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A ‘how-to’ guide for estimating animal diel activity using hierarchical models

Animal diel activity patterns can aid understanding of (a) how species behaviourally adapt to anthropogenic and natural disturbances, (b) mechanisms of species co-existence through temporal partitioning, and (c) community or ecosystem effects of diel activity shifts. Activity patterns often vary spatially, a feature ignored by the kernel density estimators (KDEs) currently used for estimating diel activity. Ignoring this source of heterogeneity may lead to biased estimates of uncertainty and misleading conclusions regarding the drivers of diel activity. Thus, there is a need for more flexible statistical approaches for estimating activity patterns and testing hypotheses regarding their biotic and abiotic drivers. We illustrate how trigonometric terms and cyclic cubic splines combined with hierarchical models can provide a valuable alternative to KDEs. Like KDEs, these models accommodate circular data, but they can also account for site-to-site and other sources of variability, correlation amongst repeated measures, and variable sampling effort. They can also more readily quantify and test hypotheses related to the effects of covariates on activity patterns. Through empirical case studies, we illustrate how hierarchical models can quantify changes in activity levels due to seasonality and in response to biotic and abiotic factors (e.g. anthropogenic stressors and co-occurrence). We also describe frequentist and Bayesian approaches for quantifying site-specific (conditional) and population-averaged (marginal) activity patterns. We provide guidelines and tutorials with detailed step-by-step instructions for fitting and interpreting hierarchical models applied to time-stamped data, such as those recorded by camera traps and audio recorders. We conclude that this approach offers a viable, flexible, and effective alternative to KDEs when modelling animal activity patterns.

Journal of Animal Ecology↗

State-and-transition simulation models: a framework for forecasting landscape change

Summary A wide range of spatially explicit simulation models have been developed to forecast landscape dynamics, including models for projecting changes in both vegetation and land use. While these models have generally been developed as separate applications, each with a separate purpose and audience, they share many common features. We present a general framework, called a state-and-transition simulation model (STSM), which captures a number of these common features, accompanied by a software product, called ST-Sim, to build and run such models. The STSM method divides a landscape into a set of discrete spatial units and simulates the discrete state of each cell forward as a discrete-time-inhomogeneous stochastic process. The method differs from a spatially interacting Markov chain in several important ways, including the ability to add discrete counters such as age and time-since-transition as state variables, to specify one-step transition rates as either probabilities or target areas, and to represent multiple types of transitions between pairs of states. We demonstrate the STSM method using a model of land-use/land-cover (LULC) change for the state of Hawai'i, USA. Processes represented in this example include expansion/contraction of agricultural lands, urbanization, wildfire, shrub encroachment into grassland and harvest of tree plantations; the model also projects shifts in moisture zones due to climate change. Key model output includes projections of the future spatial and temporal distribution of LULC classes and moisture zones across the landscape over the next 50 years. State-and-transition simulation models can be applied to a wide range of landscapes, including questions of both land-use change and vegetation dynamics. Because the method is inherently stochastic, it is well suited for characterizing uncertainty in model projections. When combined with the ST-Sim software, STSMs offer a simple yet powerful means for developing a wide range of models of landscape dynamics.

Methods in Ecology and Evolution↗

Moderating Argos location errors in animal tracking data

1. The Argos System is used worldwide to satellite-track free-ranging animals, but location errors can range from tens of metres to hundreds of kilometres. Low-quality locations (Argos classes A, 0, B and Z) dominate animal tracking data. Standard-quality animal tracking locations (Argos classes 3, 2 and 1) have larger errors than those reported in Argos manuals. 2. The Douglas Argos-filter (DAF) algorithm flags implausible locations based on user-defined thresholds that allow the algorithm's performance to be tuned to species' movement behaviours and study objectives. The algorithm is available in Movebank – a free online infrastructure for storing, managing, sharing and analysing animal movement data. 3. We compared 21,044 temporally paired global positioning system (GPS) locations with Argos location estimates collected from Argos transmitters on free-ranging waterfowl and condors (13 species, 314 individuals, 54,895 animal-tracking days). The 95th error percentiles for unfiltered Argos locations 0, A, B and Z were within 35·8, 59·6, 163·2 and 220·2 km of the true location, respectively. After applying DAF with liberal thresholds, roughly 20% of the class 0 and A locations and 45% of the class B and Z locations were excluded, and the 95th error percentiles were reduced to 17·2, 15·0, 20·9 and 18·6 km for classes 0, A, B and Z, respectively. As thresholds were applied more conservatively, fewer locations were retained, but they possessed higher overall accuracy. 4. Douglas Argos-filter can improve data accuracy by 50–90% and is an effective and flexible tool for preparing Argos data for direct biological interpretation or subsequent modelling.

Methods in Ecology and Evolution↗

Estimating population change from count data: Application to the North American Breeding Bird Survey

For birds and many other animal taxa, surveys that collect count data form a primary source of information on population change. Because counts are only indices to population size, care must be taken in using them in analyses of population change. Temporal or geographic differences in the proportion of animals counted can be misinterpreted as differences in population size. Therefore, temporally or geographically varying factors that influence the proportion of animals counted must be incorporated as covariables in the analysis of population parameters from count data. We describe the North American Breeding Bird Survey (BBS) for illustration. The BBS is a major, landscape-level survey of birds in North America; it is typical of many count surveys, in that the same sample units (survey routes) are sampled each year, and change is modeled on these routes over time. We identify covariables related to observer ability, the omission of which can bias estimation of population change from BBS data. Controlling for observer effects or other potential sources of confounding requires the specification of models relating counts to population size. We begin with a partial model specification relating expected counts to population sizes; we describe estimators currently in use in relation to this partial specification. Additional assumptions lead to a class of over-dispersed multinomial models, for which we describe estimators of population change and procedures for parsimonious model selection. We illustrate the use of over-dispersed multinomial models by an application to data for Carolina Wren (Thryothorus ludovicianus).

Ecological Applications↗

Landscape-based spatially explicit species index models for everglades restoration

As part of the effort to restore the ∼10 000-km 2 Everglades drainage in southern Florida, USA, we developed spatially explicit species index (SESI) models of a number of species and species groups. In this paper we describe the methodology and results of three such models: those for the Cape Sable Seaside Sparrow and the Snail Kite, and the species group model of long-legged wading birds. SESI models are designed to produce relative comparisons of one management alternative to a base scenario or to another alternative. The model outputs do not provide an exact quantitative prediction of future biotic group responses, but rather, when applying the same input data and different hydrologic plans, the models provide the best available means to compare the relative response of the biotic groups. We compared four alternative hydrologic management scenarios to a base scenario (i.e., predicted conditions assuming that current water management practices continue). We ranked the results of the comparisons for each set of models. No one scenario was beneficial to all species; however, they provide a uniform assessment, based on the best available observational information, of relative species responses to alternative water-management plans. As such, these models were used extensively in the restoration planning.

Florida↗

Ecosystem performance monitoring of rangelands by integrating modeling and remote sensing

Monitoring rangeland ecosystem dynamics, production, and performance is valuable for researchers and land managers. However, ecosystem monitoring studies can be difficult to interpret and apply appropriately if management decisions and disturbances are inseparable from the ecosystem's climate signal. This study separates seasonal weather influences from influences caused by disturbances and management decisions, making interannual time-series analysis more consistent and interpretable. We compared the actual ecosystem performance (AEP) of five rangeland vegetation types in the Owyhee Uplands for 9 yr to their expected ecosystem performance (EEP). Integrated growing season Normalized Difference Vegetation Index data for each of the nine growing seasons served as a proxy for annual AEP. Regression-tree models used long-term site potential, seasonal weather, and land cover data sets to generate annual EEP, an estimate of ecosystem performance incorporating annual weather variations. The difference between AEP and EEP provided a performance measure for each pixel in the study area. Ecosystem performance anomalies occurred when the ecosystem performed significantly better or worse than the model predicted. About 14% of the Owyhee Uplands showed a trend of significant underperformance or overperformance ( P <0.10). Land managers can use results from weather-based rangeland ecosystem performance models to help support adaptive management strategies.

Oregon;Idaho↗

Review of ESA SYMP 7: A dynamic perspective on ecosystem restoration–establishing temporal connectivity at the intersection between paleoecology and restoration ecology

Landscape connectivity is vital not only spatially, but also temporally; as ecosystems change, it is important to be aware of past, present, and future variables that may impact ecosystem function and biodiversity. As climate and environments continue to change, choosing appropriate restoration targets is becoming more challenging. By considering the paleoecological and paleoenvironmental record for a given region, restoration practitioners are not only able to bear witness to that region’s dynamic history, but also potentially identify multiple, alternative natural ecosystem states. Indeed, one of the deliverables of conservation paleobiology, a field that applies paleontological data and methods to present-day conservation, is to inform restoration targets. Consideration of future change is equally important, and paleoecological and paleoclimatological data are essential for informing models that can help us understand how climate change is affecting species and ecosystems at different temporal scales. The symposium “A dynamic perspective on ecosystem restoration: Establishing temporal connectivity at the intersection between paleoecology and restoration ecology” gathered representatives from macroecology, paleoecology, and restoration ecology to share their perspectives on temporal connectivity and how consideration of an ecosystem’s past, present, and future can positively impact restoration and conservation. Some speakers approached the topic theoretically, while others considered it from a more practical and applied standpoint. The goals of the symposium were to build a stronger relationship among the subdisciplines, stimulate new ideas, and identify data and/or products that would be useful to share across subdisciplines.

Bulletin Ecological Society of America↗

Cape Romain partnership for coastal protection

This final report summarizes activities, outcomes, and lessons learned from a 3-year project titled “Climate Change Adaptation for Coastal National Wildlife Refuges” with the Cape Romain National Wildlife Refuge (NWR) and local partners in the surrounding South Carolina Lowcountry. The Lowcountry is classified as the 10-county area encompassing the coastal plain of South Carolina (this report specifically focuses on Berkeley, Charleston, and Georgetown Counties). The goals of this work, sponsored by the U.S. Geological Survey’s Southeast Climate Adaptation Science Center (SECASC), were to foster active engagement with stakeholders; to develop a comprehensive definition of adaptation problems faced by agencies, organizations, and individuals near the Cape Romain NWR that accounts for global change, local values, knowledge and perceptions; and to encourage social learning and building of effective networks and trust across South Carolina Lowcountry organizations and individuals. Although project scoping began at the scale of the Atlantic seaboard, by engaging with NWRs from Massachusetts to Florida, participating refuge personnel eventually selected the Cape Romain NWR to serve as a case study for testing our goals. The Cape Romain Partnership for Coastal Conservation was established to address global change impacts at a regional level and includes representation from Federal and State resource agencies, local conservation nongovernmental organizations, and organizations representing underserved community interests. Research topics, originating from discussions with Cape Romain Partnership for Coastal Conservation members, focused on quantifying key drivers of change including localized sea-level rise (SLR) predictions, estimates of coastal hurricane inundation as amplified by SLR, and urban growth trends and forecasts. These key drivers provided a foundation to engage stakeholders in planning exercises to begin a process of collective understanding and collaborative decision making. The goal of this process was to develop collective strategies of adaptation to enhance community and ecosystem resilience in the South Carolina Lowcountry. South Carolina’s Lowcountry is experiencing rapid environmental and social transformation because of SLR rates approaching twice the global average, chronic tidal flooding and catastrophic storm surges, erosion and loss of habitats that provide essential services to wildlife and humans, and increasing social polarization fueled by aggressive low-density urban growth and other forms of land conversion. To support characterizations of plausible future scenarios, we used available or, in some cases, developed new models to project future conditions of key environmental and social-economic drivers. Because of the imprecision of mean global SLR projections, the SECASC commissioned a climatological study to account for local conditions and multiple representative concentration pathways to project a tailored distribution of future sea levels. These projections were matched to SLR scenarios provided by existing models to anticipate the range of future coastal habitat changes in the South Carolina Lowcountry. SLR scenarios were also incorporated into existing storm-surge models, which do not account for alternate baseline sea levels, to project the local effects of future hurricanes. To evaluate the extent and effects of population growth and urban expansion, we relied on an existing urban-growth model to map the spatial distribution of land-conversion probabilities, the total area of which is predicted to increase twofold to threefold over the next 60 years. In addition to this simplified model, an econometric model is in development to account for nonlinear feedback dynamics in land value, land use, and ecosystem service production. Although not yet completed, the goals of this model are to produce more-detailed projections of growth dynamics and to allow predictions of development patterns resulting from alternate land-use planning policies and incentives. Collaborative planning for an uncertain future requires more than providing decision makers with information on future physical and ecological conditions; developing effective and consensual strategies must also integrate sociological values, multiple cultural perspectives, and an understanding of human behavior. To support broad stakeholder engagement in integrative approaches to adaptation planning, emphasis was placed on the importance of considering differences in how individuals perceive their environment and create meaning. Because cultural frameworks form the basis for perceptions and, ultimately, the behaviors of individuals and institutions, we describe a model of human behavior and how it can be used to understand the effect of cultural complexity and variation in perception on choices, behavioral change, and long-term maintenance of behaviors. We consider a model commonly used in the field of behavioral health that accommodates variation in human perception when describing stages of behavior and the dynamics of behavioral change. Tailoring communication and engagement activities to targeted stakeholders is likely to benefit from increased understanding of behavioral change processes. The complex nature of this problem limited the usefulness of a traditional decision-analytic approach, we explored alternative methods for engagement, collaborative learning and decision making. Recognizing that project partners and Lowcountry stakeholders may be at different stages of preparedness and interest level for modifying behavior as a function of global change, we facilitated a scenario-planning exercise to familiarize partners with this well-established approach for communicating the opportunities and threats arising under alternative, plausible futures. We developed narratives for four alternative South Carolina Lowcountry scenarios to be used in later strategic planning that focus on quantitative trends for three primary drivers with high impact and high uncertainty: manifestations of climate change, social-political shifts at a global level, and forces of local value and power structures. This scenario-planning exercise underscored the complex relation between the temporospatial scale of the production of ecological goods and services and the institutional scale at which they are managed. We then guided the partners through an assessment of the relevant strengths and weaknesses of the Cape Romain Partnership for Coastal Protection, using the threats and opportunities characterized by each scenario to understand how the partnership might respond when attempting to meet conservation and societal objectives. The partnership identified key strengths including partnership experience, outreach and technical capacities, a substantial conservation land base, and high social cohesion in the South Carolina Lowcountry. Limited communication expertise, institutional inertia, and insufficient staffing and funding were recognized as important weaknesses across the partnership. By examining and scoring combinations of internal strengths and weaknesses and external threats and opportunities, the partnership developed sets of prioritized strategies to consider in the context of a given scenario. Although we had insufficient time to examine all scenarios in detail, the intent was to identify a portfolio of strategic actions to address threats and opportunities represented in multiple plausible futures. Top-ranking strategies encompassed a range of actions that focused on strengthening the conservation community and communicating the benefits of nature (that is, ecosystem services) to leveraging partnerships to expand land protection. This report also details the methods and preliminary results of several models developed or applied in support of this project. Two parcel-selection algorithms were used to evaluate anticipated habitat changes and patterns of urban growth to guide decisions on optimal conservation reserve design to protect habitat communities. One approach used a widely available planning software (MARXAN) to maximize conservation benefits near the Cape Romain NWR, whereas the other approach was a novel application of economic theory to account for uncertainty in future conditions and for the risks of unanticipated habitat loss. This latter model applies modern portfolio theory to estimate the risk of investing in any portfolio of land parcels (that is, candidate “reserves”) under climate-change uncertainty by quantifying the variation and spatial correlation of conservation benefits derived from each portfolio. We expanded the range of actions beyond simply whether or not to invest in a set of land parcels, an approach commonly used in spatial conservation planning, to also include consideration of divestment from currently protected lands. Such refinements allow for better accounting of system dynamics and can evaluate the benefits of flexible conservation tools such as rolling easements. Model results were conditional on a decision maker’s risk tolerance but highlighted general strategies of land conservation to increase future habitat representation beyond what is expected under the current protected land base. We built models that may help inform coastal planning by estimating salinity dynamics and the performance of oyster reef restoration efforts to predict the combined effects of global change and management of freshwater flows on coastal habitats and the processes that contribute to their resilience. These models can support restoration decisions by evaluating the expected benefits of site locations for shoreline protection and fisheries production. Lastly, we developed a spatially explicit economic model that predicts feedback dynamics among land value, land-use change, and effects on ecosystem service provision to explore zoning policies and incentives on urban growth and ecosystem services. We summarize these efforts with insights and considerations for the Cape Romain Partnership for Coastal Protection to continue to engage stakeholders in effective adaptation planning. First, notions of place attachment (referred to as sense of place), and the role of culture in social discourse are increasingly being used to understand the complex interactions between society and the environment and how societies respond and adapt to climate change. Sense of place was a unifying theme whenever the future of the South Carolina Lowcountry was discussed. The contribution of the South Carolina Lowcountry’s environmental wealth, rich cultural heritage, and quality of life to sense of place has important implications for how adaptation planning might best be pursued. More community-based governance of the commons (in other words, natural and cultural resources held in common), in which broad stakeholder participation and power sharing are key elements, is considered important. This devolution of governance is characterized by polycentric institutions and self-organizing social networks that promote a local culture of knowledge sharing, problem solving, and learning. These so-called bridging organizations (or individuals) often provide the leadership necessary to bring together potentially disparate Government agencies and institutions, private organizations, and individuals in a collective process of problem solving. Our observations also suggest that the conservation community in the South Carolina Lowcountry views its activities as integral to the broader governance of social-ecological systems, in which responses to the forces of global change are mediated through culture, economics, and politics. Rather than directly competing with other interests, the South Carolina Lowcountry conservation community seems to embrace an interpretation of conservation in which the fundamental objective is the quality of human life rather than environmental protection. Fundamental to the types of governance reforms described above is the notion of coproduction, in which experts and users collaborate to develop a shared body of knowledge. In this approach, scientists work with stakeholders to help frame questions, design research, and collect and analyze data. Such sustained collaborations are increasingly believed to be an effective way to produce useable (or actionable) science. The emphasis on social learning, leveraging strong social networks, coordinating and deliberating among diverse stakeholders, and applying principles of adaptive management is an essential contribution to adaptive capacity. The diverse and robust set of scientific approaches, methods to help stakeholders collaborate in effective and goal-driven planning processes, and decision tools resulting from this project hopefully will assist Cape Romain NWR and its partners prepare for climatic, ecological, and social changes over the coming decades.

South Carolina↗

Ecological and social drivers of Mexican wolf home range size across spatiotemporal scales

Elucidating factors influencing home range size is fundamental to the ecology and management of wildlife species, particularly those of conservation concern, because they can provide insight into how species utilize and interact with their environment. Variation in home range size can be related to intraspecific competition and social organization, energetic requirements in relation to habitat productivity, allometric relationships, and population density. The Mexican Gray Wolf ( Canis lupus baileyi ) is an endangered subspecies of the Gray Wolf whose home range size has not yet been studied. We examined ecological and social drivers of home range size of 22 Mexican Wolf packs between 2017 and 2021 across 4 biological time frames: annual; denning; post-denning; and non-denning. We used a 95% Brownian Bridge Movement Model home range estimator and generalized linear mixed-effect models to assess these relationships. Home range size was inversely correlated with estimated ungulate biomass, which was the most influential driver of home range size at annual and post-denning time frames. Larger packs utilized larger ranges during denning and post-denning time frames, while packs with larger litters had smaller ranges during the denning season. Snow depth was inversely related to home range size during the non-denning season. Our results indicate that both ecological and social factors are important and seasonally dependent in driving Mexican Wolf home range size. Use of a multiscale approach in future home range studies could discern relevant factors for species during time frames of interest.

Journal of Mammology↗

Integrating anthropogenic factors into regional-scale species distribution models — A novel application in the imperiled sagebrush biome

Species distribution models (SDM) that rely on regional-scale environmental variables will play a key role in forecasting species occurrence in the face of climate change. However, in the Anthropocene, a number of local-scale anthropogenic variables, including wildfire history, land-use change, invasive species, and ecological restoration practices can override regional-scale variables to drive patterns of species distribution. Incorporating these human-induced factors into SDMs remains a major research challenge, in part because spatial variability in these factors occurs at fine scales, rendering prediction over regional extents problematic. Here, we used big sagebrush (Artemisia tridentata Nutt.) as a model species to explore whether including human-induced factors improves the fit of the SDM. We applied a Bayesian hurdle spatial approach using 21,753 data points of field-sampled vegetation obtained from the LANDFIRE program to model sagebrush occurrence and cover by incorporating fire history metrics and restoration treatments from 1980 to 2015 throughout the Great Basin of North America.

Global Change Biology↗

Phylogeny and species traits predict bird detectability

Avian acoustic communication has resulted from evolutionary pressures and ecological constraints. We therefore expect that auditory detectability in birds might be predictable by species traits and phylogenetic relatedness. We evaluated the relationship between phylogeny, species traits, and field‐based estimates of the two processes that determine species detectability (singing rate and detection distance) for 141 bird species breeding in boreal North America. We used phylogenetic mixed models and cross‐validation to compare the relative merits of using trait data only, phylogeny only, or the combination of both to predict detectability. We found a strong phylogenetic signal in both singing rates and detection distances; however the strength of phylogenetic effects was less than expected under Brownian motion evolution. The evolution of behavioural traits that determine singing rates was found to be more labile, leaving more room for species to evolve independently, whereas detection distance was mostly determined by anatomy (i.e. body size) and thus the laws of physics. Our findings can help in disentangling how complex ecological and evolutionary mechanisms have shaped different aspects of detectability in boreal birds. Such information can greatly inform single‐ and multi‐species models but more work is required to better understand how to best correct possible biases in phylogenetic diversity and other community metrics.

Ecography↗

Biofilms provide new insight into pesticide occurrence in streams and links to aquatic ecological communities

Streambed sediment is commonly analyzed to assess occurrence of hydrophobic pesticides and risks to aquatic communities. However, stream biofilms also have the potential to accumulate pesticides and may be consumed by aquatic organisms. To better characterize risks to aquatic life, the U.S. Geological Survey Regional Stream Quality Assessment measured 93 current-use and 3 legacy pesticides in bed sediment and biofilm from 54 small streams in California across a range of land-use settings. On average, 4 times as many current-use pesticides were detected in biofilm at a site (median of 2) as in sediment (median of 0.5). Of 31 current-use pesticides detected, 20 were detected more frequently in biofilm than sediment and 10 equally frequently. Pyrethroids as a class were the most potentially toxic to benthic invertebrates, and of the nine pyrethroids detected, seven occurred more frequently in biofilm than sediment. We constructive General Additive Models to investigate relations between pesticides and six metrics of benthic community structure. Pesticides in biofilm improved fit in four of the six models, and pesticides in sediment improved fit in two. The results indicate that sampling of stream biofilms can complement bed-sediment sampling by identifying more current-use pesticides present and better estimating ecological risks.

California↗

Bayesian shared frailty models for regional inference about wildlife survival

One can joke that 'exciting statistics' is an oxymoron, but it is neither a joke nor an exaggeration to say that these are exciting times to be involved in statistical ecology. As Halstead et al. 's (2012) paper nicely exemplifies, recently developed Bayesian analyses can now be used to extract insights from data using techniques that would have been unavailable to the ecological researcher just a decade ago. Some object to this, implying that the subjective priors of the Bayesian approach is the pathway to perdition (e.g. Lele & Dennis, 2009). It is reasonable to ask whether these new approaches are really giving us anything that we could not obtain with traditional tried-and-true frequentist approaches. I believe the answer is a clear yes.

Animal Conservation↗

Space use and site fidelity of wintering whooping cranes on the Texas Gulf Coast

The Aransas-Wood Buffalo population (the only non-reintroduced, migratory population) of endangered whooping cranes ( Grus americana ) overwinters along the Texas Gulf Coast, USA. Understanding whooping crane space use on the wintering grounds reveals essential aspects of this species' ecology, which subsequently assists with conservation. Using global positioning system telemetry data from marked whooping cranes during 2009–2017, we fit continuous-time stochastic process models to describe movement and home range using autocorrelated kernel density estimation (AKDE) and explored variation in home range size in relation to age, sex, reproductive status, and drought conditions. We used the Bhattacharyya coefficient of overlap and distance between home range centroids to quantify site fidelity. We examined the effects of time between winter home ranges and the sex of the crane on site fidelity using Bayesian mixed-effects beta regression. Winter whooping crane 95% AKDE home range size averaged 30.1 ± 45.2 (SD) km 2 (median = 14.3, range = 1.1–308.6). Home ranges of sub-adult females were approximately 2 times larger than those of sub-adult males or families. As drought worsened, home ranges typically expanded. Between consecutive years, the home ranges of an adult crane exhibited 68 ± 31% overlap (site fidelity), but fidelity to winter sites declined in subsequent winters. The overlap of adult home ranges with the nearest unrelated family averaged 33 ± 28%. As a whooping crane aged, overlap with its winter home range as a juvenile declined, regardless of sex. By 4 years of age, a whooping crane had approximately 14 ± 28% overlap with its juvenile winter home range. Limited evidence suggested male whooping cranes return to within 2 km of their juvenile home range by their fifth winter. Previous data obtained from aerial surveys led ecologists to assume that whooping crane families normally used small areas (~2 km 2 ) and expressed persistent site fidelity. Our analyses showed <8% of families had home ranges ≤2 km 2 , with the average area 15 times greater, and waning site fidelity over time. Our work represents an analysis of whooping crane home ranges for this population, identifying past misconceptions of winter space use and resulting in better estimates of space requirements for future conservation efforts.

Texas↗

Pardus in the press: Drivers of leopard (Panthera pardus fusca) attack occurrence on humans in Nepal

The negative impact of large carnivore presence in human-dominated landscapes manifests as livestock depredation and in extreme cases as attacks on humans. In the case of conflict with leopards in Nepal, attacks resulting in human fatality have become more frequent over time, thus creating an urgent socio-ecological and management issue. We estimated the occurrence of leopard attacks in Nepal from human-leopard conflict cases reported in the media. We used occupancy models to analyse data collected from online news reports on incidents of leopard attacks on humans to explore drivers of leopard attacks on a landscape scale. Our results suggest that the probability of occurrence of leopard attack is associated with human population density, terrain ruggedness and livestock density. The human population density effect may be indicative of a density-dependent relationship, where attacks are more likely in areas where an increased abundance of humans increases encounter rates with leopards. The positive effect of livestock density suggests that livestock may be drawing leopards into human settlements, and consequently increasing the likelihood of attacks on humans. Terrain ruggedness might be offering ideal conditions to facilitate attacks on humans, for example remoteness and high amounts of cover to launch ambush attacks. We provide inference and insights into key determinants of leopard attacks on humans on a landscape scale. These insights can be used to guide future research, inform mitigation measures to reduce leopard attacks and foster a better understanding of the interaction between people and leopards. This study demonstrates the applicability and novelty of using a hierarchical modelling framework applied to freely and publicly available media reports to inform the applied management of human-wildlife conflict at a national scale.

People and Nature↗

Odds ratios and hurdle models: a long-term analysis of parasite infection patterns in endangered young-of-the-year suckers from Upper Klamath Lake, Oregon, USA

We used odds ratios and a hurdle model to analyze parasite co-infections over 25 years on >20,000 young-of-the year of endangered Shortnose and Lost River Suckers. Host ecologies differed as did parasite infections. Shortnose Suckers were more likely to be caught inshore and 3–5 times more likely to have Bolbophorus spp. and Contracaecum sp. infections, and Lost River Suckers were more likely to be caught offshore and approximately three times more likely to have Lernaea cyprinacea infections. An observed peak shift seems likely to be due to a lower host size limit for Bolbophorus spp. (13.6 mm) compared with L. cyprinacea (23.4 mm). The large data set allowed us to generate strong hypotheses: (i) that a major marsh restoration project had unintended consequences that resulted in an increase in infections; (ii) that co-infection with Bolbophorus spp. increased the odds of infection by L. cyprinacea and Contracaecum sp.; (iii) that significant declines in the odds of infection over approximately 25 days were due to parasite-induced host mortality; (iv) that the fish’s small size relative to L. cyprinacea and Contracaecum sp. might be directly lethal; (v) that the absence of L. cyprinacea infections in the early 1990s was associated with good year-class production of the suckers; and (vi) that parasites might increase the odds of vagrancy from the nursery ground.

Oregon↗

Idiosyncratic spatial scaling of biodiversity–disease relationships

High host biodiversity is hypothesized to dilute the risk of vector-borne diseases if many host species are ‘dead ends' that cannot effectively transmit the disease and low-diversity areas tend to be dominated by competent host species. However, many studies on biodiversity–disease relationships characterize host biodiversity at single, local spatial scales, which complicates efforts to forecast disease risk if associations between host biodiversity and disease change with spatial scale. Here, our objective is to evaluate the spatial scaling of relationships between host biodiversity and Borrelia (the bacterial taxon which causes Lyme disease) infection prevalence in small mammals. We compared the associations between infection prevalence and small mammal host diversity for local communities (individual plots) and metacommunities (multiple plots aggregated within a landscape) sampled by the National Ecological Observatory Network (NEON), an emerging continental-scale environmental monitoring program with a hierarchical sampling design. We applied a multispecies, spatially-stratified capture–recapture model to a trapping dataset to estimate five small mammal biodiversity metrics, which we used to predict infection status for a subset of trapped individuals. We found that relationships between Borrelia infection prevalence and biodiversity did indeed vary when biodiversity was quantified at different spatial scales but that these scaling behaviors were idiosyncratic among the five biodiversity metrics. For example, species richness of local communities showed a negative (dilution) effect on infection prevalence, while species richness of the small mammal metacommunity showed a positive (amplification) effect on infection prevalence. Our modeling approach can inform future analyses as data from similar monitoring programs accumulate and become increasingly available through time. Our results indicate that a focus on single spatial scales when assessing the influence of biodiversity on disease risk provides an incomplete picture of the complexity of disease dynamics in ecosystems.

Ecography↗

Observed and projected functional reorganization of riverine fish assemblages from global change

Climate and land-use/land-cover change (‘global change’) are restructuring biodiversity, globally. Broadly, environmental conditions are expected to become warmer, potentially drier (particularly in arid regions), and more anthropogenically developed in the future, with spatiotemporally complex effects on ecological communities. We used functional traits to inform Chesapeake Bay Watershed fish responses to future climate and land-use scenarios (2030, 2060, and 2090). We modelled the future habitat suitability of focal species representative of key trait axes (substrate, flow, temperature, reproduction, and trophic) and used functional and phylogenetic metrics to assess variable assemblage responses across physiographic regions and habitat sizes (headwaters through large rivers). Our focal species analysis projected future habitat suitability gains for carnivorous species with preferences for warm water, pool habitats, and fine or vegetated substrates. At the assemblage level, models projected decreasing habitat suitability for cold-water, rheophilic, and lithophilic individuals but increasing suitability for carnivores in the future across all regions. Projected responses of functional and phylogenetic diversity and redundancy differed among regions. Lowland regions were projected to become less functionally and phylogenetically diverse and more redundant while upland regions (and smaller habitat sizes) were projected to become more diverse and less redundant. Next, we assessed how this model projected assemblage changes 2005-2030 related to observed time-series trends (1999–2016). Halfway through the initial projecting period (2005–2030), we found observed trends broadly followed modelled patterns of increasing proportions of carnivorous and lithophilic individuals in lowland regions but showed opposing patterns for functional and phylogenetic metrics. Leveraging observed and predicted analyses simultaneously helps elucidate the instances and causes of discrepancies between model predictions and ongoing observed changes. Collectively, results highlight the complexity of global change impacts across broad landscapes that likely relate to differences in assemblages’ intrinsic sensitivities and external exposure to stressors.

Global Change Biology↗