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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↗
USGS2024

Propagating observation errors to enable scalable and rigorous enumeration of plant population abundance with aerial imagery

Estimating and monitoring plant population size is fundamental for ecological research, as well as conservation and restoration programs. High-resolution imagery has potential to facilitate such estimation and monitoring. However, remotely sensed estimates typically have higher uncertainty than field measurements, risking biased inference on population status. We present a model that accounts for false negative (missed plants) and false positive (misclassified or double-counted plants) error in counts from high-resolution imagery via integration with ground data. We apply it to estimate the abundance of a foundational shrub species in post-wildfire landscapes in the western United States. In these landscapes, plant recruitment is crucial for ecological recovery but locally patchy, motivating the use of spatially extensive measurements from unoccupied aerial systems (UAS). Integrating >16 ha of UAS imagery with >700 georeferenced field plots, we fit our model to generate insights into the prevalence and drivers of observation errors associated with classification algorithms used to distinguish individual plants, relationships between abundance and landscape context, and to generate spatially explicit maps of shrub abundance. Raw counts of plant abundance in high-resolution imagery resulted in substantial false negative and false positive observation errors. The probability of detecting ( p ) adult plants (≥0.25 m tall) varied between sites within 0.52 < p̂adult < 0.82, whereas the detection of smaller plants (<0.25 m) was lower, 0.03 < p̂small < 0.3. On average, we estimate that 19% of all detected plants were false positive errors, which varied spatially in relation to topographic predictors. Abundance declined toward the interior of previous wildfires and was positively associated with terrain roughness. Our study demonstrates that integrated models accounting for imperfect detection improve estimates of plant population abundance derived from inherently imperfect UAS imagery. We believe such models will further improve inference on plant population dynamics—relevant to restoration, wildlife habitat and related objectives—and echo previous calls for remote sensing applications to better differentiate between ecological and observational processes.

Methods in Ecology and Evolution↗
USGS2024

Detecting stochasticity in population time series using a non-parametric test of intrinsic predictability

1. Many ecological systems dominated by stochastic dynamics can produce complex time series that inherently limit forecast accuracy. The ‘intrinsic predictability’ of these systems can be approximated by a time series complexity metric called weighted permutation entropy (WPE). While WPE is a useful metric to gauge forecast performance prior to model building, it is sensitive to noise and may be biased depending on the length of the time series. Here, we introduce a simple randomized permutation test (rWPE) to assess whether a time series is intrinsically more predictable than white noise. 2. We apply rWPE to both simulated and empirical data to assess its performance and usefulness. To do this, we simulate population dynamics under various scenarios, including a linear trend, chaotic, periodic and equilibrium dynamics. We further test this approach with observed abundance time series for 932 species across four orders of animals from the Global Population Dynamics Database. Finally, using Adélie ( Pygoscelis adeliae ) and emperor penguin ( Aptenodytes forsteri ) time series as case studies, we demonstrate the application of rWPE to multiple populations for a single species. 3. We show that rWPE can determine whether a system is significantly more predictable than white noise, even with time series as short as 10 years that show an apparent trend under biologically realistic stochasticity levels. Additionally, rWPE has statistical power close to 100% when time series are at least 30 time steps long and show chaotic or periodic dynamics. Power decreases to ~10% under equilibrium dynamics, irrespective of time series length. Among four classes of animal taxa, mammals have the highest relative frequency (28%) of time series that are both longer than 30 time steps and indistinguishable from white noise in terms of complexity, followed by insects (16%), birds (16%) and bony fishes (11%). 4. rWPE is a straightforward and useful method widely applicable to any time series, including short ones. By informing forecasters of the inherent limitations to a system's predictability, it can guide a modeller's expectations for forecast performance.

Methods in Ecology and Evolution↗
USGS2024

Thinking beyond the closure assumption: Designing surveys for estimating biological truth with occupancy models

Occupancy models estimate distributions of imperfectly detected species, but violations of the closure assumption can bias results. However, researchers working with mobile animals may find it impossible to eliminate such violations. Here, we tested the hypothesis that occupancy models fit to realistic sampling data can generate unbiased occupancy estimates for an itinerant Wood Thrush ( Hylocichla mustelina ) population. In 2013 and 2014, we tracked movements of 41 breeding Wood Thrush males. We modelled territory shift probabilities using logistic exposure models and within-territory movements using continuous-time stochastic process models. We then constructed an individual-based model, simulated (1000 iterations) spatiotemporal locations for individuals and simulated sampling these populations using 162 different point count protocols with variable spatial (sampling radius and point placement method), and temporal (survey length, between-survey intervals and number of surveys) characteristics. We compared occupancy estimates with true values of instantaneous, daily and seasonal occupancy from the simulations. We parameterized continuous time stochastic process models based on movements within 34 unique territories and estimated a daily territory shift probability of 0.0099 (95% CI: 0.0060, 0.0152). Simulated data indicated that estimates of occupancy ranged from 0.18 (0.06, 1.00) to 0.80 (0.71, 0.89) depending on protocol characteristics. Occupancy estimates increased with increasing survey radius, survey length and between-survey interval. Protocols using shorter surveys and between-survey intervals were good estimators for instantaneous occupancy (low bias and mean-squared error) but poor estimators for daily and seasonal occupancy; longer surveys and intervals generated unbiased estimators of daily occupancy but underestimated seasonal occupancy. Logistic regression models that ignored imperfect detection outperformed occupancy models for estimating instantaneous occupancy but not daily or seasonal occupancy. For mobile animals, occupancy of sampling sites changes in space and time. Consequently, the spatial and temporal aspects of a sampling protocol have strong, but predictable, effects on occupancy model parameter estimates. Our results demonstrate that how these factors interact is critical for designing surveys that produce occupancy estimates representative of the biological process of interest to a researcher.

Methods in Ecology and Evolution↗
USGS2024

A transferable approach for quantifying benthic fish sizes and densities in annotated underwater images

1. Benthic fishes are a common target of scientific monitoring but are difficult to quantify because of their close association to bottom habitats that are hard to access. Advances in image-acquisition technologies, machine vision, and deep learning have made capturing and quantifying fishes with cameras increasingly feasible. We present a method and open-source software called ‘FishScale’ to estimate benthic fish lengths, numeric abundance, and biomass density in underwater environments assessed with down-looking monocular images. 2. ‘FishScale’ estimates fish abundances and size frequencies from near-nadir monocular images where fish have already been semantically segmented. The software accounts for lens distortion, underwater magnification effects, and fish body curvature to automatically estimate fish lengths and the areas of images where they were captured. Numeric and biomass density are estimated through a deterministic machine vision algorithm that requires a user-provided length-weight relationship for species of interest and calibration images. 3. Results from validation studies show that lengths and weights can be estimated with high accuracy and precision for round goby ( Neogobius melanostomus ) captured in distorted action camera images, and from large-bodied lake trout ( Salvelinus namaycush ) imaged with a machine vision camera. The real-world utility of the approach is demonstrated in a case study estimating round goby abundances and size frequencies along a 10.7-km transect surveyed with an autonomous underwater vehicle in Lake Michigan, USA. 4. Our validation studies demonstrate that the approach estimates benthic and benthopelagic fish lengths and weights with little bias and good accuracy and precision for species with much different body shapes and sizes. The method is applicable to data collected using a variety of nadir imaging approaches with widespread applications to fisheries monitoring and quantification of any species or object for which nadir images and working distances between the camera and feature of interest are available.

Illinois, Indiana, Michigan, Wisconsin↗