Integrated distance sampling models for simple point counts.

Point counts (PCs) are widely used in biodiversity surveys but, despite numerous advantages, simple PCs suffer from several problems: detectability, and therefore abundance, is unknown; systematic spatiotemporal variation in detectability yields biased inferences, and unknown survey area prevents formal density estimation and scaling-up to the landscape level. We introduce integrated distance sampling (IDS) models that combine distance sampling (DS) with simple PC or detection/nondetection (DND) data to capitalize on the strengths and mitigate the weaknesses of each data type. Key to IDS models is the view of simple PC and DND data as aggregations of latent DS surveys that observe the same underlying density process. This enables the estimation of separate detection functions, along with distinct covariate effects, for all data types. Additional information from repeat or time-removal surveys, or variable survey duration, enables the separate estimation of the availability and perceptibility components of detectability with DS and PC data. IDS models reconcile spatial and temporal mismatches among data sets and solve the above-mentioned problems of simple PC and DND data. To fit IDS models, we provide JAGS code and the new "IDS()" function in the R package unmarked. Extant citizen-science data generally lack the information necessary to adjust for detection biases, but IDS models address this shortcoming, thus greatly extending the utility and reach of these data. In addition, they enable formal density estimation in hybrid designs, which efficiently combine DS with distance-free, point-based PC or DND surveys. We believe that IDS models have considerable scope in ecology, management, and monitoring.

Density-habitat relationships of white-tailed deer (Odocoileus virginianus) in Finland.

In heterogeneous landscapes, resource selection constitutes a crucial link between landscape and population-level processes such as density. We conducted a non-invasive genetic study of white-tailed deer in southern Finland in 2016 and 2017 using fecal DNA samples to understand factors influencing white-tailed deer density and space use in late summer prior to the hunting season. We estimated deer density as a function of landcover types using a spatial capture-recapture (SCR) model with individual identities established using microsatellite markers. The study revealed second-order habitat selection with highest deer densities in fields and mixed forest, and third-order habitat selection (detection probability) for transitional woodlands (clear-cuts) and closeness to fields. Including landscape heterogeneity improved model fit and increased inferred total density compared with models assuming a homogenous landscape. Our findings underline the importance of including habitat covariates when estimating density and exemplifies that resource selection can be studied using non-invasive methods.

Sharing land via keystone structure: Retaining naturally regenerated trees may efficiently benefit birds in plantations.

Meeting food/wood demands with increasing human population and per-capita consumption is a pressing conservation issue, and is often framed as a choice between land sparing and land sharing. Although most empirical studies comparing the efficacy of land sparing and sharing supported land sparing, land sharing may be more efficient if its performance is tested by rigorous experimental design and habitat structures providing crucial resources for various species-keystone structures-are clearly involved. We launched a manipulative experiment to retain naturally regenerated broad-leaved trees when harvesting conifer plantations in central Hokkaido, northern Japan. We surveyed birds in harvested treatments, unharvested plantation controls, and natural forest references 1-year before the harvest and for three consecutive postharvest years. We developed a hierarchical community model separating abundance and space use (territorial proportion overlapping treatment plots) subject to imperfect detection to assess population consequences of retention harvesting. Application of the model to our data showed that retaining some broad-leaved trees increased the total abundance of forest birds over the harvest rotation cycle. Specifically, a preharvest survey showed that the amount of broad-leaved trees increased forest bird abundance in a concave manner (i.e., in the form of diminishing returns). After harvesting, a small amount of retained broad-leaved trees mitigated negative harvesting impacts on abundance, although retention harvesting reduced the space use. Nevertheless, positive retention effects on the postharvest bird density as the product of abundance and space use exhibited a concave form. Thus, small profit reductions were shown to yield large increases in forest bird abundance. The difference in bird abundance between clearcutting and low amounts of broad-leaved tree retention increased slightly from the first to second postharvesting years. We conclude that retaining a small amount of broad-leaved trees may be a cost-effective on-site conservation approach for the management of conifer plantations. The retention of 20-30 broad-leaved trees per ha may be sufficient to maintain higher forest bird abundance than clearcutting over the rotation cycle. Retention approaches can be incorporated into management systems using certification schemes and best management practices. Developing an awareness of the roles and values of naturally regenerated trees is needed to diversify plantations.

Modeling spatiotemporal abundance and movement dynamics using an integrated spatial capture-recapture movement model.

Animal movement is a fundamental ecological process affecting the survival and reproduction of individuals, the structure of populations, and the dynamics of communities. Methods to quantify animal movement and spatiotemporal abundances, however, are generally separate and therefore omit linkages between individual-level and population-level processes. We describe an integrated spatial capture-recapture (SCR) movement model to jointly estimate (1) the number and distribution of individuals in a defined spatial region and (2) movement of those individuals through time. We applied our model to a study of polar bears (Ursus maritimus) in a 28,125 km2 survey area of the eastern Chukchi Sea, USA in 2015 that incorporated capture-recapture and telemetry data. In simulation studies, the model provided unbiased estimates of movement, abundance, and detection parameters using a bivariate normal random walk and correlated random walk movement process. Our case study provided detailed evidence of directional movement persistence for both male and female bears, where individuals regularly traversed areas larger than the survey area during the 36-day study period. Scaling from individual- to population-level inferences, we found that densities varied from <0.75 bears/625 km2 grid cell/day in nearshore cells to 1.6-2.5 bears/grid cell/day for cells surrounded by sea ice. Daily abundance estimates ranged from 53 to 69 bears, with no trend across days. The cumulative number of unique bears that used the survey area increased through time due to movements into and out of the area, resulting in an estimated 171 individuals using the survey area during the study (95% credible interval 124-250). Abundance estimates were similar to a previous multiyear integrated population model using capture-recapture and telemetry data (2008-2016; Regehr et al., Scientific Reports 8:16780, 2018). Overall, the SCR-movement model successfully quantified both individual- and population-level space use, including the effects of landscape characteristics on movement, abundance, and detection, while linking the movement and abundance processes to directly estimate density within a prescribed spatial region and temporal period. Integrated SCR-movement models provide a generalizable approach to incorporate greater movement realism into population dynamics and link movement to emergent properties including spatiotemporal densities and abundances.

Leveraging community science data for population assessments during a pandemic.

The COVID-19 pandemic has disrupted field research programs, making conservation and management decision-making more challenging. However, it may be possible to conduct population assessments using integrated models that combine community science data with existing data from structured surveys. We developed a space-time integrated model to characterize spatial and temporal variability in population distribution. We fit our integrated model to 10 years of eBird (2010-2020) and 9 years of aerial survey (2010-2019) Mottled Duck count data to forecast 2020 population size along the western Gulf Coast of Texas and Louisiana. Estimates of Mottled Duck abundance were similar in magnitude to estimates calculated using previous methods but were more precise and showed evidence of a declining population. The spatial distribution for Mottled Ducks each year was characterized by several concentrations of relatively high abundance, although the location of these abundance "hotspots" varied over time. Expected abundance was higher for areas with a higher proportion of area covered by marsh habitat. By leveraging large-scale community science data, we were able to conduct a population assessment despite the disruption in structured surveys caused by the pandemic. As participation in community science platforms continues to increase, we anticipate modeling frameworks, like the integrated model we developed here, will become increasingly useful for informing conservation and management decision-making.

Evaluation of a two-season banding program to estimate and model migratory bird survival.

The management of North American waterfowl is predicated on long-term, continental-scale banding implemented prior to the hunting season (i.e., July-September) and subsequent reporting of bands recovered by hunters. However, single-season banding and encounter operations have a number of characteristics that limit their application to estimating demographic rates and evaluating hypothesized limiting factors throughout the annual cycle. We designed and implemented a two-season banding program for American Black Ducks (Anas rubripes), Mallards (A. platyrhynchos), and hybrids in eastern North America to evaluate potential application to annual life cycle conservation and sport harvest management. We assessed model fit and compared estimates of annual survival among data types (i.e., pre-hunting season only [July-September], post-hunting season only [January-March], and two-season [pre- and post-hunting season]) to evaluate model assumptions and potential application to population modeling and management. There was generally high agreement between estimates of annual survival derived using two-season and pre-season only data for all age and sex cohorts. Estimates of annual survival derived from post-season banding data only were consistently higher for adult females and juveniles of both sexes. We found patterns of seasonal survival varied by species, age, and to a lesser extent, sex. Hunter recovered birds exhibited similar spatial distributions regardless of banding season suggesting banded samples were from the same population. In contrast, goodness-of-fit tests suggest this assumption was statistically violated in some regions and years. We conclude that estimates of seasonal and annual survival for Black Ducks and Mallards based on the two-season banding program are valid and accurate based on model fit statistics, similarity in survival estimates across data and models, and similarities in the distribution of recoveries. The two-season program provides greater precision and insight into the survival process and will improve the ability of researchers and managers to test competing hypotheses regarding population regulation resulting in more effective management.

Toxic epidermal necrolysis-like lupus.

Toxic epidermal necrosis (TEN)-like lupus is a rare condition characterized by epidermal loss and mucosal ulceration occurring in patients with acute severe flares of systemic lupus erythematosus. The clinical picture may mimic drug-induced Stevens-Johnson syndrome/TEN; however, the absence of a suitable culprit drug, and the context of acute lupus point to the correct diagnosis. In a case series of three patients, further discriminating features included a slower onset of epidermal loss, more limited mucosal ulceration and a lack of ocular involvement when compared with drug-induced TEN. Histology may show similar features, including basal layer vacuolation, apoptosis and full-thickness epidermal necrosis. Patients with TEN-like lupus may have additional features of lupus, and a lupus band on direct immunofluorescence. It is important to identify this condition correctly, so that these patients can be appropriately managed with early input from Rheumatologists and prompt treatment with high-dose combined immunosuppressant therapy.

Spatial capture-recapture with random thinning for unidentified encounters.

Spatial capture-recapture (SCR) models have increasingly been used as a basis for combining capture-recapture data types with variable levels of individual identity information to estimate population density and other demographic parameters. Recent examples are the unmarked SCR (or spatial count model), where no individual identities are available and spatial mark-resight (SMR) where individual identities are available for only a marked subset of the population. Currently lacking, though, is a model that allows unidentified samples to be combined with identified samples when there are no separate classes of "marked" and "unmarked" individuals and when the two sample types cannot be considered as arising from two independent observation models. This is a common scenario when using noninvasive sampling methods, for example, when analyzing data on identified and unidentified photographs or scats from the same sites.Here we describe a "random thinning" SCR model that utilizes encounters of both known and unknown identity samples using a natural mechanistic dependence between samples arising from a single observation model. Our model was fitted in a Bayesian framework using NIMBLE.We investigate the improvement in parameter estimates by including the unknown identity samples, which was notable (up to 79% more precise) in low-density populations with a low rate of identified encounters. We then applied the random thinning SCR model to a noninvasive genetic sampling study of brown bear (Ursus arctos) density in Oriental Cantabrian Mountains (North Spain).Our model can improve density estimation for noninvasive sampling studies for low-density populations with low rates of individual identification, by making use of available data that might otherwise be discarded.

Optimal sampling design for spatial capture-recapture.

Spatial capture-recapture (SCR) has emerged as the industry standard for estimating population density by leveraging information from spatial locations of repeat encounters of individuals. The precision of density estimates depends fundamentally on the number and spatial configuration of traps. Despite this knowledge, existing sampling design recommendations are heuristic and their performance remains untested for most practical applications. To address this issue, we propose a genetic algorithm that minimizes any sensible, criteria-based objective function to produce near-optimal sampling designs. To motivate the idea of optimality, we compare the performance of designs optimized using three model-based criteria related to the probability of capture. We use simulation to show that these designs outperform those based on existing recommendations in terms of bias, precision, and accuracy in the estimation of population size. Our approach, available as a function in the R package oSCR, allows conservation practitioners and researchers to generate customized and improved sampling designs for wildlife monitoring.

Estimating and forecasting spatial population dynamics of apex predators using transnational genetic monitoring.

The ongoing recovery of terrestrial large carnivores in North America and Europe is accompanied by intense controversy. On the one hand, reestablishment of large carnivores entails a recovery of their most important ecological role, predation. On the other hand, societies are struggling to relearn how to live with apex predators that kill livestock, compete for game species, and occasionally injure or kill people. Those responsible for managing these species and mitigating conflict often lack fundamental information due to a long-standing challenge in ecology: How do we draw robust population-level inferences for elusive animals spread over immense areas? Here we showcase the application of an effective tool for spatially explicit tracking and forecasting of wildlife population dynamics at scales that are relevant to management and conservation. We analyzed the world's largest dataset on carnivores comprising more than 35,000 noninvasively obtained DNA samples from over 6,000 individual brown bears (Ursus arctos), gray wolves (Canis lupus), and wolverines (Gulo gulo). Our analyses took into account that not all individuals are detected and, even if detected, their fates are not always known. We show unequivocal quantitative evidence of large carnivore recovery in northern Europe, juxtaposed with the finding that humans are the single-most important factor driving the dynamics of these apex predators. We present maps and forecasts of the spatiotemporal dynamics of large carnivore populations, transcending national boundaries and management regimes.

Spatial proximity moderates genotype uncertainty in genetic tagging studies.

Accelerating declines of an increasing number of animal populations worldwide necessitate methods to reliably and efficiently estimate demographic parameters such as population density and trajectory. Standard methods for estimating demographic parameters from noninvasive genetic samples are inefficient because lower-quality samples cannot be used, and they assume individuals are identified without error. We introduce the genotype spatial partial identity model (gSPIM), which integrates a genetic classification model with a spatial population model to combine both spatial and genetic information, thus reducing genotype uncertainty and increasing the precision of demographic parameter estimates. We apply this model to data from a study of fishers (Pekania pennanti) in which 37% of hair samples were originally discarded because of uncertainty in individual identity. The gSPIM density estimate using all collected samples was 25% more precise than the original density estimate, and the model identified and corrected three errors in the original individual identity assignments. A simulation study demonstrated that our model increased the accuracy and precision of density estimates 63 and 42%, respectively, using three replicated assignments (e.g., PCRs for microsatellites) per genetic sample. Further, the simulations showed that the gSPIM model parameters are identifiable with only one replicated assignment per sample and that accuracy and precision are relatively insensitive to the number of replicated assignments for high-quality samples. Current genotyping protocols devote the majority of resources to replicating and confirming high-quality samples, but when using the gSPIM, genotyping protocols could be more efficient by devoting more resources to low-quality samples.

Movement-assisted localization from acoustic telemetry data.

BACKGROUND: Acoustic telemetry technologies are being increasingly deployed to study a variety of aquatic taxa including fishes, reptiles, and marine mammals. Large cooperative telemetry networks produce vast quantities of data useful in the study of movement, resource selection and species distribution. Efficient use of acoustic telemetry data requires estimation of acoustic source locations from detections at receivers (i.e., "localization"). Multiple processes provide information for localization estimation including detection/non-detection data at receivers, information on signal rate, and an underlying movement model describing how individuals move and utilize space. Frequently, however, localization methods only integrate a subset of these processes and do not utilize the full spatial encounter history information available from receiver arrays. METHODS: In this paper we draw analogies between the challenges of acoustic telemetry localization and newly developed methods of spatial capture-recapture (SCR). We develop a framework for localization that integrates explicit sub-models for movement, signal (or cue) rate, and detection probability, based on acoustic telemetry spatial encounter history data. This method, which we call movement-assisted localization, makes efficient use of the full encounter history data available from acoustic receiver arrays, provides localizations with fewer than three detections, and even allows for predictions to be made of the position of an individual when it was not detected at all. We demonstrate these concepts by developing generalizable Bayesian formulations of the SCR movement-assisted localization model to address study-specific challenges common in acoustic telemetry studies. RESULTS: Simulation studies show that movement-assisted localization models improve point-wise RMSE of localization estimates by >50% and greatly increased the precision of estimated trajectories compared to localization using only the detection history of a given signal. Additionally, integrating a signal rate sub-model reduced biases in the estimation of movement, signal rate, and detection parameters observed in independent localization models. CONCLUSIONS: Movement-assisted localization provides a flexible framework to maximize the use of acoustic telemetry data. Conceptualizing localization within an SCR framework allows extensions to a variety of data collection protocols, improves the efficiency of studies interested in movement, resource selection, and space-use, and provides a unifying framework for modeling acoustic data.

Migratory behavior and winter geography drive differential range shifts of eastern birds in response to recent climate change.

Over the past half century, migratory birds in North America have shown divergent population trends relative to resident species, with the former declining rapidly and the latter increasing. The role that climate change has played in these observed trends is not well understood, despite significant warming over this period. We used 43 y of monitoring data to fit dynamic species distribution models and quantify the rate of latitudinal range shifts in 32 species of birds native to eastern North America. Since the early 1970s, species that remain in North America throughout the year, including both resident and migratory species, appear to have responded to climate change through both colonization of suitable area at the northern leading edge of their breeding distributions and adaption in place at the southern trailing edges. Neotropical migrants, in contrast, have shown the opposite pattern: contraction at their southern trailing edges and no measurable shifts in their northern leading edges. As a result, the latitudinal distributions of temperate-wintering species have increased while the latitudinal distributions of neotropical migrants have decreased. These results raise important questions about the mechanisms that determine range boundaries of neotropical migrants and suggest that these species may be particularly vulnerable to future climate change. Our results highlight the potential importance of climate change during the nonbreeding season in constraining the response of migratory species to temperature changes at both the trailing and leading edges of their breeding distributions. Future research on the interactions between breeding and nonbreeding climate change is urgently needed.

Author Correction: Large-scale variation in density of an aquatic ecosystem indicator species.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Using bear rub data and spatial capture-recapture models to estimate trend in a brown bear population.

Trends in population abundance can be challenging to quantify during range expansion and contraction, when there is spatial variation in trend, or the conservation area is large. We used genetic detection data from natural bear rubbing sites and spatial capture-recapture (SCR) modeling to estimate local density and population growth rates in a grizzly bear population in northwestern Montana, USA. We visited bear rubs to collect hair in 2004, 2009-2012 (3,579-4,802 rubs) and detected 249-355 individual bears each year. We estimated the finite annual population rate of change 2004-2012 was 1.043 (95% CI = 1.017-1.069). Population density shifted from being concentrated in the north in 2004 to a more even distribution across the ecosystem by 2012. Our genetic detection sampling approach coupled with SCR modeling allowed us to estimate spatially variable growth rates of an expanding grizzly bear population and provided insight into how those patterns developed. The ability of SCR to utilize unstructured data and produce spatially explicit maps that indicate where population change is occurring promises to facilitate the monitoring of difficult-to-study species across large spatial areas.

Modeling spatially and temporally complex range dynamics when detection is imperfect.

Species distributions are determined by the interaction of multiple biotic and abiotic factors, which produces complex spatial and temporal patterns of occurrence. As habitats and climate change due to anthropogenic activities, there is a need to develop species distribution models that can quantify these complex range dynamics. In this paper, we develop a dynamic occupancy model that uses a spatial generalized additive model to estimate non-linear spatial variation in occupancy not accounted for by environmental covariates. The model is flexible and can accommodate data from a range of sampling designs that provide information about both occupancy and detection probability. Output from the model can be used to create distribution maps and to estimate indices of temporal range dynamics. We demonstrate the utility of this approach by modeling long-term range dynamics of 10 eastern North American birds using data from the North American Breeding Bird Survey. We anticipate this framework will be particularly useful for modeling species' distributions over large spatial scales and for quantifying range dynamics over long temporal scales.

Incorporating citizen science data in spatially explicit integrated population models.

Information about population abundance, distribution, and demographic rates is critical for understanding a species' ecology and for effective conservation and management. To collect data over large spatial and temporal extents for such inferences, especially for species with low densities or wide distributions, citizen science can be an efficient approach. Integrated models have also emerged as an important methodology to estimate population parameters by combining multiple types of data, including citizen science data. We developed a spatially explicit integrated model that combines opportunistically collected presence-absence (PA) data, commonly collected in citizen science efforts, with systematically collected spatial capture-recapture (SCR) data, which are often limited to small spatial and temporal extents. We conducted single and multi-season simulations with parameters informed by North American black bear (Ursus americanus) populations, to evaluate the influence of varying amounts of opportunistic PA data collected at larger spatial and temporal extents on the estimation of population-level parameters. Integrating opportunistic PA data increased the precision and accuracy of posterior estimates of abundance, and survival and recruitment rates. In some cases, adding PA locations improved abundance estimates more than increasing PA detection probability. Posterior estimates were as precise and unbiased as when higher quality, but sparse, SCR data were available. We also applied the integrated model to SCR and citizen science PA data collected on black bears in New York, with results consistent with our simulations. Our findings indicate that citizen science in integrated models can be a cost-efficient way to improve estimates of population parameters and increase the spatiotemporal extent of inference. Continued developments with integrated models and citizen science data will offer additional ways to improve our understanding of population structure and demographics.

Reserve design to optimize functional connectivity and animal density.

Ecological distance-based spatial capture-recapture models (SCR) are a promising approach for simultaneously estimating animal density and connectivity, both of which affect spatial population processes and ultimately species persistence. We explored how SCR models can be integrated into reserve-design frameworks that explicitly acknowledge both the spatial distribution of individuals and their space use resulting from landscape structure. We formulated the design of wildlife reserves as a budget-constrained optimization problem and conducted a simulation to explore 3 different SCR-informed optimization objectives that prioritized different conservation goals by maximizing the number of protected individuals, reserve connectivity, and density-weighted connectivity. We also studied the effect on our 3 objectives of enforcing that the space-use requirements of individuals be met by the reserve for individuals to be considered conserved (referred to as home-range constraints). Maximizing local population density resulted in fragmented reserves that would likely not aid long-term population persistence, and maximizing the connectivity objective yielded reserves that protected the fewest individuals. However, maximizing density-weighted connectivity or preemptively imposing home-range constraints on reserve design yielded reserves of largely spatially compact sets of parcels covering high-density areas in the landscape with high functional connectivity between them. Our results quantify the extent to which reserve design is constrained by individual home-range requirements and highlight that accounting for individual space use in the objective and constraints can help in the design of reserves that balance abundance and connectivity in a biologically relevant manner.

Diseño de Reservas para Optimizar la Conectividad Funcional y la Densidad Animal Resumen Los modelos de captura-recaptura espacial (CRE) basados en distancias ecológicas son un método prometedor para estimar la densidad animal y la conectividad, las cuales afectan los procesos poblacionales espaciales y, en última instancia, la persistencia de las especies. Exploramos cómo se puede integrar a los modelos CRE en los marcos de diseño de reserva que explícitamente reconocen tanto la distribución espacial de los individuos como su uso del espacio resultante de la estructura del paisaje. Formulamos el diseño de reservas de vida silvestre como un problema de optimización de presupuesto limitado y realizamos una simulación para explorar 3 diferentes objetivos de optimización informados por CRE que priorizaron diferentes metas de conservación mediante la maximización del número de individuos protegidos; la conectividad de la reserva y la conectividad ponderada por la densidad. También estudiamos el efecto sobre nuestros objetivos de hacer que los requerimientos individuales de uso de espacio fuesen satisfechos por la reserva de manera que se pudiese considerar que los individuos estaban protegidos (referidos como restricciones de rango de hogar). La maximización de la densidad de la población local resultó en reservas fragmentadas que probablemente no contribuyan a la persistencia de la población a largo plazo, mientras que la maximización de la conectividad produjo reservas que protegían al menor número de individuos. Sin embargo, la maximización de la conectividad ponderada por la densidad o la imposición preventiva de restricciones de rango de hogar en el diseño de reservas produjo reservas compuestas por conjuntos de parcelas mayormente compactas espacialmente que cubrían áreas de densidad alta en el paisaje con alta conectividad funcional entre ellas. Nuestros resultados cuantifican la extensión a la cual el diseño de reservas esta limitado por los requerimientos de rango de hogar individuales y resaltan que la consideración del uso de espacio individual en el objetivo y limitaciones puede ayudar al diseño de reservas que equilibren la abundancia y la conectividad de manera biológicamente relevante.

Genetic tagging in the Anthropocene: scaling ecology from alleles to ecosystems.

The Anthropocene is an era of marked human impact on the world. Quantifying these impacts has become central to understanding the dynamics of coupled human-natural systems, resource-dependent livelihoods, and biodiversity conservation. Ecologists are facing growing pressure to quantify the size, distribution, and trajectory of wild populations in a cost-effective and socially acceptable manner. Genetic tagging, combined with modern computational and genetic analyses, is an under-utilized tool to meet this demand, especially for wide-ranging, elusive, sensitive, and low-density species. Genetic tagging studies are now revealing unprecedented insight into the mechanisms that control the density, trajectory, connectivity, and patterns of human-wildlife interaction for populations over vast spatial extents. Here, we outline the application of, and ecological inferences from, new analytical techniques applied to genetically tagged individuals, contrast this approach with conventional methods, and describe how genetic tagging can be better applied to address outstanding questions in ecology. We provide example analyses using a long-term genetic tagging dataset of grizzly bears in the Canadian Rockies. The genetic tagging toolbox is a powerful and overlooked ensemble that ecologists and conservation biologists can leverage to generate evidence and meet the challenges of the Anthropocene.