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At least 613 records · Page 34Linked to original sources
USGS2022

Spatial dynamic N-mixture models with interspecific interactions

Interspecific interactions and movement are key factors that drive the coexistence of metapopulations in heterogenous landscapes. Yet, it is challenging to understand these factors because separating movement from local population processes relied on capture-based data that are difficult to collect. Recent development of spatial dynamic N-mixture models (SDNMs) made it possible to draw inference on local population growth and movement using count data of unmarked populations. However, no SDNMs have been developed to account for interspecific interactions and double counting observation errors. In this study we further developed SDNMs to account for interspecific interactions and both false negative and double counting observation errors. We conducted simulation studies to evaluate the inferential performance of these models under different ecological systems (competition, predator-prey), observation processes (binomial, Poisson), and sampling situations including the number of surveyed sites, detection, and the adoption of robust sampling design. We then illustrated the applications of these models with two case studies, one representing a competition system (mallard Anas platyrhynchos , northern pintail Anas acuta ) with binomial observations and the other representing a predator-prey system (bobcat Lynx rufus , wild turkey Meleagris gallopavo ) with Poisson observations. The results of the simulation studies showed that the models provide unbiased parameter estimates regardless of the ecological system, observation process, and sampling situation. Case studies further demonstrated the capabilities of these models in revealing important ecological processes. More specifically, the first case study revealed a negative effect of the superior competitor, Mallard, on the population growth of the inferior competitor, pintail as well as different movement patterns between these species, while the second case study reveal a top-down effect of bobcat abundance on wild turkey population growth and their differential habitat preferences. The models developed in this study could be used by themselves on count data or serve as population sub-models in integrated modeling frameworks to provide insights about metapopulation dynamics of interacting species in heterogeneous landscapes. The flexible structures of these models made them highly adaptive and relevant in population and community ecology.

Methods in Ecology and Evolution↗
USGS2022

Defining fine-scaled population structure among continuously distributed populations

Understanding wildlife population structure and connectivity can help managers identify conservation strategies, as structure can facilitate the study of population changes and habitat connectivity can provide information on dispersal and biodiversity. To facilitate the use of wildlife monitoring data for improved adaptive management, we developed a novel approach to define hierarchical tiers (multiple scales) of population structure. We defined population structure by combining graph theory with biological inference about dispersal capability (based on movement, gene flow, and habitat condition) and functional processes affecting movement (e.g. habitat selection across scales of landscape preferences). First, we developed least-cost paths between high fidelity sites (habitat patches) using a cost surface, informed from functional processes of habitat characteristics to account for resistance of inter-patch movements. Second, we combined the paths into a multi-path graph construct. Third, we used information on potential connectivity (dispersal distances) and functional connectivity (permeability of fragmented landscapes based on selection preferences) to decompose the graph into hierarchical tiers of connected subpopulations, denoting the degree that dispersal affected population structure. As a case study, we applied our approach across the greater sage-grouse ( Centrocercus urophasianus ) range, a species of conservation concern in western United States. We described the relative importance of local populations and where to potentially avoid landscape disturbances that may negatively affect population connectivity using centrality measures supported by graph theory, and we demonstrated close alignment of the resulting population structure with population densities. This method can be adapted for other species with site fidelity and used as a management tool to evaluate population trends and responses to landscape changes across different temporal and spatial scales.

Methods in Ecology and Evolution↗
USGS2022

The power of forecasts to advance ecological theory

Ecological forecasting provides a powerful set of methods for predicting short- and long-term change in living systems. Forecasts are now widely produced, enabling proactive management for many applied ecological problems. However, despite numerous calls for an increased emphasis on prediction in ecology, the potential for forecasting to accelerate ecological theory development remains underrealized. Here, we provide a conceptual framework describing how ecological forecasts can energize and advance ecological theory. We emphasize the many opportunities for future progress in this area through increased forecast development, comparison and synthesis. Our framework describes how a forecasting approach can shed new light on existing ecological theories while also allowing researchers to address novel questions. Through rigorous and repeated testing of hypotheses, forecasting can help to refine theories and understand their generality across systems. Meanwhile, synthesizing across forecasts allows for the development of novel theory about the relative predictability of ecological variables across forecast horizons and scales. We envision a future where forecasting is integrated as part of the toolset used in fundamental ecology. By outlining the relevance of forecasting methods to ecological theory, we aim to decrease barriers to entry and broaden the community of researchers using forecasting for fundamental ecological insight.

Methods in Ecology and Evolution↗
USGS2022

A statistical framework for modelling migration corridors

1. Management of animal populations requires spatially explicit knowledge of movement corridors, such as those used during seasonal migrations. GPS tracking data allows for mapping of corridors from directly observed movements, providing important insights, but tracking data is absent for many populations. 2. We developed a novel statistical corridor modeling approach that predicts movement corridors from cost-distance movement models fit directly to migration tracking data. Unlike existing predictive approaches, this does not require the ad-hoc transformation of habitat suitability surfaces into resistance surfaces. We tested the ability of the approach to recover parameters used to generate simulated movements. We then used GPS data from three migrating mule deer (Odocoileus hemionus) herds in Idaho and Wyoming to model corridors as a function of elevation, slope, aspect, percent shrub, date of peak green-up, snow-off date, and human footprint. We assessed the predictive ability of the fitted models using validation tracks from the same herd as well as from the other herds. 3. The approach reproduced parameters used to generate the simulated movements, predicted the corridors used by migratory populations, and described the direction, magnitude, and confidence levels of the effects of environmental variables on corridors. The effects environmental variables had on corridors differed depending on the herd. Within-herd validation indicated that fitted corridor models are more accurate at predicting migration corridors than null models, and cross-herd validation indicated that fitted models for some herds accurately predicted the observed migrations of other herds. 4. In addition to the practical benefit of mapping corridors for management, our statistical corridor modeling framework sets the stage for evaluating fundamental questions about the fitness tradeoffs, navigation, learning, fidelity, and movement constraints that influence migratory and other corridor-generating behavior. Models of predictive corridors can inform management and planning for the conservation of migrations across taxa, including the potential restoration of corridors. Our corridor modeling approach is also readily applied to non-migratory animal movements.

Idaho, Wyoming↗
USGS2022

Migration Mapper: Identifying movement corridors and seasonal ranges for large mammal conservation

Modern tracking technology has facilitated a novel understanding of terrestrial mammal movement while revealing that movements are being truncated and lost. The first step towards conserving mobile animals is identifying movement corridors and key seasonal ranges. Yet, the identification and subsequent mapping of these important areas has remained a challenge due to the analytical skills necessary to conduct such analyses. Migration Mapper (MM) is a user-friendly software that provides tools to analyse global positioning system (GPS) collar data to create season-specific, population-level polygons representing areas where most of a population moves (i.e. movement corridors) and areas where most of a population spends time (e.g. high-use areas, seasonal ranges). MM consists of six standalone modules including data cleaning and review, seasonal movement delineation, movement model application, calculation of population-level outputs and visualization of results. Analysis of GPS data using MM can provide the spatial polygons necessary to facilitate conservation and policy planning. New initiatives at the local and global levels are already beginning to use MM to facilitate conservation of large, terrestrial mammals.

Methods in Ecology and Evolution↗
USGS2022

Know what you don't know: Embracing state uncertainty in disease-structured multistate models

Hidden Markov models (HMMs) are broadly applicable hierarchical models that derive their utility from separating state processes from observation processes yielding the data. Multistate models such as mark–recapture and dynamic multistate occupancy models are HMMs frequently used in ecology. In their early formulations, states, such as pathogen infection status, were assumed to be perfectly observed without ambiguity. However, state uncertainty is a pervasive feature of many ecological studies, and multievent models were developed to explicitly account for it. We developed a novel extended multievent mark–recapture model that incorporates state uncertainty at multiple levels of detection. Using a disease-structured example, both false negative and false positive state assignment errors are modelled at two levels of state assignment—the pathogen sampling process and the diagnostic process that samples are subjected to. We additionally describe methods to jointly model infection intensity to integrate heterogeneity in ecological parameters, such as mortality and infection dynamics, and the pathogen detection processes. We provide code to simulate and analyse datasets with various underlying ecological processes and fit our model to a mark–recapture dataset of Mixophyes fleayi (Fleay's barred frog) infected with the amphibian chytrid fungus ( Batrachochytrium dendrobatidis , Bd ). In our case study, we found evidence for various state assignment errors: the sampling protocol performed poorly in detecting Bd , pathogen detection was highly dependent on infection intensity and false positives were non-negligible. Incorporating state uncertainty yielded significantly higher estimates of infection prevalence and 4–5 times lower rates of infection state transitions compared to those obtained from a traditional multistate model. Our results highlight that incorporating state assignment errors improves inference on the ecological process, especially when sensitivity and specificity of the state assignment processes are low. The general model structure can be applied to other HMMs, providing a foundation for modelling state uncertainty in related models. For disease-structured multistate models, we recommend conducting robust design surveys and collecting samples during each capture event to facilitate incorporating pathogen detection errors.

Methods in Ecology and Evolution↗
USGS2022

A review of supervised learning methods for classifying animal behavioural states from environmental features

Accurately predicting behavioural modes of animals in response to environmental features is important for ecology and conservation. Supervised learning (SL) methods are increasingly common in animal movement ecology for classifying behavioural modes. However, few examples exist of applying SL to classify polytomous animal behaviour from environmental features especially in the context of millions of animal observations. We review SL methods (weighted k -nearest neighbours; neural nets; random forests; and boosted classification trees with XGBoost) for classifying polytomous animal behaviour from environmental predictors. We also describe tuning parameter selection and assessment strategies, approaches for visualizing relationships between predictors and class outputs, and computational considerations. We demonstrate these methods by predicting three categories of risk to bald eagles from colliding with wind turbines using, as predictors, 12 environmental state features associated with 1.7 million GPS telemetry data points from 57 eagles. Of the SL methods we considered, XGBoost yielded the most accurate model with 86.2% classification accuracy and pairwise-averaged area under the ROC curve of 90.6. Computational time of XGBoost scaled better to large data than any other SL method. We also show how SHAP values integrated in the R package ( xgboost ) facilitate investigation of variable relationships and importance. For big data applications, XGBoost appears to provide superior classification accuracy and computational efficiency. Our results suggest XGBoost should be considered as an early modelling option in situations where the intent is to classify millions of animal behaviour observations from environmental predictors and to understand relationships between those predictors and movement behaviours. We also offer a tutorial to assist researchers in implementing this method.

Methods in Ecology and Evolution↗
USGS2022

Estimating reproductive and juvenile survival rates when offspring ages are uncertain: A novel multievent mark-resight model with beluga whale case study

Understanding the survival and reproductive rates of a population is critical to determining its long-term dynamics and viability. Mark-resight models are often used to estimate these demographic rates, but estimation of survival and reproductive rates is challenging, especially for wide-ranging, patchily distributed, or cryptic species. In particular, existing mark-resight models cannot accommodate data from populations in which offspring remain with parents for multiple years, are not always detected, and cannot be aged with certainty. Here we describe a Bayesian multievent mark-resight modelling framework that uses all available adult and adult-offspring sightings (including sightings with older offspring of uncertain age) to estimate reproductive rates and survival rates of adults and juveniles. We extend existing multievent mark-resight models that typically only incorporate adult breeding state uncertainty by additionally accounting for age uncertainty in unmarked offspring and uncertainty in the duration of the mother-offspring association. We describe our model in general terms and with a simple illustrative example, then apply it in a more complex empirical setting using 13 years of photo-ID data from a critically endangered population of beluga whales Delphinapterus leucas . We evaluated model performance using simulated data under a range of sample sizes, and adult and offspring detection rates. Applying our model to the beluga data yielded precise estimates for all demographic rates of interest (despite substantial uncertainty in calf ages), including nonbreeder survival and reproductive rates lower than in other beluga populations. Simulations suggested our model yields asymptotically unbiased parameter estimates with good precision and low bias even with moderate sample sizes and detection rates. This work represents an important new development in multievent mark-resight modelling, allowing estimation of reproductive and juvenile survival rates for populations with extended adult—offspring associations and uncertain offspring ages (e.g. some marine mammals, elephants, bears, great apes, bats and birds). Our model facilitated estimation of robust demographic rates for an endangered beluga population that were previously inestimable (e.g. nonbreeder and juvenile survival, reproductive rate) and that will yield new insights into this population's continued decline.

Methods in Ecology and Evolution↗
USGS2023

Assimilating ecological theory with empiricism: Using constrained generalized additive models to enhance survival analyses

1. Integrating ecological theory with empirical methods is ubiquitous in ecology using hierarchical Bayesian models. However, there has been little development focused on integration of ecological theory into models for survival analysis. Survival is a fundamental process, linking individual fitness with population dynamics, but incorporating life history strategies to inform survival estimation can be challenging because mortality processes occur at multiple scales. 2. We develop an approach to survival analysis, incorporating model constraints based on a species' life history strategy using functional analytical tools. Specifically, we structurally separate intrinsic patterns of mortality that arise from age-specific processes (e.g. increasing survival during early life stages due to growth or maturation, versus senescence) from extrinsic mortality patterns that arise over different periods of time (e.g. seasonal temporal shifts). We use shape constrained generalized additive models (CGAMs) to obtain age-specific hazard functions that incorporate theoretical information based on classical survivorship curves into the age component of the model and capture extrinsic factors in the time component. 3. We compare the performance of our modelling approach to standard survival modelling tools that do not explicitly incorporate species life history strategy in the model structure, using metrics of predictive power, accuracy, efficiency and computation time. We applied these models to two case studies that reflect different functional shapes for the underlying survivorship curves, examining age-period survival for white-tailed deer Odocoileus virginianus in Wisconsin, USA and Columbian sharp-tailed grouse T ympanuchus phasianellus columbianus in Colorado, USA.

Colorado, Wisconsin↗
USGS2023

Inferring pathogen presence when sample misclassification and partial observation occur

Surveillance programmes are essential for detecting emerging pathogens and often rely on molecular methods to make inference about the presence of a target disease agent. However, molecular methods rarely detect target DNA perfectly. For example, molecular pathogen detection methods can result in misclassification (i.e. false positives and false negatives) or partial detection errors (i.e. detections with ‘ambiguous’, ‘uncertain’ or ‘equivocal’ results). Then, when data are to be analysed, these partial observations are either discarded or censored; this, however, disregards information that could be used to make inference about the true state of the system. There is a critical need for more direction and guidance related to how many samples are enough to declare a unit of interest ‘pathogen free’. Here, we develop a Bayesian hierarchal framework that accommodates false negative, false positive and uncertain detections to improve inference related to the occupancy of a pathogen. We apply our modelling framework to a case study of the fungal pathogen Pseudogymnoascus destructans (Pd) identified in Texas bats at the invasion front of white-nose syndrome. To improve future surveillance programmes, we provide guidance on sample sizes required to be 95% certain a target organism is absent from a site. We found that the presence of uncertain detections increased the variability of resulting posterior probability distributions of pathogen occurrence, and that our estimates of required sample size were very sensitive to prior information about pathogen occupancy, pathogen prevalence and diagnostic test specificity. In the Pd case study, we found that the posterior probability of occupancy was very low in 2018, but occupancy probability approached 1 in 2020, reflecting increasing prior probabilities of occupancy and prevalence elicited from the site manager. Our modelling framework provides the user a posterior probability distribution of pathogen occurrence, which allows for subjective interpretation by the decision-maker. To help readers apply and use the methods we developed, we provide an interactive RShiny app that generates target species occupancy estimation and sample size estimates to make these methods more accessible to the scientific community ( https://rmummah.shinyapps.io/ambigDetect_sampleSize ). This modelling framework and sample size guide may be useful for improving inferences from molecular surveillance data about emerging pathogens, non-native invasive species and endangered species where misclassifications and ambiguous detections occur.

Methods in Ecology and Evolution↗
USGS2023

MetaIPM: Placing integral projection models into a metapopulation framework

Metapopulation models include spatial population dynamics such as dispersion and migration between subpopulations. Integral projection models (IPMs) can include demographic rates as a function of size. Traditionally, metapopulation models do not included detailed populaiton models such as IPMs. In some situations, both local population dynamics (e.g. size-based survival) and spatial dynamics are important. We present a Python package, MetaIPM , which places IPMs into a metapopulation framework, and allow users to readily construct and apply these models that combine local population dynamics within a metapopulation framework. MetaIPM includes an IPM for each subpopulation that is connected to other subpopulations via a metapopulation movement model. These movements can include dispersion, migration or other patterns. The IPM can include for size-specific demographic rates (e.g. survival, recruitment) as well as management actions, such as length-based harvest (e.g. gear specific capture sizes, varying slot limits across political boundaries). The model also allows for changes in metapopulation connectivity between locations, such as a fish passage ladders to enhance movement or deterrents to reduce movement. Thus, resource managers can use MetaIPM to compare different management actions such as the harvest gear type (which can be length-specific) and harvest locations. We demonstrate how MetaIPM may be applied to inform managers seeking to limit the spread of an invasive species in a system with important metapopulation dynamics. Specifically, we compared removal lengths (all length fish versus longer fish only) for an invasive fish population in a fragmented, inland river system. MetaIPM allowed users to compare the importance of harvesting source populations away from the invasion front, as well as species at the invasion front. The model would also allow for future comparisons of different deterrent placement locations in the system. Moving beyond our example system, we describe how MetaIPM can be applied to other species, systems and management approaches. The MetaIPM packages includes Jupyter Notebooks documenting the package as well as a second set of JupyterNotebooks showing the application of the package to our example system.

Methods in Ecology and Evolution↗
USGS2023

A hierarchical modelling framework for estimating individual- and population-level reproductive success from movement data

Rapidly advancing animal telemetry technologies paired with new statistical models can provide insight into the behaviour of otherwise unobservable free-living animals. Changes in behaviour apparent from pairing telemetry with statistical models often occur as animals undertake key life-history activities, such as reproduction. For many species that are secretive or occupy remote areas, these life-history events are difficult to detect with conventional survey techniques, and consequently, vital rates are difficult to estimate. We present a hierarchical modelling framework, which integrates movement data observed via animal-borne telemetry and optional, infrequent survey data, to estimate individual- and population-level reproductive success. The approach combines a mechanistic movement model and survival model, and allows for assessing the effects of hypothesized individual and environmental covariates on reproductive success. We first tested our approach with simulated data, and then applied it to movement data from migratory golden eagles ( Aquila chrysaetos) breeding in southcentral Alaska across four breeding seasons. We show that results supported our biological hypotheses that changes in movement coincided with the timing of reproductive failures, and that changes in movement could be used to assess breeding success (and failure) at the individual and population levels. The analysis also provided evidence of inter-annual variation in population-level nest success and the timing of nesting failures. This new approach is adaptable to many species that care for young and can be tracked with telemetry devices, and can provide not only individual-level information useful for testing ecological hypotheses, but estimates of demographic parameters that can directly inform conservation and management if tagged animals are representative of the population.

Methods in Ecology and Evolution↗
USGS2023

Positioning aquatic animals with acoustic transmitters

Geolocating aquatic animals with acoustic tags has been ongoing for decades, relying on the detection of acoustic signals at multiple receivers with known positions to calculate a 2D or 3D position, and ultimately recreate the path of an aquatic animal from detections at fixed stations. This method of underwater geolocation is evolving with new software and hardware options available to help investigators design studies and calculate positions using solvers based predominantly on time-difference-of-arrival and time-of-arrival. We provide an overview of the considerations necessary to implement positioning in aquatic acoustic telemetry studies, including how to design arrays of receivers, test performance, synchronize receiver clocks and calculate positions from the detection data. We additionally present some common positioning algorithms, including both the free open-source solvers and the ‘black-box’ methods provided by some manufacturers for calculating positions. This paper is the first to provide a comprehensive overview of methods and considerations for designing and implementing better positioning studies that will support users, and encourage further knowledge advances in aquatic systems.

Methods in Ecology and Evolution↗
USGS2024

occupancyTuts: Occupancy modelling tutorials with RPresence

1. The occupancy modelling framework offers tremendous flexibility in estimating species abundance and distribution patterns while accounting for imperfect detection, and has seen rapid growth and adoption since its introduction at the beginning of the century. 2. At the same time, in an era of big data, there are increasing demands on developing quantitative skills and proficiency in young ecologists, many of whom lack the quantitative training needed to conduct research professionally. 3. We introduce occupancyTuts , an R package that features 28 learnr tutorials that teach the statistical underpinnings of several occupancy models. The tutorials include written content, instructional videos, R exercises, and quiz elements, covering a range of topics including statistical underpinnings, single- and dynamic-occupancy models, study design and several of the ‘spin-off’ models that extend the basic framework. 4. We plan for development of new tutorials that use RPresence as the analysis engine, and welcome new tutorial contributions that use other R packages as the analysis engine as well.

Methods in Ecology and Evolution↗
USGS2024

Managing ecosystems with resist-accept-direct (RAD)

In recent years considerable interest has been generated in a new approach known as resist – accept – direct , or RAD, for managing ecosystems in the face of climate change. Under RAD, strategic responses to climate change are described in terms of three broad categories: resisting climate transformation, accepting the transformation and continuing to manage as best one can, and directing the transformed system toward novel ecological conditions. In particular, the potential for integrating RAD and adaptive management has been broadly considered, though absent a decision-making framework needed for implementation. We propose a hierarchical decision scheme for RAD that accounts for strategy selection among the three RAD options, as well as adaptive decision making within each option. We use stochastic models and uncertainties about ecosystem processes to account for the dynamics of climate-transformed ecosystems, and show how these features can be used to inform RAD strategies. Operationally, the approach involves decisions at two levels: one level involves choosing a policy for each strategy, and the second level involves deciding which strategy has the greatest policy value. The structure described here extends recent work in climate change adaptation, by including Markovian decisions under climate change, strategy-specific policies, and value functions for assessing and selecting RAD strategies. We provide a hierarchical accounting of decisions and responses, and develop rules for the timing of those decisions. Combining RAD and adaptive management can help to organize thinking about ecological conservation under climate change, and focus attention on mechanisms for making decisions. We believe the structure presented here can facilitate conservation efforts under the non-stationary climate conditions we are sure to face for the foreseeable future.

Methods in Ecology and Evolution↗
USGS2024

Geographic principles applied to population dynamics: A spatially interpolated integrated population model

A major impediment to wildlife conservation and management, from a quantitative perspective, is dealing with high degrees of uncertainty associated with population estimates. Integrated population models (IPMs) can help alleviate that challenge, but they are often limited to narrow spatial or temporal windows owing to the financial and logistical burdens of acquiring requisite datasets. To expand the spatiotemporal scope of practical IPM implementation, we developed a novel method that expresses demographic relatedness among sampled and unsampled locations using geographic principles of spatial autocorrelation. We interpolated demographic parameters at unsampled locations using parameter estimates from data-informed locations. Errors attributable to the interpolative process were corrected using a joint likelihood and locally recorded count data (‘cheaper’ and broadly distributed). We evaluated the spatially interpolated IPM (SIIPM) for precision and accuracy under variable levels of spatial autocorrelation using simulated data and a Leave-One-Out Cross-Validation (LOOCV) technique. Conventional IPMs and state-space models (SSM) were fit to the same simulated datasets to provide a comparative assessment of the novel method. In a final, empirical demonstration we fit the SIIPM to data collected from Greater Sage-Grouse ( Centrocercus urophasianus ; sage-grouse) populations located in Nevada, U.S.A. during 2013–2021. SIIPMs outperformed conventional IPMs when fit to data possessing moderate-to-high levels of spatial autocorrelation. Under moderate levels of autocorrelation, the average improvement in parameter estimation was 13.6% for survival, 65.3% for recruitment and 23.7% for rate of population change (𝜆). When spatial autocorrelation was low, the SIIPM still outperformed contemporary approaches in areas that were geographically close (<67 km) to sampling locations. Under low autocorrelation-near distance scenarios, we observed SIIPM parameters that were 30.8% (recruitment), 32.5% (𝜆; IPM comparison) and 54.0% (𝜆; SSM comparison) more precise than contemporary models. Spatial autocorrelation is often assumed but rarely tested when comparing population dynamics across regions of large geographic extent. We demonstrated that SIIPMs can improve precision of species' vital rate estimation when extrapolating model inference beyond populations for which long-term monitoring data exists. Specific to sage-grouse, these results support previous conclusions of broad-scale spatial autocorrelation in population dynamics and a reproductive-survival trade-off previously documented at smaller scales.

Methods in Ecology and Evolution↗
USGS2024

Clustering and unconstrained ordination with Dirichlet process mixture models

Assessment of similarity in species composition or abundance across sampled locations is a common goal in multi-species monitoring programs. Existing ordination techniques provide a framework for clustering sample locations based on species composition by projecting high-dimensional community data into a low-dimensional, latent ecological gradient representing species composition. However, these techniques require specification of the number of distinct ecological communities present in the latent space, which can be difficult to determine in advance. We develop an ordination model capable of simultaneous clustering and ordination that allows for estimation of the number of clusters present in the latent ecological gradient. This model draws latent coordinates for each sample location from a Dirichlet process mixture model, affording researchers with probabilistic statements about the number of clusters present in the latent ecological gradient. The model is compared to existing methods for simultaneous clustering and ordination via simulation and applied to two empirical datasets; JAGS code to fit the proposed model is provided in an appendix. The first dataset concerns presence-absence records of fish in the Doubs river in eastern France and the second dataset describes presence-absence records of plant species in Craters of the Moon National Monument and Preserve (CRMO) in Idaho, USA. Results from both analyses align with existing ecological gradients at each location. Development of the Dirichlet process ordination model provides wildlife managers with data-driven inferences about the number of distinct communities present across monitored locations, allowing for more cost-effective monitoring and reliable decision-making for conservation management.

Methods in Ecology and Evolution↗
USGS2024

A heuristic method to evaluate consequences for flight control and stability induced by attachment of biologging devices to birds and bats

Biologging is central to the study of wildlife, but questions remain about the minimization of effects of biologging devices. Rarely considered are changes biologging devices induce on an animal's centre of mass (COM) and resulting losses of flight control and stability. We applied established aeronautical principles to estimate how the COM of a flying bird or bat may be affected by the typical positioning of a biologging device on the neck, back, hips or tail. We then adopted modified thresholds from aerospace engineering to estimate limits beyond which changes to COM result in fitness-relevant alterations to flight control and stability. Generic models illustrate a trade-off between the placement and mass of a biologging device that influences flight control and stability. Seven species-specific examples show the substantial differences in consequences of changes to COM for animals of different sizes and body types. Placement of a device on the tail always resulted in the greatest shift in COM and placement in the centre of the back resulted in the smallest shift. The 5% weight threshold some use for a biologging device provides little room for error in terms of stability and can easily cause dangerous changes to COM. The 3% weight threshold others use causes considerably smaller changes in the COM, but when placed away from the natural COM, still can affect flight control and stability. Researchers interested in minimizing the effects to fitness of wildlife should consider weight, balance and COM when affixing biologging devices. The farther a device is from the natural COM, the smaller it should be relative to the mass of the animal.

Methods in Ecology and Evolution↗