Addressing challenges when studying mobile or episodic species: hierarchical Bayes estimation of occupancy and use

Domains of scale in cumulative effects of energy sector development on boreal birds

Context

Industrial development in Canada's boreal forest creates cumulative environmental effects on biodiversity. Some effects may be scale-dependent, creating uncertainty in understanding and hindering effective management.

Objectives

We estimated cumulative effects of energy sector development on distributions of sixteen migratory songbird species at multiple spatial scales within the boreal region of Alberta, Canada, and evaluated evidence for scale domains in species responses.

Methods

We used a hierarchical, multi-scale sampling and modelling framework to compare effects of oil and gas footprint on songbirds at five spatial scales. We used Bayesian Lasso to facilitate direct comparison of parameter estimates across scales, and tested for differences in grouped parameter estimates among species.

Results

We found consistent scale-dependent patterns across species, showing variable responses to development occurring at the smallest scale, little effect at intermediate scales, and stronger, mainly positive effects at the largest scales. Differences in grouped parameter estimates across scales showed strong evidence for scale domains in the response of songbirds to energy sector development.

Conclusions

We concluded that variable effects at the smallest scale represented individual habitat selection, while larger scale positive effects reflected expanding distributions of open habitat- and disturbance-associated species in areas of high oil and gas footprint. Our results show that single-scale analyses do not reflect population processes occurring at other scales. Future research on linking patterns at different scales is required to fully understand cumulative effects of land use change on wildlife populations.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10980-023-01779-8.

Environmental DNA detects Spawning Habitat of an ephemeral migrant fish (Anadromous Rainbow Smelt: Osmerus mordax)

BACKGROUND

Anadromous rainbow smelt (Osmerus mordax) have experienced a large range reduction in recent decades and the status of remnant spawning populations is poorly known in Maine, where these fish have significant ecological, cultural, and commercial relevance. Defining the remnant range of anadromous smelt is more difficult than for many declining fish species because adults are only ephemerally present while spawning in small coastal streams at night during spring runoff periods when traditional assessments can be unreliable or even hazardous. We hypothesized that eDNA might facilitate improved survey efforts to define smelt spawning habitat, but that detection could also face challenges from adult eDNA quickly flushing out of these small stream systems. We combined daytime eDNA sampling with nighttime fyke netting to ascertain a potential window of eDNA detection before conducting eDNA surveys in four streams of varying abundance. Hierarchical occupancy modeling was in turn employed to estimate eDNA encounter probabilities relative to numbers of sampling events (date), samples within events, and qPCR replicates within samples.

RESULTS

Results from the combined eDNA and fyke net study indicated eDNA was detectable over an extended period, culminating approximately 8-13 days following peak spawning, suggesting developing smelt larvae might be the primary source of eDNA. Subsequently, smelt eDNA was readily detected in eDNA surveys of four streams, particularly following remediation of PCR inhibitors. Hierarchical occupancy modeling confirmed our surveys had high empirical detection for most sites, and that future surveys employing at least three sampling events, three samples per event, and six qPCR replicates can afford greater than 90% combined detection capability in low abundance systems.

CONCLUSIONS

These results demonstrate that relatively modest eDNA sampling effort has high capacity to detect this ephemerally present species of concern at low to moderate abundances. As such, smelt eDNA detection could improve range mapping by providing longer survey windows, safer sampling conditions, and lower field effort in low density systems, than afforded by existing visual and netting approaches.

Simulated treatment effects on bird communities inform landscape-scale dry conifer forest management

Human land use and climate change have increased forest density and wildfire risk in dry conifer forests of western North America, threatening various ecosystem services, including habitat for wildlife. Government policy supports active management to restore historical structure and ecological function. Information on potential contributions of restoration to wildlife habitat can allow assessment of tradeoffs with other ecological benefits when prioritizing treatments. We predicted avian responses to simulated treatments representing alternative scenarios to inform landscape-scale forest management planning along the Colorado Front Range. We used data from the Integrated Monitoring in Bird Conservation Regions program to inform a hierarchical multispecies occupancy model relating species occupancy and richness with canopy cover at two spatial scales. We then simulated changes in canopy cover (remotely sensed in 2018) under three alternative scenarios, (1) a "fuels reduction" scenario representing landscape-wide 30% reduction in canopy cover, (2) a "restoration" scenario representing more nuanced, spatially variable treatments targeting historical conditions, and (3) a reference, no-change scenario. Model predictions showed areas of potential gains and losses for species richness, richness of ponderosa pine forest habitat specialists, and the ratio of specialists to generalists at two (1 km² and 250 m² ) spatial scales. Under both fuels reduction and restoration scenarios, we projected greater gains than losses for species richness. Surprisingly, despite restoration more explicitly targeting ecologically relevant historical conditions, fuels reduction benefited bird species richness over a greater spatial extent than restoration, particularly in the lower montane life zone. These benefits reflected generally positive species associations with moderate canopy cover promoted more consistently under the fuels reduction scenario. In practice, contemporary forest management is likely to lie somewhere between the fuels reduction and restoration scenarios represented here. Therefore, our results inform where and how active forest management can best support avian diversity. Although our study raises questions regarding the value of including landscape-scale heterogeneity as a management objective, we do not question the value of targeting finer scale heterogeneity (i.e., stand and treatment level). Rather, our results combined with those from previous work clarify the scale at which targeting structural heterogeneity and historical reference conditions can promote particular ecosystem services.

Landscape-scale conservation mitigates the biodiversity loss of grassland birds

The decline of biodiversity from anthropogenic landscape modification is among the most pressing conservation problems worldwide. In North America, long-term population declines have elevated the recovery of the grassland avifauna to among the highest conservationpriorities. Because the vast majority of grasslands of the Great Plains are privately owned, the recovery of these ecosystems and bird populations within them depend on landscape-scale conservation strategies that integrate social, economic, and biodiversity objectives. The Conservation Reserve Program (CRP) is a voluntary program for private agricultural producers administered by the United States Department of Agriculture that provides financial incentives to take cropland out of production and restore perennial grassland. We investigated spatial patterns of grassland availability and restoration to inform landscape-scale conservation for a comprehensive community of grassland birds in the Great Plains. The research objectives were to (1) determine how apparent habitat loss has affected spatial patterns of grassland bird biodiversity, (2) evaluate the effectiveness of CRP for offsetting the biodiversity declines of grassland birds, and (3) develop spatially explicit predictions to estimate the biodiversity benefit of adding CRP to landscapes impacted by habitat loss. We used the Integrated Monitoring in Bird Conservation Regions program to evaluate hypotheses for the effects of habitat loss and restoration on both the occupancy and species richness of grassland specialists within a continuum-modeling framework. We found the odds of community occupancy declined by 37% for every 1 SD decrease in grassland availability [log_e (km² )] and increased by 20% for every 1 SD increase in CRP land cover [log_e (km² )]. There was 17% turnover in species composition between intact grasslands and CRP landscapes, suggesting that grasslands restored by CRP retained considerable, but incomplete, representation of biodiversity in agricultural landscapes. Spatially explicit predictions indicated that absolute conservation outcomes were greatest at high latitudes in regions with high biodiversity, whereas the relative outcomes were greater at low latitudes in highly modified landscapes. By evaluating community-wide responses to landscape modification and CRP restoration at bioregional scales, our study fills key information gaps for developing collaborative strategies, and for balancing conservation of avian biodiversity and social well-being in the agricultural production landscapes of the Great Plains.

Empirical evaluation of the spatial scale and detection process of camera trap surveys

BACKGROUND

Camera traps present a valuable tool for monitoring animals but detect species imperfectly. Occupancy models are frequently used to address this, but it is unclear what spatial scale the data represent. Although individual cameras monitor animal activity within a small target window in front of the device, many practitioners use these data to infer animal presence over larger, vaguely-defined areas. Animal movement is generally presumed to link these scales, but fine-scale heterogeneity in animal space use could disrupt this relationship.

METHODS

We deployed cameras at 10 m intervals across a 0.6 ha forest plot to create an unprecedentedly dense sensor array that allows us to compare animal detections at these two scales. Using time-stamped camera detections we reconstructed fine-scale movement paths of four mammal species and characterized (a) how well animal use of a single camera represented use of the surrounding plot, (b) how well cameras detected animals, and (c) how these processes affected overall detection probability, p. We used these observations to parameterize simulations that test the performance of occupancy models in realistic scenarios.

RESULTS

We document two important aspects of animal movement and how it affects sampling with passive detectors. First, animal space use is heterogeneous at the camera-trap scale, and data from a single camera may poorly represent activity in its surroundings. Second, cameras frequently (14-71%) fail to record passing animals. Our simulations show how this heterogeneity can introduce unmodeled variation into detection probability, biasing occupancy estimates for species with low p.

CONCLUSIONS

Occupancy or population estimates with camera traps could be improved by increasing camera reliability to reduce missed detections, adding covariates to model heterogeneity in p, or increasing the area sampled by each camera through different sampling designs or technologies.

An ecotoxicological approach to microplastics on terrestrial and aquatic organisms: A systematic review in assessment, monitoring and biological impact

Marine and land plastic debris biodegrades at micro- and nanoscales through progressive fragmentation. Oceanographic model studies confirm the presence of up to ∼2.41 million tons of microplastics across the Atlantic, Pacific, and Indian subtropical gyres. Microplastics distribute from primary (e.g., exfoliating cleansers) and secondary (e.g., chemical deterioration) sources in the environment. This anthropogenic phenomenon poses a threat to the flora and fauna of terrestrial and aquatic ecosystems as ingestion and entanglement cases increase over time. This review focuses on the impact of microplastics across taxa at suggested environmentally relevant concentrations, and advances the groundwork for future ecotoxicological-based research on microplastics including the main points: (i) adhesion of chemical pollutants (e.g., PCBs); (ii) biological effects (e.g., bioaccumulation, biomagnification, biotransportation) in terrestrial and aquatic organisms; (iii) physico-chemical properties (e.g., polybrominated diphenyl ethers) and biodegradation pathways in the environment (e.g., chemical stress, heat stress); and (iv) an ecotoxicological prospect for optimized impact assessments.

A Bayesian nested patch occupancy model to estimate steelhead movement and abundance

Anthropogenic impacts on riverine systems have, in part, led to management concerns regarding the population status of species using these systems. In an effort to assess the efficacy of restoration actions, and in order to improve monitoring of species of concern, managers have turned to PIT (passive integrated transponder) tag studies with in-stream detectors to monitor movements of tagged individuals throughout river networks. However, quantifying movements in a river network using PIT tag data with incomplete coverage and imperfect detections presents a challenge. We propose a flexible Bayesian analytic framework that models the imperfectly detected movements of tagged individuals in a nested PIT tag array river network. This model structure provides probabilistic estimates of up-stream migration routes for each tagged individual based on a set of underlying nested state variables. These movement estimates can be converted into abundance estimates when an estimate of abundance is available for a location within the river network. We apply the model framework to data from steelhead (Oncorhynchus mykiss) in the Upper Columbia River basin and evaluate model performance (precision/variance of simulated population sizes) as a function of population tagging rates and PIT tag array detection probability densities within the river system using a simulation framework. This simulation framework provides both model validation (precision) and the ability to evaluate expected performance improvements (variance) due to changes in tagging rates or PIT receiver array configuration. We also investigate the impact of different network configurations on model estimates. Results from such investigations can help inform decisions regarding future monitoring and management.

Using environmental DNA and occupancy modelling to estimate rangewide metapopulation dynamics

We demonstrate the power of combining two emergent tools for resolving rangewide metapopulation dynamics. First, we employed environmental DNA (eDNA) surveys to efficiently generate multiseason rangewide site occupancy histories. Second, we developed a novel dynamic, spatial multiscale occupancy model to estimate metapopulation dynamics. The model incorporates spatial relationships, explicitly accounts for non-detection bias and allows direct evaluation of the drivers of extinction and colonization. We applied these tools to examine metapopulation dynamics of endangered tidewater goby, a species endemic to California estuarine habitats. We analysed rangewide eDNA data from 190 geographically isolated sites (813 total water samples) surveyed from 2 years (2016 and 2017). Rangewide estimates of the proportion of sites that were occupied varied little between 2016 (0.52) and 2017 (0.51). However, there was evidence of extinction and colonization dynamics. The probability of extinction of an occupied site (0.106) and probability of colonization of an unoccupied site (0.085) were nearly equal. Stability in site occupancy proportions combined with nearly equal rates of extinction and colonization suggests a dynamic equilibrium between the 2 years surveyed. Assessment of covariate effects revealed that colonization probability increased as the number of occupied neighbouring sites increased and as distance between occupied sites decreased. We show that eDNA surveys can rapidly provide a snapshot of a species distribution over a broad geographic range and, when these surveys are paired with occupancy modelling, can uncover metapopulation dynamics and their drivers.

Maximizing the value of forest restoration for tropical mammals by detecting three-dimensional habitat associations

Tropical forest ecosystems are facing unprecedented levels of degradation, severely compromising habitat suitability for wildlife. Despite the fundamental role biodiversity plays in forest regeneration, identifying and prioritizing degraded forests for restoration or conservation, based on their wildlife value, remains a significant challenge. Efforts to characterize habitat selection are also weakened by simple classifications of human-modified tropical forests as intact vs. degraded, which ignore the influence that three-dimensional (3D) forest structure may have on species distributions. Here, we develop a framework to identify conservation and restoration opportunities across logged forests in Borneo. We couple high-resolution airborne light detection and ranging (LiDAR) and camera trap data to characterize the response of a tropical mammal community to changes in 3D forest structure across a degradation gradient. Mammals were most responsive to covariates that accounted explicitly for the vertical and horizontal characteristics of the forest and actively selected structurally complex environments comprising tall canopies, increased plant area index throughout the vertical column, and the availability of a greater diversity of niches. We show that mammals are sensitive to structural simplification through disturbance, emphasizing the importance of maintaining and enhancing structurally intact forests. By calculating occurrence thresholds of species in response to forest structural change, we identify areas of degraded forest that would provide maximum benefit for multiple high-conservation value species if restored. The study demonstrates the advantages of using LiDAR to map forest structure, rather than relying on overly simplistic classifications of human-modified tropical forests, for prioritizing regions for restoration.

Cannot see the diversity for all the species: Evaluating inclusion criteria for local species lists when using abundant citizen science data

Abundant citizen science data on species occurrences are becoming increasingly available and enable identifying composition of communities occurring at multiple sites with high temporal resolution. However, for species displaying temporary patterns of local occurrences that are transient to some sites, biodiversity measures are clearly dependent on the criteria used to include species into local species lists. Using abundant opportunistic citizen science data from frequently visited wetlands, we investigated the sensitivity of α- and β-diversity estimates to the use raw versus detection-corrected data and to the use of inclusion criteria for species presence reflecting alternative site use. We tested seven inclusion criteria (with varying number of days required to be present) on time series of daily occurrence status during a breeding season of 90 days for 77 wetland bird species. We show that even when opportunistic presence-only observation data are abundant, raw data may not produce reliable local species richness estimates and rank sites very differently in terms of species richness. Furthermore, occupancy model based α- and β-diversity estimates were sensitive to the inclusion criteria used. Total species lists (all species observed at least once during a season) may therefore mask diversity differences among sites in local communities of species, by including vagrant species on potentially breeding communities and change the relative rank order of sites in terms of species richness. Very high sampling effort does not necessarily free opportunistic data from its inherent bias and can produce a pattern in which many species are observed at least once almost everywhere, thus leading to a possible paradox: The large amount of biological information may hinder its usefulness. Therefore, when prioritizing among sites to manage or preserve species diversity estimates need to be carefully related to relevant inclusion criteria depending on the diversity estimate in focus.

Hierarchical multi-grain models improve descriptions of species' environmental associations, distribution, and abundance

The characterization of species' environmental niches and spatial distribution predictions based on them are now central to much of ecology and conservation, but implicitly requires decisions about the appropriate spatial scale (i.e., grain) of analysis. Ecological theory and empirical evidence suggest that range-resident species respond to their environment at two characteristic, hierarchical spatial grains: (1) response grain, the (relatively fine) grain at which an individual uses environmental resources, and (2) occupancy grain, the (relatively coarse) grain equivalent to a typical home range. We use a multi-grain (MG) occupancy model, aided by fine-grain remotely sensed imagery, to simultaneously estimate species-environment associations at both grains, conduct grain optimization to measure response grain, and apply this analysis framework to an example species: a medium-sized bird (Tockus deckeni) in a heterogeneous East African landscape. Based on home range analysis of movement data, we calculate an occupancy grain of 1 km for T. deckeni. Using a grain optimization procedure across 32 grains from 10 to 500 m, we identify 60 m as the most strongly supported response grain for a suite of environmental variables, slightly coarser than opportunistic behavioral observations would have suggested. Validation confirms that the accuracy of the optimized MG occupancy model substantially exceeds that of equivalent single-grain (SG) occupancy models. We further use a simulation approach to assess the potential impacts of accounting for the multi-scale structure of species' environmental requirements on estimates of population size. We find that the more strongly supported MG approach consistently predicts a minimum population size in the study landscape that is much lower than that provided by the SG model. This suggests that SG approaches commonly used in conservation applications could lead to overly optimistic abundance and population estimates, and that the MG approach may be more appropriate for supporting species conservation goals. More generally, we conclude that multi-grain approaches of the sort presented, and increasingly enabled by growing high-resolution remotely sensed data, hold great promise for offering a more mechanistic framework for assessing the appropriate grain(s) for population monitoring and management and enable more reliable estimates of abundances and species' distributions.

Dry conifer forest restoration benefits Colorado Front Range avian communities

Fire suppression has increased stand density and risk of severe, stand-replacing wildfire in lower elevation dry conifer forests of western North America, threatening ecological function. The U.S. Forest Service's Collaborative Forest Landscape Restoration Program (CFLRP) aims to mitigate impacts to ecological function, while mandating effectiveness monitoring to verify restoration success. Expected benefits include improved conditions for biodiversity, but relatively few empirical studies evaluate restoration effects on biodiversity. We applied the Integrated Monitoring in Bird Conservation Regions program to survey birds in relation to CFLRP treatments along the Colorado Front Range in 2015-2017. We employed hierarchical models to analyze species occupancy and richness at 1972 points nested within 141 1-km² grid cells. Our objectives were to investigate (1) species occupancy relationships with treatments at local (point) and landscape (grid) spatial scales, (2) potential mechanisms for treatment relationships considering species and treatment relationships with forest structure and composition (i.e., habitat relationships), and (3) treatment and habitat relationships with species richness. The data supported positive and negative point-level treatment relationships, suggesting uneven species distributions between treated and untreated points. At the grid scale, however, we only found positive species relationships with percent area treated, and accordingly, grid-level species richness increased with treatment extent. Potential mechanisms for treatment relationships included treatments generating foraging opportunities for aerial insectivores by opening the canopy, improving conditions for ground-associated species by increasing herbaceous growth, and limiting opportunities for shrub-nesting species by reducing shrub cover. Landscape-scale patterns suggest CFLRP treatments can benefit avian communities by generating habitat for open-forest species without necessarily eliminating habitat for closed-forest species. Our results provide evidence for a commonly expected but rarely verified pattern of increased species richness with forest heterogeneity. We suggest restoration treatments will most benefit forest bird diversity by reducing canopy cover, encouraging herbaceous ground cover, limiting ladder fuel species, and encouraging shrub diversity in canopy openings, while maintaining some dense forest stands on the landscape.

Integrating data from different survey types for population monitoring of an endangered species: the case of the Eld's deer

Despite its value for conservation decision-making, we lack information on population abundances for most species. Because establishing large-scale monitoring schemes is rarely feasible, statistical methods that combine multiple data sources are promising approaches to maximize use of available information. We built a Bayesian hierarchical model that combined different survey data of the endangered Eld’s deer in Shwesettaw Wildlife Sanctuary (SWS) in Myanmar and tested our approach in simulation experiments. We combined spatially-restricted line-transect abundance data with more spatially-extensive camera-trap occupancy data to enable estimation of the total deer abundance. The integrated model comprised an ecological model (common to both survey types, based on the equivalence between cloglog-transformed occurrence probability and log-transformed expected abundance) and separate observation models for each survey type. We estimated that the population size of Eld’s deer in SWS is c. 1519 (1061–2114), suggesting it is the world’s largest wild population. The simulations indicated that the potential benefits of combining data include increased precision and better sampling of the spatial variation in the environment, compared to separate analysis of each survey. Our analytical approach, which integrates the strengths of different survey methods, has widespread application for estimating species’ abundances, especially in information-poor regions of the world.

Framing pictures: A conceptual framework to identify and correct for biases in detection probability of camera traps enabling multi-species comparison

Obtaining reliable species observations is of great importance in animal ecology and wildlife conservation. An increasing number of studies use camera traps (CTs) to study wildlife communities, and an increasing effort is made to make better use and reuse of the large amounts of data that are produced. It is in these circumstances that it becomes paramount to correct for the species- and study-specific variation in imperfect detection within CTs. We reviewed the literature and used our own experience to compile a list of factors that affect CT detection of animals. We did this within a conceptual framework of six distinct scales separating out the influences of (a) animal characteristics, (b) CT specifications, (c) CT set-up protocols, and (d) environmental variables. We identified 40 factors that can potentially influence the detection of animals by CTs at these six scales. Many of these factors were related to only a few overarching parameters. Most of the animal characteristics scale with body mass and diet type, and most environmental characteristics differ with season or latitude such that remote sensing products like NDVI could be used as a proxy index to capture this variation. Factors that influence detection at the microsite and camera scales are probably the most important in determining CT detection of animals. The type of study and specific research question will determine which factors should be corrected. Corrections can be done by directly adjusting the CT metric of interest or by using covariates in a statistical framework. Our conceptual framework can be used to design better CT studies and help when analyzing CT data. Furthermore, it provides an overview of which factors should be reported in CT studies to make them repeatable, comparable, and their data reusable. This should greatly improve the possibilities for global scale analyses of (reused) CT data.

A dynamic multi-scale occupancy model to estimate temporal dynamics and hierarchical habitat use for nomadic species

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

Environmental DNA analysis of river herring in Chesapeake Bay: A powerful tool for monitoring threatened keystone species

Environmental DNA (eDNA) sampling has emerged as a powerful tool to detect and quantify species abundance in aquatic environments. However, relatively few studies have compared the performance of eDNA-based abundance estimates to traditional catch or survey approaches in the field. Here, we have developed and field-tested a qPCR assay to detect eDNA from alewife and blueback herring (collectively known as ‘river herring’), comparing eDNA-based presence and abundance data to traditional methods of quantification (ichthyoplankton sampling and adult observations). Overall, the qPCR assay showed very high target specificity in lab trials, and was successful in detecting river herring for 11/12 Chesapeake Bay tributaries in spring 2015 and 2016, with 106 out of 445 samples exhibiting positive eDNA hits. We found a strong correlation between eDNA abundance and ichthyoplankton count data (Spearman’s Rho = 0.52), and Phi-tests (correlation of presence/absence data) showed higher correlation between eDNA and ichthyoplankton data (Phi = 0.45) than adult data (Phi = 0.35). Detection probability was significantly lower on western vs. eastern shore tributaries of Chesapeake Bay, and blueback herring and alewife were more likely detected on the western and eastern shores, respectively. Temporal patterns of eDNA abundance over the spring spawning season revealed that alewife were present in high abundances weeks ahead of blueback herring, which aligns with known differences in spawning behavior of the species. In summary, the eDNA abundance data corresponded well to other field methods and has great potential to assist future monitoring efforts of river herring abundance and habitat use.

ednaoccupancy: An r package for multiscale occupancy modelling of environmental DNA data

In this article, we describe ednaoccupancy, an r package for fitting Bayesian, multiscale occupancy models. These models are appropriate for occupancy surveys that include three nested levels of sampling: primary sample units within a study area, secondary sample units collected from each primary unit and replicates of each secondary sample unit. This design is commonly used in occupancy surveys of environmental DNA (eDNA). ednaoccupancy allows users to specify and fit multiscale occupancy models with or without covariates, to estimate posterior summaries of occurrence and detection probabilities, and to compare different models using Bayesian model-selection criteria. We illustrate these features by analysing two published data sets: eDNA surveys of a fungal pathogen of amphibians and eDNA surveys of an endangered fish species.

A statistically rigorous sampling design to integrate avian monitoring and management within Bird Conservation Regions

Monitoring is an essential component of wildlife management and conservation. However, the usefulness of monitoring data is often undermined by the lack of 1) coordination across organizations and regions, 2) meaningful management and conservation objectives, and 3) rigorous sampling designs. Although many improvements to avian monitoring have been discussed, the recommendations have been slow to emerge in large-scale programs. We introduce the Integrated Monitoring in Bird Conservation Regions (IMBCR) program designed to overcome the above limitations. Our objectives are to outline the development of a statistically defensible sampling design to increase the value of large-scale monitoring data and provide example applications to demonstrate the ability of the design to meet multiple conservation and management objectives. We outline the sampling process for the IMBCR program with a focus on the Badlands and Prairies Bird Conservation Region (BCR 17). We provide two examples for the Brewer's sparrow (Spizella breweri) in BCR 17 demonstrating the ability of the design to 1) determine hierarchical population responses to landscape change and 2) estimate hierarchical habitat relationships to predict the response of the Brewer's sparrow to conservation efforts at multiple spatial scales. The collaboration across organizations and regions provided economy of scale by leveraging a common data platform over large spatial scales to promote the efficient use of monitoring resources. We designed the IMBCR program to address the information needs and core conservation and management objectives of the participating partner organizations. Although it has been argued that probabilistic sampling designs are not practical for large-scale monitoring, the IMBCR program provides a precedent for implementing a statistically defensible sampling design from local to bioregional scales. We demonstrate that integrating conservation and management objectives with rigorous statistical design and analyses ensures reliable knowledge about bird populations that is relevant and integral to bird conservation at multiple scales.

Improving inference for aerial surveys of bears: The importance of assumptions and the cost of unnecessary complexity

Obtaining useful estimates of wildlife abundance or density requires thoughtful attention to potential sources of bias and precision, and it is widely understood that addressing incomplete detection is critical to appropriate inference. When the underlying assumptions of sampling approaches are violated, both increased bias and reduced precision of the population estimator may result. Bear (Ursus spp.) populations can be difficult to sample and are often monitored using mark‐recapture distance sampling (MRDS) methods, although obtaining adequate sample sizes can be cost prohibitive. With the goal of improving inference, we examined the underlying methodological assumptions and estimator efficiency of three datasets collected under an MRDS protocol designed specifically for bears. We analyzed these data using MRDS, conventional distance sampling (CDS), and open‐distance sampling approaches to evaluate the apparent bias‐precision tradeoff relative to the assumptions inherent under each approach. We also evaluated the incorporation of informative priors on detection parameters within a Bayesian context. We found that the CDS estimator had low apparent bias and was more efficient than the more complex MRDS estimator. When combined with informative priors on the detection process, precision was increased by >50% compared to the MRDS approach with little apparent bias. In addition, open‐distance sampling models revealed a serious violation of the assumption that all bears were available to be sampled. Inference is directly related to the underlying assumptions of the survey design and the analytical tools employed. We show that for aerial surveys of bears, avoidance of unnecessary model complexity, use of prior information, and the application of open population models can be used to greatly improve estimator performance and simplify field protocols. Although we focused on distance sampling‐based aerial surveys for bears, the general concepts we addressed apply to a variety of wildlife survey contexts.

Patterns of Snow Leopard Site Use in an Increasingly Human-Dominated Landscape

Human population growth and concomitant increases in demand for natural resources pose threats to many wildlife populations. The landscapes used by the endangered snow leopard (Panthera uncia) and their prey is increasingly subject to major changes in land use. We aimed to assess the influence of 1) key human activities, as indicated by the presence of mining and livestock herding, and 2) the presence of a key prey species, the blue sheep (Pseudois nayaur), on probability of snow leopard site use across the landscape. In Gansu Province, China, we conducted sign surveys in 49 grid cells, each of 16 km² in size, within a larger area of 3392 km². We analysed the data using likelihood-based habitat occupancy models that explicitly account for imperfect detection and spatial auto-correlation between survey transect segments. The model-averaged estimate of snow leopard occupancy was high [0.75 (SE 0.10)], but only marginally higher than the naïve estimate (0.67). Snow leopard segment-level probability of detection, given occupancy on a 500 m spatial replicate, was also high [0.68 (SE 0.08)]. Prey presence was the main determinant of snow leopard site use, while human disturbances, in the form of mining and herding, had low predictive power. These findings suggest that snow leopards continue to use areas very close to such disturbances, as long as there is sufficient prey. Improved knowledge about the effect of human activity on large carnivores, which require large areas and intact prey populations, is urgently needed for conservation planning at the local and global levels. We highlight a number of methodological considerations that should guide the design of such research.

Trophic dynamics of shrinking Subarctic lakes: naturally eutrophic waters impart resilience to rising nutrient and major ion concentrations

Shrinking lakes were recently observed for several Arctic and Subarctic regions due to increased evaporation and permafrost degradation. Along with lake drawdown, these processes often boost aquatic chemical concentrations, potentially impacting trophic dynamics. In particular, elevated chemical levels may impact primary productivity, which may in turn influence populations of primary and secondary consumers. We examined trophic dynamics of 18 shrinking lakes of the Yukon Flats, Alaska, that had experienced pronounced increases in nutrient (>200 % total nitrogen, >100 % total phosphorus) and ion concentrations (>100 % for four major ions combined) from 1985-1989 to 2010-2012, versus 37 stable lakes with relatively little chemical change over the same period. We found that phytoplankton stocks, as indexed by chlorophyll concentrations, remained unchanged in both shrinking and stable lakes from the 1980s to 2010s. Moving up the trophic ladder, we found significant changes in invertebrate abundance across decades, including decreased abundance of five of six groups examined. However, these decadal losses in invertebrate abundance were not limited to shrinking lakes, occurring in lakes with stable surface areas as well. At the top of the food web, we observed that probabilities of lake occupancy for ten waterbird species, including adults and chicks, remained unchanged from the period 1985-1989 to 2010-2012. Overall, our study lakes displayed a high degree of resilience to multi-trophic cascades caused by rising chemical concentrations. This resilience was likely due to their naturally high fertility, such that further nutrient inputs had little impact on waters already near peak production.

Evaluating Multi-Level Models to Test Occupancy State Responses of Plethodontid Salamanders

Plethodontid salamanders are diverse and widely distributed taxa and play critical roles in ecosystem processes. Due to salamander use of structurally complex habitats, and because only a portion of a population is available for sampling, evaluation of sampling designs and estimators is critical to provide strong inference about Plethodontid ecology and responses to conservation and management activities. We conducted a simulation study to evaluate the effectiveness of multi-scale and hierarchical single-scale occupancy models in the context of a Before-After Control-Impact (BACI) experimental design with multiple levels of sampling. Also, we fit the hierarchical single-scale model to empirical data collected for Oregon slender and Ensatina salamanders across two years on 66 forest stands in the Cascade Range, Oregon, USA. All models were fit within a Bayesian framework. Estimator precision in both models improved with increasing numbers of primary and secondary sampling units, underscoring the potential gains accrued when adding secondary sampling units. Both models showed evidence of estimator bias at low detection probabilities and low sample sizes; this problem was particularly acute for the multi-scale model. Our results suggested that sufficient sample sizes at both the primary and secondary sampling levels could ameliorate this issue. Empirical data indicated Oregon slender salamander occupancy was associated strongly with the amount of coarse woody debris (posterior mean = 0.74; SD = 0.24); Ensatina occupancy was not associated with amount of coarse woody debris (posterior mean = -0.01; SD = 0.29). Our simulation results indicate that either model is suitable for use in an experimental study of Plethodontid salamanders provided that sample sizes are sufficiently large. However, hierarchical single-scale and multi-scale models describe different processes and estimate different parameters. As a result, we recommend careful consideration of study questions and objectives prior to sampling data and fitting models.

Estimating occupancy dynamics for large-scale monitoring networks: amphibian breeding occupancy across protected areas in the northeast United States

Regional monitoring strategies frequently employ a nested sampling design where a finite set of study areas from throughout a region are selected and intensive sampling occurs within a subset of sites within the individual study areas. This sampling protocol naturally lends itself to a hierarchical analysis to account for dependence among subsamples. Implementing such an analysis using a classic likelihood framework is computationally challenging when accounting for detection errors in species occurrence models. Bayesian methods offer an alternative approach for fitting models that readily allows for spatial structure to be incorporated. We demonstrate a general approach for estimating occupancy when data come from a nested sampling design. We analyzed data from a regional monitoring program of wood frogs (Lithobates sylvaticus) and spotted salamanders (Ambystoma maculatum) in vernal pools using static and dynamic occupancy models. We analyzed observations from 2004 to 2013 that were collected within 14 protected areas located throughout the northeast United States. We use the data set to estimate trends in occupancy at both the regional and individual protected area levels. We show that occupancy at the regional level was relatively stable for both species. However, substantial variation occurred among study areas, with some populations declining and some increasing for both species. In addition, When the hierarchical study design is not accounted for, one would conclude stronger support for latitudinal gradient in trends than when using our approach that accounts for the nested design. In contrast to the model that does not account for nesting, the nested model did not include an effect of latitude in the 95% credible interval. These results shed light on the range‐level population status of these pond‐breeding amphibians, and our approach provides a framework that can be used to examine drivers of local and regional occurrence dynamics.

Multiple cameras required to reliably detect feral cats in northern Australian tropical savanna: an evaluation of sampling design when using camera traps

Context Feral cats are a major cause of mammal declines and extinctions in Australia. However, cats are elusive and obtaining reliable ecological data is challenging. Although camera traps are increasingly being used to study feral cats, their successful use in northern Australia has been limited. Aims We evaluated the efficacy of camera-trap sampling designs for detecting cats in the tropical savanna of northern Australia. We aimed to develop a camera-trapping method that would yield detection probabilities adequate for precise occupancy estimates. Methods First, we assessed the influence of two micro-habitat placements and three lure types on camera-trap detection rates of feral cats. Second, using multiple camera traps at each site, we examined the relationship between sampling effort and detection probability by using a multi-method occupancy model. Key results We found no significant difference in detection rates of feral cats using a variety of lures and micro-habitat placement. The mean probability of detecting a cat on one camera during one week of sampling was very low (p = 0.15) and had high uncertainty. However, the probability of detecting a cat on at least one of five cameras deployed concurrently on a site was 48% higher (p = 0.22) and had a greater precision. Conclusions The sampling effort required to achieve detection rates adequate to infer occupancy of feral cats by camera trap is considerably higher in northern Australia than has been observed elsewhere in Australia. Adequate detection of feral cats in the tropical savanna of northern Australia will necessitate inclusion of more camera traps and a longer survey duration. Implications Sampling designs using camera traps need to be rigorously trialled and assessed to optimise detection of the target species for different Australian biomes. A standard approach is suggested for detecting feral cats in northern Australian savannas.

On a dhole trail: examining ecological and anthropogenic correlates of dhole habitat occupancy in the Western ghats of India

Although they play a critical role in shaping ecological communities, many threatened predator species are data-deficient. The Dhole Cuon alpinus is one such rare canid with a global population thought to be <2500 wild individuals. We assessed habitat occupancy patterns of dholes in the Western Ghats of Karnataka, India, to understand ecological and anthropogenic determinants of their distribution and habitat-use. We conducted spatially replicated detection/non-detection surveys of dhole signs along forest trails at two appropriate scales: the entire landscape and a single wildlife reserve. Landscape-scale habitat occupancy was assessed across 38,728 km² surveying 206 grid cells of 188-km² each. Finer scale habitat-use within 935 km² Bandipur Reserve was studied surveying 92 grid cells of 13-km² km each. We analyzed the resulting data of dhole signs using likelihood-based habitat occupancy models. The models explicitly addressed the problematic issue of imperfect detection of dhole signs during field surveys as well as potential spatial auto-correlation between sign detections made on adjacent trail segments. We show that traditional ‘presence versus absence’ analyses underestimated dhole habitat occupancy by 60% or 8682 km² [naïve = 0.27; (SE) = 0.68 (0.08)] in the landscape. Addressing imperfect sign detections by estimating detection probabilities [_(L) (SE) = 0.12 (0.11)] was critical for reliable estimation. Similar underestimation occurred while estimating habitat-use probability at reserve-scale [naïve = 0.39; (SE) = 0.71 (0.06)]. At landscape scale, relative abundance of principal ungulate prey primarily influenced dhole habitat occupancy. Habitat-use within a reserve, however, was predominantly and negatively influenced by anthropogenic disturbance. Our results are the first rigorous assessment of dhole occupancy at multiple spatial scales with potential conservation value. The approach used in this study has potential utility for cost-effectively assessing spatial distribution and habitat-use in other species, landscapes and reserves.

Coffee agroforests remain beneficial for neotropical bird community conservation across seasons

Coffee agroforestry systems and secondary forests have been shown to support similar bird communities but comparing these habitat types are challenged by potential biases due to differences in detectability between habitats. Furthermore, seasonal dynamics may influence bird communities differently in different habitat types and therefore seasonal effects should be considered in comparisons. To address these issues, we incorporated seasonal effects and factors potentially affecting bird detectability into models to compare avian community composition and dynamics between coffee agroforests and secondary forest fragments. In particular, we modeled community composition and community dynamics of bird functional groups based on habitat type (coffee agroforest vs. secondary forest) and season while accounting for variation in capture probability (i.e. detectability). The models we used estimated capture probability to be similar between habitat types for each dietary guild, but omnivores had a lower capture probability than frugivores and insectivores. Although apparent species richness was higher in coffee agroforest than secondary forest, model results indicated that omnivores and insectivores were more common in secondary forest when accounting for heterogeneity in capture probability. Our results largely support the notion that shade-coffee can serve as a surrogate habitat for secondary forest with respect to avian communities. Small coffee agroforests embedded within the typical tropical countryside matrix of secondary forest patches and small-scale agriculture, therefore, may host avian communities that resemble those of surrounding secondary forest, and may serve as viable corridors linking patches of forest within these landscapes. This information is an important step toward effective landscape-scale conservation in Neotropical agricultural landscapes.