@article{gilbert_amaral_smith_williams_ceyzyk_ayebare_davis_leuenberger_doser_zipkin_2024, title={A century of statistical Ecology}, url={https://doi.org/10.1002/ecy.4283}, DOI={10.1002/ecy.4283}, abstractNote={Abstract As data and computing power have surged in recent decades, statistical modeling has become an important tool for understanding ecological patterns and processes. Statistical modeling in ecology faces two major challenges. First, ecological data may not conform to traditional methods, and second, professional ecologists often do not receive extensive statistical training. In response to these challenges, the journal Ecology has published many innovative statistical ecology papers that introduced novel modeling methods and provided accessible guides to statistical best practices. In this paper, we reflect on Ecology 's history and its role in the emergence of the subdiscipline of statistical ecology, which we define as the study of ecological systems using mathematical equations, probability, and empirical data. We showcase 36 influential statistical ecology papers that have been published in Ecology over the last century and, in so doing, comment on the evolution of the field. As data and computing power continue to increase, we anticipate continued growth in statistical ecology to tackle complex analyses and an expanding role for Ecology to publish innovative and influential papers, advancing the discipline and guiding practicing ecologists.}, journal={Ecology}, author={Gilbert, Neil A. and Amaral, Bruna R. and Smith, Olivia M. and Williams, Peter J. and Ceyzyk, Sydney and Ayebare, Samuel and Davis, Kayla L. and Leuenberger, Wendy and Doser, Jeffrey W. and Zipkin, Elise F.}, year={2024}, month={Jun} }
 @article{bajcz_glisson_doser_larkin_fieberg_2024, title={A within-lake occupancy model for starry stonewort, Nitellopsis obtusa, to support early detection and monitoring}, url={https://doi.org/10.1038/s41598-024-52608-0}, DOI={10.1038/s41598-024-52608-0}, abstractNote={Abstract To efficiently detect aquatic invasive species early in an invasion when control may still be possible, predictions about which locations are likeliest to be occupied are needed at fine scales but are rarely available. Occupancy modeling could provide such predictions given data of sufficient quality and quantity. We assembled a data set for the macroalga starry stonewort ( Nitellopsis obtusa ) across Minnesota and Wisconsin, USA, where it is a new and high-priority invader. We used these data to construct a multi-season, single-species spatial occupancy model that included biotic, abiotic, and movement-related predictors. Distance to the nearest access was an important occurrence predictor, highlighting the likely role boats play in spreading starry stonewort. Fetch and water depth also predicted occupancy. We estimated an average detection probability of 63% at sites with mean non- N. obtusa plant cover, declining to ~ 38% at sites with abundant plant cover, especially that of other Characeae. We recommend that surveyors preferentially search for starry stonewort in areas of shallow depth and high fetch close to boat accesses. We also recommend searching during late summer/early fall when detection is likelier. This study illustrates the utility of fine-scale occupancy modeling for predicting the locations of nascent populations of difficult-to-detect species.}, journal={Scientific Reports}, author={Bajcz, Alex W. and Glisson, Wesley J. and Doser, Jeffrey W. and Larkin, Daniel J. and Fieberg, John R.}, year={2024}, month={Feb} }
 @article{zipkin_doser_2024, title={Context matters in ecological forecasting: Lessons in predicting species distributions}, url={https://doi.org/10.1111/gcb.17123}, DOI={10.1111/gcb.17123}, abstractNote={Forecasting the future state of a species is a tricky process, as there are numerous hidden factors that influence species trajectories in addition to the obvious unknowns about the future state of the planet. We echo the guidance of Clare et al. (2024) to use near‐term and long‐term forecasting in complementary ways. Near‐term forecasts can be used to guide specific management and conservation actions, which can be updated as new data and evidence are collected. Long‐term forecasts can be used to characterize uncertainty further into the future, which can help guide longstanding conservation planning and legislative actions that are based on such uncertainty in possible future outcomes.}, journal={Global Change Biology}, author={Zipkin, Elise F. and Doser, Jeffrey W.}, year={2024}, month={Jan} }
 @article{quinlan_doser_kammerer_grozinger_2024, title={Estimating genus-specific effects of non-native honey bees and urbanization on wild bee communities: A case study in Maryland, United States}, url={https://doi.org/10.1016/j.scitotenv.2024.175783}, DOI={10.1016/j.scitotenv.2024.175783}, journal={Science of The Total Environment}, author={Quinlan, Gabriela M. and Doser, Jeffrey W. and Kammerer, Melanie A. and Grozinger, Christina M.}, year={2024}, month={Nov} }
 @article{kellner_doser_belant_2024, title={Functional R code is rare in species distribution and abundance papers}, volume={11}, ISSN={["1939-9170"]}, url={https://doi.org/10.1002/ecy.4475}, DOI={10.1002/ecy.4475}, journal={ECOLOGY}, author={Kellner, Kenneth F. and Doser, Jeffrey W. and Belant, Jerrold L.}, year={2024}, month={Nov} }
 @article{doser_kéry_saunders_finley_bateman_grand_reault_weed_zipkin_2024, title={Guidelines for the use of spatially varying coefficients in species distribution models}, url={https://doi.org/10.1111/geb.13814}, DOI={10.1111/geb.13814}, abstractNote={Abstract Aim Species distribution models (SDMs) are increasingly applied across macroscales using detection‐nondetection data. These models typically assume that a single set of regression coefficients can adequately describe species–environment relationships and/or population trends. However, such relationships often show nonlinear and/or spatially varying patterns that arise from complex interactions with abiotic and biotic processes that operate at different scales. Spatially varying coefficient (SVC) models can readily account for variability in the effects of environmental covariates. Yet, their use in ecology is relatively scarce due to gaps in understanding the inferential benefits that SVC models can provide compared to simpler frameworks. Innovation Here we demonstrate the inferential benefits of SVC SDMs, with a particular focus on how this approach can be used to generate and test ecological hypotheses regarding the drivers of spatial variability in population trends and species–environment relationships. We illustrate the inferential benefits of SVC SDMs with simulations and two case studies: one that assesses spatially varying trends of 51 forest bird species in the eastern United States over two decades and a second that evaluates spatial variability in the effects of five decades of land cover change on grasshopper sparrow ( Ammodramus savannarum ) occurrence across the continental United States. Main conclusions We found strong support for SVC SDMs compared to simpler alternatives in both empirical case studies. Factors operating at fine spatial scales, accounted for by the SVCs, were the primary divers of spatial variability in forest bird occurrence trends. Additionally, SVCs revealed complex species–habitat relationships with grassland and cropland area for grasshopper sparrow, providing nuanced insights into how future land use change may shape its distribution. These applications display the utility of SVC SDMs to help reveal the environmental factors that drive species distributions across both local and broad scales. We conclude by discussing the potential applications of SVC SDMs in ecology and conservation.}, journal={Global Ecology and Biogeography}, author={Doser, Jeffrey W. and Kéry, Marc and Saunders, Sarah P. and Finley, Andrew O. and Bateman, Brooke L. and Grand, Joanna and Reault, Shannon and Weed, Aaron S. and Zipkin, Elise F.}, year={2024}, month={Apr} }
 @article{doser_finley_saunders_kéry_weed_zipkin_2024, title={Modeling Complex Species-Environment Relationships Through Spatially-Varying Coefficient Occupancy Models}, url={https://doi.org/10.1007/s13253-023-00595-6}, DOI={10.1007/s13253-023-00595-6}, journal={Journal of Agricultural, Biological and Environmental Statistics}, author={Doser, Jeffrey W. and Finley, Andrew O. and Saunders, Sarah P. and Kéry, Marc and Weed, Aaron S. and Zipkin, Elise F.}, year={2024}, month={Jan} }
 @article{kovalenko_doser_bate_six_2024, title={Paired acoustic recordings and point count surveys reveal Clark's nutcracker and whitebark pine associations across Glacier National Park}, url={https://doi.org/10.1002/ece3.10867}, DOI={10.1002/ece3.10867}, abstractNote={Abstract Global declines in tree populations have led to dramatic shifts in forest ecosystem composition, biodiversity, and functioning. These changes have consequences for both forest plant and wildlife communities, particularly when declining species are involved in coevolved mutualisms. Whitebark pine ( Pinus albicaulis ) is a declining keystone species in western North American high‐elevation ecosystems and an obligate mutualist of Clark's nutcracker ( Nucifraga columbiana ), an avian seed predator and disperser. By leveraging traditional point count surveys and passive acoustic monitoring, we investigated how stand characteristics of whitebark pine in a protected area (Glacier National Park, Montana, USA) influenced occupancy and vocal activity patterns in Clark's nutcracker. Using Bayesian spatial occupancy models and generalized linear mixed models, we found that habitat use of Clark's nutcracker was primarily supported by greater cone density and increasing diameter of live whitebark pine. Additionally, we demonstrated the value of performing parallel analyses with traditional point count surveys and passive acoustic monitoring to provide multiple lines of evidence for relationships between Clark's nutcracker and whitebark pine forest characteristics. Our findings allow managers to gauge the whitebark pine conditions important for retaining high nutcracker visitation and prioritize management efforts in whitebark pine ecosystems with low nutcracker visitation.}, journal={Ecology and Evolution}, author={Kovalenko, Vladimir and Doser, Jeffrey W. and Bate, Lisa J. and Six, Diana L.}, year={2024}, month={Jan} }
 @article{doser_finley_kéry_zipkin_2024, title={spAbundance: An R package for single‐species and multi‐species spatially explicit abundance models}, url={https://doi.org/10.1111/2041-210X.14332}, DOI={10.1111/2041-210X.14332}, abstractNote={Abstract Numerous modelling techniques exist to estimate abundance of plant and animal populations. The most accurate methods account for multiple complexities found in ecological data, such as observational biases, spatial autocorrelation, and species correlations. There is, however, a lack of user‐friendly and computationally efficient software to implement the various models, particularly for large data sets. We developed the spAbundance R package for fitting spatially explicit Bayesian single‐species and multi‐species hierarchical distance sampling models, N‐mixture models, and generalized linear mixed models. The models within the package can account for spatial autocorrelation using Nearest Neighbour Gaussian Processes and accommodate species correlations in multi‐species models using a latent factor approach, which enables model fitting for data sets with large numbers of sites and/or species. We provide three vignettes and three case studies that highlight spAbundance functionality. We used spatially explicit multi‐species distance sampling models to estimate density of 16 bird species in Florida, USA, an N‐mixture model to estimate black‐throated blue warbler ( Setophaga caerulescens ) abundance in New Hampshire, USA, and a spatial linear mixed model to estimate forest above‐ground biomass across the continental USA. spAbundance provides a user‐friendly, formula‐based interface to fit a variety of univariate and multivariate spatially explicit abundance models. The package serves as a useful tool for ecologists and conservation practitioners to generate improved inference and predictions on the spatial drivers of abundance in populations and communities.}, journal={Methods in Ecology and Evolution}, author={Doser, Jeffrey W. and Finley, Andrew O. and Kéry, Marc and Zipkin, Elise F.}, year={2024}, month={Jun} }
 @article{zipkin_doser_davis_leuenberger_ayebare_davis_2023, title={Integrated community models: A framework combining multispecies data sources to estimate the status, trends and dynamics of biodiversity}, url={https://doi.org/10.1111/1365-2656.14012}, DOI={10.1111/1365-2656.14012}, abstractNote={Abstract Data deficiencies among rare or cryptic species preclude assessment of community‐level processes using many existing approaches, limiting our understanding of the trends and stressors for large numbers of species. Yet evaluating the dynamics of whole communities, not just common or charismatic species, is critical to understanding and the responses of biodiversity to ongoing environmental pressures. A recent surge in both public science and government‐funded data collection efforts has led to a wealth of biodiversity data. However, these data collection programmes use a wide range of sampling protocols (from unstructured, opportunistic observations of wildlife to well‐structured, design‐based programmes) and record information at a variety of spatiotemporal scales. As a result, available biodiversity data vary substantially in quantity and information content, which must be carefully reconciled for meaningful ecological analysis. Hierarchical modelling, including single‐species integrated models and hierarchical community models, has improved our ability to assess and predict biodiversity trends and processes. Here, we highlight the emerging ‘integrated community modelling’ framework that combines both data integration and community modelling to improve inferences on species‐ and community‐level dynamics. We illustrate the framework with a series of worked examples. Our three case studies demonstrate how integrated community models can be used to extend the geographic scope when evaluating species distributions and community‐level richness patterns; discern population and community trends over time; and estimate demographic rates and population growth for communities of sympatric species. We implemented these worked examples using multiple software methods through the R platform via packages with formula‐based interfaces and through development of custom code in JAGS, NIMBLE and Stan. Integrated community models provide an exciting approach to model biological and observational processes for multiple species using multiple data types and sources simultaneously, thus accounting for uncertainty and sampling error within a unified framework. By leveraging the combined benefits of both data integration and community modelling, integrated community models can produce valuable information about both common and rare species as well as community‐level dynamics, allowing for holistic evaluation of the effects of global change on biodiversity.}, journal={Journal of Animal Ecology}, author={Zipkin, Elise F. and Doser, Jeffrey W. and Davis, Courtney L. and Leuenberger, Wendy and Ayebare, Samuel and Davis, Kayla L.}, year={2023}, month={Dec} }
 @article{doser_finley_banerjee_2023, title={Joint species distribution models with imperfect detection for high‐dimensional spatial data}, url={https://doi.org/10.1002/ecy.4137}, DOI={10.1002/ecy.4137}, abstractNote={Determining the spatial distributions of species and communities is a key task in ecology and conservation efforts. Joint species distribution models are a fundamental tool in community ecology that use multi-species detection-nondetection data to estimate species distributions and biodiversity metrics. The analysis of such data is complicated by residual correlations between species, imperfect detection, and spatial autocorrelation. While many methods exist to accommodate each of these complexities, there are few examples in the literature that address and explore all three complexities simultaneously. Here we developed a spatial factor multi-species occupancy model to explicitly account for species correlations, imperfect detection, and spatial autocorrelation. The proposed model uses a spatial factor dimension reduction approach and Nearest Neighbor Gaussian Processes to ensure computational efficiency for data sets with both a large number of species (e.g., >100) and spatial locations (e.g., 100,000). We compared the proposed model performance to five alternative models, each addressing a subset of the three complexities. We implemented the proposed and alternative models in the spOccupancy software, designed to facilitate application via an accessible, well documented, and open-source R package. Using simulations, we found that ignoring the three complexities when present leads to inferior model predictive performance, and the impacts of failing to account for one or more complexities will depend on the objectives of a given study. Using a case study on 98 bird species across the continental US, the spatial factor multi-species occupancy model had the highest predictive performance among the alternative models. Our proposed framework, together with its implementation in spOccupancy, serves as a user-friendly tool to understand spatial variation in species distributions and biodiversity while addressing common complexities in multi-species detection-nondetection data.}, journal={Ecology}, author={Doser, Jeffrey W. and Finley, Andrew O. and Banerjee, Sudipto}, year={2023}, month={Sep} }
 @article{doser_stoudt_2024, title={“Fractional replication” in single‐visit multi‐season occupancy models: Impacts of spatiotemporal autocorrelation on identifiability}, url={https://doi.org/10.1111/2041-210X.14275}, DOI={10.1111/2041-210X.14275}, abstractNote={Abstract Understanding variation in species occupancy is an important task for conservation. When assessing occupancy patterns over multiple temporal seasons, it is recommended to visit at least a subset of sites multiple times within a season during a period of closure to account for observation biases. However, logistical constraints can inhibit re‐visitation of sites within a season, resulting in the use of single‐visit multi‐season occupancy models. Some have suggested that autocorrelation in space and/or time can provide “fractional replication” to separately estimate occupancy probability from detection probability, but the reliability of such approaches is not well understood. We perform an extensive simulation study to assess the reliability of estimates from single‐visit multi‐season occupancy models under differing amounts of spatial and temporal autocorrelation (“fractional replication”). We assess model performance under both correctly specified models and multiple forms of model mis‐specification, and compare estimates from single‐visit models to models with varying amounts of within‐season replication. We also assess the reliability of single‐visit models to estimate occupancy probability of ovenbirds ( Seiurus aurocapilla ) in New Hampshire, USA. We found less bias in estimates from single‐visit occupancy models with long‐range spatial autocorrelation in occupancy probability compared to short‐range spatial autocorrelation when the model is correctly specified. However, under certain forms of model mis‐specification, estimates from single‐visit multi‐season occupancy models were biased and had low coverage rates regardless of the characteristics of the “fractional replication”. In contrast, models with varying amounts of additional replication were robust to model mis‐specification. Our findings suggest that “fractional replication” cannot replace true replication in terms of occupancy probability identifiability and that researchers should consider the potential inaccuracies when using single‐visit multi‐season occupancy models. We show that a little true replication can go a long way with even 10% of sites being revisited within a season leading to reasonably robust estimates even in the presence of extreme model mis‐specifications. When possible, we recommend performing multiple within‐season visits to at least a subset of spatial locations or integrating single‐visit data with other data sources to mitigate reliance on parametric assumptions required for reliable inference in single‐visit multi‐season occupancy models.}, journal={Methods in Ecology and Evolution}, author={Doser, Jeffrey W. and Stoudt, Sara}, year={2024}, month={Feb} }
 @article{doser_leuenberger_sillett_hallworth_zipkin_2022, title={Integrated community occupancy models: A framework to assess occurrence and biodiversity dynamics using multiple data sources}, url={https://doi.org/10.1111/2041-210X.13811}, DOI={10.1111/2041-210X.13811}, abstractNote={Abstract 1. The occurrence and distributions of wildlife populations and communities are shifting as a result of global changes. To evaluate whether these shifts are negatively impacting biodiversity processes, it is critical to monitor the status, trends and effects of environmental variables on entire communities. However, modelling the dynamics of multiple species simultaneously can require large amounts of diverse data, and few modelling approaches exist to simultaneously provide species and community‐level inferences. 2. We present an ‘integrated community occupancy model’ (ICOM) that unites principles of data integration and hierarchical community modelling in a single framework to provide inferences on species‐specific and community occurrence dynamics using multiple data sources. The ICOM combines replicated and nonreplicated detection–nondetection data sources using a hierarchical framework that explicitly accounts for different detection and sampling processes across data sources. We use simulations to compare the ICOM to previously developed hierarchical community occupancy models and single species integrated distribution models. We then apply our model to assess the occurrence and biodiversity dynamics of foliage‐gleaning birds in the White Mountain National Forest in the northeastern USA from 2010 to 2018 using three independent data sources. 3. Simulations reveal that integrating multiple data sources in the ICOM increased precision and accuracy of species and community‐level inferences compared to single data source models, although benefits of integration were dependent on the information content of individual data sources (e.g. amount of replication). Compared to single species models, the ICOM yielded more precise species‐level estimates. Within our case study, the ICOM had the highest out‐of‐sample predictive performance compared to single species models and models that used only a subset of the three data sources. 4. The ICOM provides more precise estimates of occurrence dynamics compared to multi‐species models using single data sources or integrated single‐species models. We further found that the ICOM had improved predictive performance across a broad region of interest with an empirical case study of forest birds. The ICOM offers an attractive approach to estimate species and biodiversity dynamics, which is additionally valuable to inform management objectives of both individual species and their broader communities.}, journal={Methods in Ecology and Evolution}, author={Doser, Jeffrey W. and Leuenberger, Wendy and Sillett, T. Scott and Hallworth, Michael T. and Zipkin, Elise F.}, year={2022}, month={Apr} }
 @article{doser_finley_kéry_zipkin_2022, title={spOccupancy: An R package for single‐species, multi‐species, and integrated spatial occupancy models}, url={https://doi.org/10.1111/2041-210X.13897}, DOI={10.1111/2041-210X.13897}, abstractNote={Abstract Occupancy modelling is a common approach to assess species distribution patterns, while explicitly accounting for false absences in detection–nondetection data. Numerous extensions of the basic single‐species occupancy model exist to model multiple species, spatial autocorrelation and to integrate multiple data types. However, development of specialized and computationally efficient software to incorporate such extensions, especially for large datasets, is scarce or absent. We introduce the spOccupancy R package designed to fit single‐species and multi‐species spatially explicit occupancy models. We fit all models within a Bayesian framework using Pólya‐Gamma data augmentation, which results in fast and efficient inference. spOccupancy provides functionality for data integration of multiple single‐species detection–nondetection datasets via a joint likelihood framework. The package leverages Nearest Neighbour Gaussian Processes to account for spatial autocorrelation, which enables spatially explicit occupancy modelling for potentially massive datasets (e.g. 1,000s–100,000s of sites). spOccupancy provides user‐friendly functions for data simulation, model fitting, model validation (by posterior predictive checks), model comparison (using information criteria and k‐fold cross‐validation) and out‐of‐sample prediction. We illustrate the package's functionality via a vignette, simulated data analysis and two bird case studies. The spOccupancy package provides a user‐friendly platform to fit a variety of single and multi‐species occupancy models, making it straightforward to address detection biases and spatial autocorrelation in species distribution models even for large datasets.}, journal={Methods in Ecology and Evolution}, author={Doser, Jeffrey W. and Finley, Andrew O. and Kéry, Marc and Zipkin, Elise F.}, year={2022}, month={Aug} }
 @article{doser_finley_weed_zipkin_2021, title={Integrating automated acoustic vocalization data and point count surveys for estimation of bird abundance}, url={https://doi.org/10.1111/2041-210X.13578}, DOI={10.1111/2041-210X.13578}, abstractNote={Monitoring wildlife abundance across space and time is an essential task to study their population dynamics and inform effective management. Acoustic recording units are a promising technology for efficiently monitoring bird populations and communities. We present an integrated modeling framework that combines high-quality but temporally sparse bird point count survey data with acoustic recordings. Using simulations, we compare the accuracy and precision of abundance estimates using differing amounts of acoustic vocalizations obtained from a clustering algorithm, point count data, and a subset of manually validated acoustic vocalizations. We also use our modeling framework in a case study to estimate abundance of the Eastern Wood-Pewee (Contopus virens) in Vermont, U.S.A. The simulation study reveals that combining acoustic and point count data via an integrated model improves accuracy and precision of abundance estimates compared with models informed by either acoustic or point count data alone. Combining acoustic data with only a small number of point count surveys yields estimates of abundance without the need for validating any of the identified vocalizations from the acoustic data. Within our case study, the integrated models provided moderate support for a decline of the Eastern Wood-Pewee in this region. Our integrated modeling approach combines dense acoustic data with few point count surveys to deliver reliable estimates of species abundance without the need for manual identification of acoustic vocalizations or a prohibitively expensive large number of repeated point count surveys. Our proposed approach offers an efficient monitoring alternative for large spatio-temporal regions when point count data are difficult to obtain or when monitoring is focused on rare species with low detection probability.}, journal={Methods in Ecology and Evolution}, author={Doser, Jeffrey W. and Finley, Andrew O. and Weed, Aaron S. and Zipkin, Elise F.}, year={2021}, month={Jun} }
 @article{doser_weed_zipkin_miller_finley_2021, title={Trends in bird abundance differ among protected forests but not bird guilds}, volume={31}, url={https://doi.org/10.1002/eap.2377}, DOI={10.1002/eap.2377}, abstractNote={Improved monitoring and associated inferential tools to efficiently identify declining bird populations, particularly of rare or sparsely distributed species, is key to informed conservation and management across large spatio-temporal regions. We assess abundance trends for 106 bird species in a network of eight national park forests located within the northeast USA from 2006-2019 using a novel hierarchical model. We develop a multi-species, multi-region removal sampling model that shares information across species and parks to enable inference on rare species and sparsely sampled parks and to evaluate the effects of local forest structure. Trends in bird abundance over time varied widely across parks, but species showed similar trends within parks. Three parks (Acadia, Marsh-Billings-Rockefeller, and Morristown) decreased in bird abundance across all species, while three parks (Saratoga, Roosevelt-Vanderbilt, and Weir-Farm) increased in abundance. Bird abundance peaked at medium levels of basal area and high levels of percent forest and forest regeneration, with percent forest having the largest effect. Variation in these effects across parks could be a result of differences in forest structural stage and diversity. Our novel hierarchical model enables estimates of abundance at the network, park, guild, and species levels. We found large variation in abundance trends across parks but not across bird guilds, suggesting that local forest condition may have a broad and consistent effect on the entire bird community within a given park. Management should target the three parks with overall decreasing trends in bird abundance to further identify what specific factors are driving observed declines across the bird community. Understanding how bird communities respond to local forest structure and other stressors is crucial for informed and lasting management.}, number={6}, journal={Ecological Applications}, publisher={Wiley}, author={Doser, Jeffrey W. and Weed, Aaron S. and Zipkin, Elise F. and Miller, Kathryn M. and Finley, Andrew O.}, year={2021}, month={Sep} }
 @article{doser_finley_kéry_zipkin_2021, title={spOccupancy: An R package for single species, multispecies, and integrated spatial occupancy models}, url={https://arxiv.org/abs/2111.12163}, journal={arXiv}, author={Doser, Jeffrey W. and Finley, Andrew O. and Kéry, Marc and Zipkin, Elise F.}, year={2021}, month={Nov} }
 @article{doser_finley_kasten_gage_2020, title={Assessing soundscape disturbance through hierarchical models and acoustic indices: A case study on a shelterwood logged northern Michigan forest}, volume={113}, url={https://doi.org/10.1016/j.ecolind.2020.106244}, DOI={10.1016/j.ecolind.2020.106244}, journal={Ecological Indicators}, publisher={Elsevier BV}, author={Doser, Jeffrey W. and Finley, Andrew O. and Kasten, Eric P. and Gage, Stuart H.}, year={2020}, month={Jun}, pages={106244} }
 @article{doser_hannam_finley_2020, title={Characterizing functional relationships between anthropogenic and biological sounds: a western New York state soundscape case study}, volume={35}, url={https://doi.org/10.1007/s10980-020-00973-2}, DOI={10.1007/s10980-020-00973-2}, number={3}, journal={Landscape Ecology}, publisher={Springer Science and Business Media LLC}, author={Doser, Jeffrey W. and Hannam, Kristina M. and Finley, Andrew O.}, year={2020}, month={Mar}, pages={689–707} }
 @article{teimouri_doser_finley_2020, title={ForestFit: An R package for modeling plant size distributions}, volume={131}, url={http://dx.doi.org/10.1016/j.envsoft.2020.104668}, DOI={10.1016/j.envsoft.2020.104668}, journal={Environmental Modelling & Software}, publisher={Elsevier BV}, author={Teimouri, Mahdi and Doser, Jeffrey W. and Finley, Andrew O.}, year={2020}, month={Sep}, pages={104668} }