,946) birds in Torotama Island and 2,232 (95% CI: 1,199–3,584) in Lagoa do Peixe National Park during the 2019/20 austral summer. Although no pronounced trend was detected, BBSA abundance fluctuated greatly from year to year. Our results demonstrate that only two of the four key areas hold high densities of BBSA and highlight the positive effect of short grass on BBSA numbers. Short-grass coastal habitats used by BBSA are strongly influenced by livestock grazing and climate, and are expected to shrink in size with future development and climatic changes.

Functional response traits and altered ecological niches drive the disassembly of cloud forest bird communities in tropical montane countrysides.

Anthropogenic disturbance contributes to global change by reshaping the ecological niche space available to biological communities. Quantifying the range of functional response traits required for species persistence is central towards understanding the mechanisms underlying community disassembly in disturbed landscapes. We used intensive field surveys of cloud forest bird communities across seven replicate landscapes undergoing agricultural conversion in the Peruvian Andes to examine how a suite of 16 functional response traits related to morphology, diet, foraging behaviour and environmental niche breadth predict (1) species-specific abundance changes in countryside habitats compared to forest and (2) differential changes to the ecological niche space occupied by communities. Our analyses relied on (1) hierarchical distance sampling models to examine the functional predictors of abundance change across the agricultural land use gradient while accounting for imperfect detection and (2) n-dimensional hypervolumes to quantify the expansion and contraction of ecological niche space in countryside habitats. Key traits related to increased abundance in early successional and mixed-intensity agricultural areas included (1) morphological adaptations to dense understorey habitats, (2) plant-based diets (flowers, fruit and seeds) and (3) broad elevational range limits and habitat breadth. Species occupying mixed and high-intensity agricultural land use regimes had mean elevational range limits 20%-60% wider than species found within forests. Collectively, ecological niche space expanded within agricultural habitats for traits related to diet and environmental niche breadth, while contracting for foraging and dispersal traits. Such changes were driven by species with unique functional trait combinations. Our results reveal the dynamic changes to ecological niche space that underly community structure in disturbed landscapes and highlight how increased niche breadth can ameliorate disturbance sensitivity for generalist species. We emphasize that functional traits can be used to predict changes in community structure across disturbance gradients, allowing insights into specific mechanisms underlying community disassembly beyond emergent patterns of functional diversity. By identifying key functional trait groups that align with different countryside habitats, we demonstrate how conservation practitioners can contribute to the retention of avian functional diversity in agricultural landscapes throughout the world.

Abundance and distribution of ruffed grouse Bonasa umbellus at the southern periphery of the range

Peripheral populations of ruffed grouse Bonasa umbellus at the trailing edge of their range are likely to be at risk of population declines and potential extirpation from continued climate and habitat change. Accurate estimates of abundance and distribution are needed for peripheral populations to inform management decisions. We show that ruffed grouse density at the southern periphery of their range is much lower than in areas farther north. We surveyed the abundance of drumming male grouse across northern Georgia during the springs of 2020–2021 by performing roadside point counts at 634 survey points. We used hierarchical distance sampling models to predict grouse abundance across north Georgia and model spatial variation in density. We estimated population size to be 2671 ruffed grouse in Georgia (95% confidence interval 678–11 084). Mean grouse density was 0.003 drumming males/ha (SD = 0.006), with the highest density centered in the high‐elevation mountains. Uncertainty around abundance estimates was high, in part due to low precision when estimating availability. Low detectability and precision are likely to be common issues when surveying low‐abundance populations, as often occurs at range peripheries. We suggest that abundance estimation could be improved by integrating alternate methods, such as automated recording units, into existing monitoring protocols.

Results from Kentucky’s 10-year Bobwhite Recovery Plan

The northern bobwhite (Colinus virginianus; hereafter, bobwhite) has experienced a precipitous population decline through almost all its historical range over the last 6 decades. We initiated a 10-year restoration plan in Kentucky, USA in 2008 and reported on it through 3 published “Road to Recovery” reports along with 30 peer-reviewed articles and abstracts, 2 technical documents, 7 theses or dissertations, and 11 popular literature pieces. Seven Quail Focus Areas were selected across the state based on site personnel, geographic position (east to west), and land ownership (e.g., private, public, state, federal) for monitoring and habitat management. The focus areas averaged 11,895 acres and area managed for quail on an annual basis ranged from 9% to 42%. Management took the form of herbicide applications, disking, fire, planting, grazing, control of woody vegetation, and mowing. Hierarchical distance sampling models using time-of-removal information to inform detection processes were used to assess bobwhite density on each focus area. Models were based on spring breeding bird point counts in which a suite of grassland songbirds were recorded and fall covey counts in which only bobwhite were recorded. Roadside surveys (direct observation) and hunter harvest information were also used for statewide comparisons of focus areas and statewide trends. Across all years and focus areas a 40% increase in the quail population was observed in 20 years (38%) of the possible 53 years of survey data. Stable to increasing trends were also observed in focal area populations of dickcissel (Spiza americana), eastern meadowlark (Sturnella magna), eastern kingbird (Tyrannus tyrannus), field sparrow (Spizella pusilla), prairie warbler (Setophaga discolor), and Bell’s vireo (Vireo bellii). A focus on habitat management and associated monitoring on relatively small (<20,000 acres) areas was shown to be successful on an individual area, as determined by increases in bird numbers across years and challenges completed in the Quail Plan but did not result in an observed increase in statewide quail indices. Future quail restoration plans in Kentucky should be directed more toward open production land, which is made up of 6 million acres of pastureland, hay land, row crop agriculture, reverting fields, reclaimed mineland, and grasslands. This work will be completed by utilizing working lands, such as field borders in row crop systems and grazing native warm-season grasses in pasture or hay systems, for wildlife activities. Monitoring systems on these production lands will be in the breeding season only and autonomous recording devices will be

Estimating Northern Bobwhite Density in Privately-Owned Forests Across the Southeast

Rigorous density estimates can inform management, conservation planning, and policy decisions. Northern bobwhite (Colinus virginianus; hereafter, bobwhite) populations are declining throughout their range, including the southeastern United States. Numerous private land conservation initiatives are underway to restore bobwhite populations in these areas, but baseline estimates of density spanning privately owned forests are sparse. This information gap makes it difficult to evaluate the population-level effects of these conservation programs or develop expectations for timelines to reach population targets. We sought to understand baseline densities across privately owned pine forests in the southeastern United States in areas targeted for bobwhite conservation. We sampled 138 pine stands a total of 286 times across Alabama, Florida, Georgia, North Carolina, and South Carolina from 2018 to 2020 using autumn distance sampling point counts. We sampled 110 stands that were either planned or applied U.S. Department of Agriculture, Natural Resources Conservation Service (NRCS) management contracts, specifically Working Lands for Wildlife (WLFW) Northern Bobwhite partnerships or similar Environmental Quality Incentives Program contracts. The WLFW contracts were solely in focal counties identified in the National Bobwhite Conservation Initiative 2.0 plan and by cooperating state wildlife agencies. Contracts were on average 17.4 ha (SD = 27.0). We also sampled 28 areas with extensive (>200 ha) and intensive (e.g., at least every 3 years for the last ~10 years) habitat management for bobwhite. We used a hierarchical distance sampling model to estimate density. Density was lowest in North Carolina and South Carolina. Densities in Alabama were slightly greater, and greatest densities were in Georgia and Florida. However, the majority of observed densities were 2 coveys/40 ha or below, and all were below 4 coveys/40 ha. Large and intensively managed sites on average had 4 times greater density compared to smaller contracts. Specific densities will be reported in the future. Our sampling efforts fill an important information gap regarding densities throughout private lands in the southeastern United States. Our models indicate that bobwhite densities are generally low in pine forests of the Southeast even in counties identified as having the greatest conservation potential. The relatively greater densities at large and intensively managed sites illustrate the need for more intensive landscape-scale planning when implementing private land conservation. Pending research will compare densities at planned and applied NRCS contracts in the context of landscape-scale habitat amount.

A hierarchical distance sampling model to estimate spatially explicit sea otter density

AbstractWildlife managers often rely on analyses conducted prior to the widespread adoption of hierarchical models which can lead to questions about the accuracy of previous inferences. Hierarchical models allow observed data to be partitioned into factors that influenced the collection of the data such as detectability of animals (i.e., observation processes) and factors that influence the ecology of a population such as features that affect the distribution of animals (i.e., ecological processes). Population surveys for sea otters in the Aleutian Islands, Alaska, have historically been conducted by conflating the observation and ecological processes potentially leading to inaccurate population estimates. Based on boat and plane‐based sea otter survey data collected in 2017, we sought to overcome many problems of previous sea otter surveys in southwestern Alaska. We developed a spatially explicit hierarchical distance sampling model to estimate the abundance of sea otters in the Eastern Aleutians Management Unit while explicitly accounting for factors that affect the ability to detect sea otters during surveys (i.e., group size, ocean conditions). We also sought to account for the environmental factors leading to the non‐uniform distribution of sea otter groups by identifying relationships between otter group abundance and environmental attributes (i.e., ocean depth, presence of kelp, underwater substrate). Detection of sea otter groups was related to group size, and ocean conditions (e.g., ocean swell size). After accounting for detection, we estimated a mean population size of 8593 individual sea otters (95% CI: 7450–9984) which is considerably higher than previous estimates, although comparisons are difficult given divergent methodologies. Sea otter group density was negatively related to ocean depth and the presence of rock and gravel as underwater substrates. Conversely, sea otter group density was positively related to the presence of kelp and mud as an underwater substrate. Our hierarchical distance sampling model accounted for the observation process which allowed better estimates of the environmental attributes affecting otter abundance. Our research can serve as a template for other study systems requiring spatially explicit density estimates from a distance sampling framework and can help provide a new baseline for managers to gauge future population changes in sea otters.

Top-down effects of raptor predation on northern bobwhite.

Understanding how predators affect prey species is a central endeavor in applied ecology. Game birds are a culturally and economically important group of birds throughout the world. Specifically, northern bobwhite (Colinus virginianus) is an imperiled game bird native to North America that has declined precipitously over the past 65years. Concurrently, raptor populations increased substantially as a result of pesticide bans and legal protections. However, relationships between raptors and bobwhites are not well-understood because of limited long-term data. We analyzed long-term raptor survey and bobwhite survival datasets from 2008 to 2018 to determine if oscillations in raptor abundance affected bobwhite survival. We used a novel open multi-species dynamics hierarchical distance sampling model to estimate the abundance of raptors. We used a known-fate survival model to determine if variation in raptor abundance affected bobwhite survival. We had multiple working hypotheses regarding biological relationships between raptor abundance and bobwhite survival. Raptors affected bobwhites in every biological season but were more influential in the breeding season and late winter supporting the notion of bobwhite behavior and raptor migration were driving observed patterns. Our results suggest that even in areas with abundant habitat, predators exert top-down influences on vital rates suggesting similar or greater influences on populations under poor habitat conditions.

Farmland composition and farming practices explain spatio-temporal variations in red-legged partridge density in central Spain

Many farmland bird populations are declining, and their negative trends are often associated with changes in land-use or farming practices, including the use of agrochemicals. The red-legged partridge (RLP) is a Mediterranean farmland game species of high socio-economic importance whose populations are thought to have declined sharply since the mid-20th century associated with farmland changes. However, no large-scale studies have tested whether abundance or trends of RLP are related to farmland composition or management.We used hierarchical distance sampling models to estimate RLP abundance in 2010 in central Spain (Castilla-La Mancha), a main European population stronghold of this species. We studied associations between RLP density and land-uses (including variation in management: irrigated crops or organic farming). We also assessed regional abundance variation over seven years (2010–2017) and its relationship with changes in land-use.Our results show that RLP abundance increased with the availability of natural vegetation and traditional rain-fed vineyards, but decreased with increasing proportions of tree crops and irrigated vineyards; the latter association was less pronounced in areas sensitive to nitrate contamination in water, where the amount of fertilizers applied in farmland and use of certain farming practices is more strictly regulated. These results support the idea that increases in intensive vineyards are detrimental to the RLP. We also report a strong population decline of RLP in the region, with a 51% abundance reduction in seven years. This decline was steeper in areas where more natural vegetation had been lost and where ecological tree crops had increased.Overall, our results indicate that changes in land-use (type of crop, or the destruction of natural vegetation in farmland) and farming practices (e.g. use of irrigation in certain crops, use of nitrates) have important impacts on this farmland bird, affecting both spatial distribution and population dynamics.

Assessment and novel application ofN‐mixture models for aerial surveys of wildlife

AbstractAerial surveys are a critical tool for inference of wildlife populations, yet are limited by current available methods to account for imperfect detection without marking animals.N‐mixture models use count data from replicate surveys for estimation of wildlife abundance while accounting for imperfect detection. This statistical framework was recently developed but is rarely applied to aerial surveys of terrestrial wildlife. We applied anN‐mixture model incorporating temporary emigration to a novel aerial survey design to estimate the availability, detection probability, and abundance of an unmarked population of white‐tailed deer (Odocoileus virginianus) in Michigan, USA, using surveys in 2014 and 2016. We assessed the selection of the sample unit size for theN‐mixture model in a post hoc sensitivity analysis and compared all estimates to a hierarchical distance sampling (HDS) approach. Hierarchical distance sampling is an expansion of conventional distance sampling (CDS), a standard method applied to aerial survey designs for wildlife, where HDS allows for modeling spatial variation in abundance and detection probability using environmental covariates and can account for changing population demographics. We foundN‐mixture models produced similar parameter estimates to HDS with both models inferring a population increase between years, which was consistent with independently available estimates for this population (2014N‐mixture = 402 [CI: 303.8–515.27], and HDS = 391 [CI: 297.67–504.63]; 2016N‐mixture = 732 [CI: 572.89–924.22], and HDS = 708 [CI: 552.45–893.89]). Estimates from both models were sensitive to the sample unit size selected in an open population framework. We recommend using ecological (e.g., home range size) and statistical (e.g., sample size) considerations when selecting a sample unit size that reflects the movement ecology of a species and evaluating the sensitivity of parameters to sample unit size. In our study,N‐mixture models and HDS models afforded a practical field approach and analytical methodology that resulted in precise estimation in a population of unmarked deer over time. When used with aerial surveys in terrestrial systems where distance sampling methods may be difficult to implement,N‐mixture models can address observer error and estimate populations for conservation and management goals without marking individual animals.

Optimizing conservation in species-specific agricultural landscapes.

Recovery of grassland birds in agricultural landscapes is a global imperative. Agricultural landscapes are complex, and the value of resource patches may vary substantially among species. The spatial extent at which landscape features affect populations (i.e., scale of effect) may also differ among species. There is a need for regional-scale conservation planning that considers landscape-scale and species-specific responses of grassland birds to environmental change. We developed a spatially explicit approach to optimizing grassland conservation in the context of species-specific landscapes and prioritization of species recovery and applied it to a conservation program in Kentucky (USA). We used a hierarchical distance-sampling model with an embedded scale of effect predictor to estimate the relationship between landscape structure and abundance of eastern meadowlarks (Sturnella magna), field sparrows (Spizella pusilla), and northern bobwhites (Colinus virginianus). We used a novel spatially explicit optimization procedure rooted in multi-attribute utility theory to design alternative conservation strategies (e.g., prioritize only northern bobwhite recovery or assign equal weight to each species' recovery). Eastern meadowlarks and field sparrows were more likely to respond to landscape-scale resource patch adjacencies than landscape-scale patch densities. Northern bobwhite responded to both landscape-scale resource patch adjacencies and densities and responded strongly to increased grassland density. Effects of landscape features on local abundance decreased as distance increased and had negligible influence at 0.8 km for eastern meadowlarks (0.7-1.2 km 95% Bayesian credibility intervals [BCI]), 2.5 km for field sparrows (1.5-5.8 km 95% BCI), and 8.4 km for bobwhite (6.4-26 km 95% BCI). Northern bobwhites were predicted to benefit greatly from future grassland conservation regardless of conservation priorities, but eastern meadowlark and field sparrow were not. Our results suggest similar species can respond differently to broad-scale conservation practices because of species-specific, distance-dependent relationships with landscape structure. Our framework is quantitative, conceptually simple, customizable, and predictive and can be used to optimize conservation in heterogeneous ecosystems while considering landscape-scale processes and explicit prioritization of species recovery.

Biotic and abiotic drivers of dispersion dynamics in a large-bodied tropical vertebrate, the Western Bornean orangutan.

Understanding of animal responses to dynamic resource landscapes is based largely on research on temperate species with small body sizes and fast life histories. We studied a large, tropical mammal with an extremely slow life history, the Western Bornean orangutan (Pongo pygmaeus wurmbii), across a heterogeneous natural landscape encompassing seven distinct forest types. Our goals were to characterize fluctuations in abundance, test hypotheses regarding the relationship between dispersion dynamics and resource availability, and evaluate how movement patterns are influenced by abiotic conditions. We surveyed abundance in Gunung Palung National Park, West Kalimantan, Indonesia, for 99 consecutive months and simultaneously recorded weather data and assessed fruit availability. We developed a Bayesian hierarchical distance sampling model to estimate population dispersion and assess the roles of fruit availability, rainfall, and temperature in driving movement patterns across this heterogeneous landscape. Orangutan abundance varied dramatically over space and time. Each forest type was important in sustaining more than 40% of the total orangutans on site during at least one month, as animals moved to track asynchronies in fruiting phenology. We conclude that landscape-level movements buffer orangutans against fruit scarcity, peat swamps are crucial fallback habitats, and orangutans' use of high elevation forests is strongly dependent on abiotic conditions. Our results show that orangutans can periodically occupy putative-sink habitats and be virtually absent for extended periods from habitats that are vitally important in sustaining their population, highlighting the need for long-term studies and potential risks in interpreting occurrence or abundance measures as indicators of habitat importance.

Giant NuthatchSitta magnadensity and habitat association in a potential stronghold in northern Thailand

SummaryPopulations of the globally endangered Giant NuthatchSitta magnahave declined significantly throughout their small world range in southern China and northern continental South-East Asia. Distance sampling and direct observations were used to estimate the density and habitat use of the Giant Nuthatch in a potential stronghold for the species, Chiang Dao Wildlife Sanctuary, Thailand. Thirteen line transects covering 6.2 km were established in open, pine-oak habitat favoured by the nuthatch, during November 2014 to December 2015. The mean density of Giant Nuthatch was 3.94±1.22 (95% CI = 2.15–7.22) individuals/km2. Hierarchical distance sampling models indicated that habitat use by Giant Nuthatch was strongly correlated with trees of large diameter. A pair of Giant Nuthatches was radio-tracked as they fed nestlings during the 2016 breeding season. The female’s home range, centred on the nest, was 7.79 ha (95% kernel home range), approximately one-fourth of that of the male, 32.01 ha. Giant Nuthatches showed a significant preference for foraging on pinesPinusspp. and used the fissured bark of mature pines in which to store acorns of deciduous tree species. Our research indicates the importance of large, mature trees in open pine-oak forest in supporting Giant Nuthatch populations.

Evaluating the current condition of a threatened marine mammal population: Estimating northern sea otter (Enhydra lutris kenyoni) abundance in southwest Alaska

AbstractWe estimated density and abundance of the threatened southwest Alaska distinct population segment of northern sea otters (Enhydra lutris kenyoni) in two management units. We conducted aerial surveys in Bristol Bay and South Alaska Peninsula management units in 2016, and modeled sea otter density and abundance with Bayesian hierarchical distance sampling models and spatial environmental covariates (depth, distance to shore, depth × distance to shore). Spatial environmental covariates substantially impacted sea otter group density in both management units, but effects sizes differed between the two management units. Abundance (9,733 otters, 95% CrI 6,412–17,819) and density (0.82 otters/km2, 95% CrI 0.54–1.49) estimates for Bristol Bay indicated a moderate population size. In contrast, abundance (546 otters, 95% CrI 322–879) and density (0.06 otters/km2, 95% CrI 0.03–0.09) estimates indicated a relatively low population size in South Alaska Peninsula. Overall, our results highlight the importance of accounting for the detection process in monitoring at‐risk species to reduce the uncertainty associated with making conclusions about population declines.

Building a better baseline to estimate 160 years of avian population change and create historically informed conservation targets.

Globally, anthropogenic land-cover change has been dramatic over the last few centuries and is frequently invoked as a major cause of wildlife population declines. Baseline data currently used to assess population trends, however, began well after major changes to the landscape. In the United States and Canada, breeding bird population trends are assessed by the North American Breeding Bird Survey, which began in the 1960s. Estimates of distribution and abundance prior to major habitat alteration would add historical perspective to contemporary trends and allow for historically based conservation targets. We used a hindcasting framework to estimate change in distribution and abundance of 7 bird species in the Willamette Valley, Oregon (United States). After reconciling classification schemes of current and 1850s reconstructed land cover, we used multiscale species distribution models and hierarchical distance sampling models to predict spatially explicit densities in the modern and historical landscapes. We estimated that since the 1850s, White-breasted Nuthatch (Sitta carolinensis) and Western Meadowlark (Sturnella neglecta) populations, 2 species sensitive to fragmentation of oak woodlands and grasslands, declined by 93% and 97%, respectively. Five other species we estimated nearly stable or increasing populations, despite steep regional declines since the 1960s. Based on these estimates, we developed historically based conservation targets for amount of habitat, population, and density for each species. Hindcasted reconstructions provide historical perspective for assessing contemporary trends and allow for historically based conservation targets that can inform current management.

Abundance estimation from point counts when replication is spatially intensive but temporally limited: comparing binomial N-mixture and hierarchical distance sampling models

We investigated the performance of hierarchical distance sampling (HDS) versus binomial N-mixture (binmix) models, both aiming at abundance (or, equivalently, density) estimation. We tested the accuracy of density estimates using simulated data and compared them to the estimates coming from a Red-breasted Flycatcher (Ficedula parva) point count survey in the Dar¿lubie Forest (N Poland). In both the simulations and the actual data, we mimicked varying plot size (i.e., radius length, and site area) and song loudness from quiet to loud by modifying detection functions. We found that the resolution at which distance detection data are collected (i.e., the number and width of distance classes) had essentially no effect on estimates and their precision in HDS, even when birds were only assigned into two, wide distance classes, such as “close” and “far”. Both site size (radius length) and song loudness affected density estimates in HDS only slightly: a positive bias (by 5%–17%) occurred when sites were small and a lower precision occurred for quiet singers.