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Poddar U, Lam K, Gurevitch J. Trends in research approaches and gender in plant ecology dissertations over four decades. Ecol Evol 2024; 14:e11554. [PMID: 38863722 PMCID: PMC11165400 DOI: 10.1002/ece3.11554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 05/20/2024] [Accepted: 05/26/2024] [Indexed: 06/13/2024] Open
Abstract
Dissertations are a foundational scientific product; they are the formative product that early-career scientists create and share original knowledge. The methodological approaches used in dissertations vary with the research field. In plant ecology, these approaches include observations, experiments (field or controlled environment), literature reviews, theoretical approaches, or analyses of existing data (including "big data"). Recently, concerns have been raised about the rise of "big data" studies and the loss of observational and field-based studies in ecology, but such trends have not been formally quantified. Therefore, we examined how the emphasis on each of these categories has changed over time and whether male and female authors differ in the methods employed. We found remarkable temporal consistency, with observational studies being dominant over the entire time span examined. There was an increase in the number of approaches employed per dissertation, with increases in analyses of databases and theoretical studies adding to rather than replacing traditional methodologies (like observations and field experiments). The representation of women increased over time. There were some differences in the approaches taken by men and women, which requires further investigation.
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Affiliation(s)
- Urmi Poddar
- Department of Ecology and EvolutionStony Brook UniversityStony BrookNew YorkUSA
| | - Kristi Lam
- Department of Ecology and EvolutionStony Brook UniversityStony BrookNew YorkUSA
- Roslyn High SchoolRoslyn HeightsNew YorkUSA
| | - Jessica Gurevitch
- Department of Ecology and EvolutionStony Brook UniversityStony BrookNew YorkUSA
- Department of Forestry and Natural ResourcesPurdue UniversityWest LafayetteIndianaUSA
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2
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Nater CR. Redefining 'state-of-the-art' for integrated population models with immigration. J Anim Ecol 2024; 93:520-524. [PMID: 38634153 DOI: 10.1111/1365-2656.14087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 03/22/2024] [Indexed: 04/19/2024]
Abstract
Research Highlight: Christian, M., Oosthuizen, W. C., Bester, M. N., & de Bruyn, P. N. (2024). Robustly estimating the demographic contribution of immigration: Simulation, sensitivity analysis and seals. Journal of Animal Ecology. https://doi.org/10.1111/1365-2656.14053. Immigration can have profound consequences for local population dynamics and demography, but collecting data to accurately quantifying it is challenging. The recent rise of integrated population models (IPMs) offers an alternative by making it possible to estimate immigration without the need for explicit data, and to quantify its contribution to population dynamics through transient Life Table Response Experiments (tLTREs). Simulation studies have, however, highlighted that this approach can be prone to bias and overestimation. In their new study, Christian et al. address one of the root causes of this issue by improving the estimation of time variation in vital rates and immigration using Gaussian processes in lieu of traditionally used temporal random effects. They demonstrate that IPM-tLTRE frameworks with Gaussian processes produce more accurate and less biased estimates of immigration and its contribution to population dynamics and illustrate the applicability of this approach using a long-term data set on elephant seals (Mirounga leonida). Results are validated with a simulation study and suggest that immigration of breeding females has been central for population recovery of elephant seals despite the species' high female site fidelity. Christian et al. thus present new insights into population regulation of long-lived marine mammals and highlight the potential for using Gaussian process priors in IPMs. They also illustrate a suite of 'best practices' for state-of-the-art IPM-tLTRE analyses and provide an inspirational example for the kind of ecological modelling workflow that can be invaluable not just as a starting point for fellow ecologists picking up or improving their own IPM-tLTRE analyses, but also for teaching and in contexts where model estimates are used for informing management and conservation decision-making.
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Affiliation(s)
- Chloé R Nater
- Norwegian Institute of Nature Research (NINA), Trondheim, Norway
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Dertien JS, Baldwin RF. Does scale or method matter for conservation? Application of directional and omnidirectional connectivity models in spatial prioritizations. FRONTIERS IN CONSERVATION SCIENCE 2023. [DOI: 10.3389/fcosc.2023.976914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023] Open
Abstract
IntroductionThe maintenance of habitat connections between fragmented habitat patches is vital for the conservation of wildlife populations and ecosystem functioning. The awareness of connectivity issues for species conservation has resulted in a growth of connectivity modeling and the application of these results in conservation planning. Such connectivity modeling efforts can include several decisions or data limitations, which could influence the connectivity results and ultimately a systematic conservation plan (SCP). However, there has been little investigation of how these different decisions on species, scale, and extent influence the ultimate conservation planning outcomes.MethodsWe modeled the connectivity of northern bobwhite (Colinus virginianus), North American river otter (Lontra canadensis), and black bear (Ursus americanus), three species with varying ecological requirements, through the Congaree Biosphere Region, South Carolina, USA. We modeled habitat suitability for each species using boosted regression trees and converted these results into resistance layers for the connectivity analyses. We compared models for each species at multistate regional and local extents using directional and omnidirectional circuit theory approaches. We then used the results from each modeling combination as conservation goals for three different SCPs to determine how connectivity modeling decisions may influence geographic conservation decisions.ResultsThere was substantial positive spatial correlation between the three connectivity models of each species, and there appeared to be general agreement among mammals as to most important primary corridors. Across all species, the greatest agreement was between the omnidirectional and local directional models as compared with the regional directional plan, which highlighted a unique corridor. The omnidirectional conservation plan required the least amount of planning units to achieve its conservation goals, followed by the local and then regional directional plans that required over 200 km2 more land area to be conserved.DiscussionOur results indicate that overall variations in connectivity modeling decisions may have only a moderate impact on the identification of important movement corridors for conservation at the local scale. Practitioners should base modeling decisions on the ecology of the study region, conservation question, and available computing resource.
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Merkle JA, Gage J, Sawyer H, Lowrey B, Kauffman MJ. Migration Mapper: Identifying movement corridors and seasonal ranges for large mammal conservation. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Jerod A. Merkle
- Department of Zoology and Physiology University of Wyoming Laramie WY USA
| | | | - Hall Sawyer
- Western Ecosystems Technology, Inc. Laramie WY USA
| | - Blake Lowrey
- Wyoming Cooperative Fish and Wildlife Research Unit, Department of Zoology and Physiology University of Wyoming Laramie WY USA
| | - Matthew J. Kauffman
- U.S. Geological Survey, Wyoming Cooperative Fish and Wildlife Research Unit, Department of Zoology and Physiology University of Wyoming Laramie WY USA
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Bergen S, Huso MM, Duerr AE, Braham MA, Katzner TE, Schmuecker S, Miller TA. Classifying behavior from short-interval biologging data: An example with GPS tracking of birds. Ecol Evol 2022; 12:e08395. [PMID: 35154643 PMCID: PMC8819645 DOI: 10.1002/ece3.8395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 10/28/2021] [Accepted: 11/03/2021] [Indexed: 11/17/2022] Open
Abstract
Recent advances in digital data collection have spurred accumulation of immense quantities of data that have potential to lead to remarkable ecological insight, but that also present analytic challenges. In the case of biologging data from birds, common analytical approaches to classifying movement behaviors are largely inappropriate for these massive data sets.We apply a framework for using K-means clustering to classify bird behavior using points from short time interval GPS tracks. K-means clustering is a well-known and computationally efficient statistical tool that has been used in animal movement studies primarily for clustering segments of consecutive points. To illustrate the utility of our approach, we apply K-means clustering to six focal variables derived from GPS data collected at 1-11 s intervals from free-flying bald eagles (Haliaeetus leucocephalus) throughout the state of Iowa, USA. We illustrate how these data can be used to identify behaviors and life-stage- and age-related variation in behavior.After filtering for data quality, the K-means algorithm identified four clusters in >2 million GPS telemetry data points. These four clusters corresponded to three movement states: ascending, flapping, and gliding flight; and one non-moving state: perching. Mapping these states illustrated how they corresponded tightly to expectations derived from natural history observations; for example, long periods of ascending flight were often followed by long gliding descents, birds alternated between flapping and gliding flight.The K-means clustering approach we applied is both an efficient and effective mechanism to classify and interpret short-interval biologging data to understand movement behaviors. Furthermore, because it can apply to an abundance of very short, irregular, and high-dimensional movement data, it provides insight into small-scale variation in behavior that would not be possible with many other analytical approaches.
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Affiliation(s)
- Silas Bergen
- Department of Mathematics and StatisticsWinona State UniversityWinonaMinnesotaUSA
| | - Manuela M. Huso
- U.S. Geological SurveyForest and Rangeland Ecosystem Science CenterCorvallisOregonUSA
- Statistics DepartmentOregon State UniversityCorvallisOregonUSA
| | - Adam E. Duerr
- Bloom Research Inc.Los AngelesCaliforniaUSA
- West Virginia UniversityMorgantownWest VirginiaUSA
- Conservation Science Global, Inc.West Cape MayNew JerseyUSA
| | | | - Todd E. Katzner
- U.S. Geological SurveyForest and Rangeland Ecosystem Science CenterBoiseIdahoUSA
| | - Sara Schmuecker
- U.S. Fish and Wildlife ServiceIllinois‐Iowa Field OfficeMolineIllinoisUSA
| | - Tricia A. Miller
- West Virginia UniversityMorgantownWest VirginiaUSA
- Conservation Science Global, Inc.West Cape MayNew JerseyUSA
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6
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Gupte PR, Beardsworth CE, Spiegel O, Lourie E, Toledo S, Nathan R, Bijleveld AI. A guide to pre-processing high-throughput animal tracking data. J Anim Ecol 2022; 91:287-307. [PMID: 34657296 PMCID: PMC9299236 DOI: 10.1111/1365-2656.13610] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 10/14/2021] [Indexed: 11/29/2022]
Abstract
Modern, high-throughput animal tracking increasingly yields 'big data' at very fine temporal scales. At these scales, location error can exceed the animal's step size, leading to mis-estimation of behaviours inferred from movement. 'Cleaning' the data to reduce location errors is one of the main ways to deal with position uncertainty. Although data cleaning is widely recommended, inclusive, uniform guidance on this crucial step, and on how to organise the cleaning of massive datasets, is relatively scarce. A pipeline for cleaning massive high-throughput datasets must balance ease of use and computationally efficiency, in which location errors are rejected while preserving valid animal movements. Another useful feature of a pre-processing pipeline is efficiently segmenting and clustering location data for statistical methods while also being scalable to large datasets and robust to imperfect sampling. Manual methods being prohibitively time-consuming, and to boost reproducibility, pre-processing pipelines must be automated. We provide guidance on building pipelines for pre-processing high-throughput animal tracking data to prepare it for subsequent analyses. We apply our proposed pipeline to simulated movement data with location errors, and also show how large volumes of cleaned data can be transformed into biologically meaningful 'residence patches', for exploratory inference on animal space use. We use tracking data from the Wadden Sea ATLAS system (WATLAS) to show how pre-processing improves its quality, and to verify the usefulness of the residence patch method. Finally, with tracks from Egyptian fruit bats Rousettus aegyptiacus, we demonstrate the pre-processing pipeline and residence patch method in a fully worked out example. To help with fast implementation of standardised methods, we developed the R package atlastools, which we also introduce here. Our pre-processing pipeline and atlastools can be used with any high-throughput animal movement data in which the high data-volume combined with knowledge of the tracked individuals' movement capacity can be used to reduce location errors. atlastools is easy to use for beginners while providing a template for further development. The common use of simple yet robust pre-processing steps promotes standardised methods in the field of movement ecology and leads to better inferences from data.
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Affiliation(s)
- Pratik Rajan Gupte
- Groningen Institute for Evolutionary Life SciencesUniversity of GroningenGroningenThe Netherlands
- Department of Coastal SystemsNIOZ Royal Netherlands Institute for Sea ResearchDen BurgThe Netherlands
| | - Christine E. Beardsworth
- Department of Coastal SystemsNIOZ Royal Netherlands Institute for Sea ResearchDen BurgThe Netherlands
| | - Orr Spiegel
- School of ZoologyFaculty of Life SciencesTel Aviv UniversityTel AvivIsrael
- Minerva Center for Movement EcologyThe Hebrew University of JerusalemJerusalemIsrael
| | - Emmanuel Lourie
- Minerva Center for Movement EcologyThe Hebrew University of JerusalemJerusalemIsrael
- Movement Ecology LabDepartment of Ecology, Evolution, and BehaviorAlexander Silberman Institute of Life SciencesThe Hebrew University of JerusalemJerusalemIsrael
| | - Sivan Toledo
- Minerva Center for Movement EcologyThe Hebrew University of JerusalemJerusalemIsrael
- Blavatnik School of Computer ScienceTel Aviv UniversityTel AvivIsrael
| | - Ran Nathan
- Minerva Center for Movement EcologyThe Hebrew University of JerusalemJerusalemIsrael
- Movement Ecology LabDepartment of Ecology, Evolution, and BehaviorAlexander Silberman Institute of Life SciencesThe Hebrew University of JerusalemJerusalemIsrael
| | - Allert I. Bijleveld
- Department of Coastal SystemsNIOZ Royal Netherlands Institute for Sea ResearchDen BurgThe Netherlands
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Wild TA, Wikelski M, Tyndel S, Alarcón‐Nieto G, Klump BC, Aplin LM, Meboldt M, Williams HJ. Internet on animals: Wi‐Fi‐enabled devices provide a solution for big data transmission in biologging. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13798] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
- Timm A. Wild
- Department of Migration Max Planck Institute of Animal Behavior Radolfzell Germany
- Department of Biology University of Konstanz Konstanz Germany
- Product Development Group Zurich (pd z) ETH Zürich Zürich Switzerland
| | - Martin Wikelski
- Department of Migration Max Planck Institute of Animal Behavior Radolfzell Germany
- Department of Biology University of Konstanz Konstanz Germany
- Centre for the Advanced Study of Collective Behaviour University of Konstanz Konstanz Germany
| | - Stephen Tyndel
- Cognitive and Cultural Ecology Research Group Max Planck Institute of Animal Behavior Radolfzell Germany
| | - Gustavo Alarcón‐Nieto
- Cognitive and Cultural Ecology Research Group Max Planck Institute of Animal Behavior Radolfzell Germany
| | - Barbara C. Klump
- Cognitive and Cultural Ecology Research Group Max Planck Institute of Animal Behavior Radolfzell Germany
| | - Lucy M. Aplin
- Centre for the Advanced Study of Collective Behaviour University of Konstanz Konstanz Germany
- Cognitive and Cultural Ecology Research Group Max Planck Institute of Animal Behavior Radolfzell Germany
| | - Mirko Meboldt
- Product Development Group Zurich (pd z) ETH Zürich Zürich Switzerland
| | - Hannah J. Williams
- Department of Migration Max Planck Institute of Animal Behavior Radolfzell Germany
- Department of Biology University of Konstanz Konstanz Germany
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Emery NC, Crispo E, Supp SR, Farrell KJ, Kerkhoff AJ, Bledsoe EK, O'Donnell KL, McCall AC, Aiello-Lammens ME. Data Science in Undergraduate Life Science Education: A Need for Instructor Skills Training. Bioscience 2021; 71:1274-1287. [PMID: 34867087 PMCID: PMC8634500 DOI: 10.1093/biosci/biab107] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
There is a clear demand for quantitative literacy in the life sciences, necessitating competent instructors in higher education. However, not all instructors are versed in data science skills or research-based teaching practices. We surveyed biological and environmental science instructors (n = 106) about the teaching of data science in higher education, identifying instructor needs and illuminating barriers to instruction. Our results indicate that instructors use, teach, and view data management, analysis, and visualization as important data science skills. Coding, modeling, and reproducibility were less valued by the instructors, although this differed according to institution type and career stage. The greatest barriers were instructor and student background and space in the curriculum. The instructors were most interested in training on how to teach coding and data analysis. Our study provides an important window into how data science is taught in higher education biology programs and how we can best move forward to empower instructors across disciplines.
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Affiliation(s)
- Nathan C Emery
- Michigan State University, East Lansing, Michigan, United States
| | - Erika Crispo
- Pace University, New York City, New York, United States
| | | | | | | | - Ellen K Bledsoe
- University of Regina with CIEE's Living Data Project, Regina, Saskatchewan, Canada
| | | | | | - Matthew E Aiello-Lammens
- Environmental Studies and Science Department and director of the Environmental Science Graduate Program at Pace University, New York City, New York, United States
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9
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Bissonette JA. Big Data, Exploratory Data Analyses and Questionable Research Practices: Suggestion for a Foundational Principle. WILDLIFE SOC B 2021. [DOI: 10.1002/wsb.1201] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- J. A. Bissonette
- Department of Wildland Resources, Quinney College of Natural Resources Utah State University Logan UT 84341‐5200 USA
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10
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Harrison XA. A brief introduction to the analysis of time-series data from biologging studies. Philos Trans R Soc Lond B Biol Sci 2021; 376:20200227. [PMID: 34176325 PMCID: PMC8237163 DOI: 10.1098/rstb.2020.0227] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/29/2021] [Indexed: 12/24/2022] Open
Abstract
Recent advances in tagging and biologging technology have yielded unprecedented insights into wild animal physiology. However, time-series data from such wild tracking studies present numerous analytical challenges owing to their unique nature, often exhibiting strong autocorrelation within and among samples, low samples sizes and complicated random effect structures. Gleaning robust quantitative estimates from these physiological data, and, therefore, accurate insights into the life histories of the animals they pertain to, requires careful and thoughtful application of existing statistical tools. Using a combination of both simulated and real datasets, I highlight the key pitfalls associated with analysing physiological data from wild monitoring studies, and investigate issues of optimal study design, statistical power, and model precision and accuracy. I also recommend best practice approaches for dealing with their inherent limitations. This work will provide a concise, accessible roadmap for researchers looking to maximize the yield of information from complex and hard-won biologging datasets. This article is part of the theme issue 'Measuring physiology in free-living animals (Part II)'.
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Affiliation(s)
- Xavier A. Harrison
- Centre for Ecology and Conservation, University of Exeter, Penryn TR10 9FE, UK
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11
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Signer J, Fieberg JR. A fresh look at an old concept: home-range estimation in a tidy world. PeerJ 2021; 9:e11031. [PMID: 33954027 PMCID: PMC8048401 DOI: 10.7717/peerj.11031] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 02/09/2021] [Indexed: 11/27/2022] Open
Abstract
A rich set of statistical techniques has been developed over the last several decades to estimate the spatial extent of animal home ranges from telemetry data, and new methods to estimate home ranges continue to be developed. Here we investigate home-range estimation from a computational point of view and aim to provide a general framework for computing home ranges, independent of specific estimators. We show how such a workflow can help to make home-range estimation easier and more intuitive, and we provide a series of examples illustrating how different estimators can be compared easily. This allows one to perform a sensitivity analysis to determine the degree to which the choice of estimator influences qualitative and quantitative conclusions. By providing a standardized implementation of home-range estimators, we hope to equip researchers with the tools needed to explore how estimator choice influences answers to biologically meaningful questions.
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Affiliation(s)
- Johannes Signer
- Wildlife Sciences, Faculty of Forestry and Forest Ecology, University of Goettingen, Göttingen, Germany
| | - John R. Fieberg
- Department of Fisheries, Wildlife, and Conservation Biology, University of Minnesota, St. Paul, MN, USA
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12
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The Use of Animal-Borne Biologging and Telemetry Data to Quantify Spatial Overlap of Wildlife with Marine Renewables. JOURNAL OF MARINE SCIENCE AND ENGINEERING 2021. [DOI: 10.3390/jmse9030263] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The growth of the marine renewable energy sector requires the potential effects on marine wildlife to be considered carefully. For this purpose, utilization distributions derived from animal-borne biologging and telemetry data provide accurate information on individual space use. The degree of spatial overlap between potentially vulnerable wildlife such as seabirds and development areas can subsequently be quantified and incorporated into impact assessments and siting decisions. While rich in information, processing and analyses of animal-borne tracking data are often not trivial. There is therefore a need for straightforward and reproducible workflows for this technique to be useful to marine renewables stakeholders. The aim of this study was to develop an analysis workflow to extract utilization distributions from animal-borne biologging and telemetry data explicitly for use in assessment of animal spatial overlap with marine renewable energy development areas. We applied the method to European shags (Phalacrocorax aristotelis) in relation to tidal stream turbines. While shag occurrence in the tidal development area was high (99.4%), there was no overlap (0.14%) with the smaller tidal lease sites within the development area. The method can be applied to any animal-borne bio-tracking datasets and is relevant to stakeholders aiming to quantify environmental effects of marine renewables.
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Conners MG, Michelot T, Heywood EI, Orben RA, Phillips RA, Vyssotski AL, Shaffer SA, Thorne LH. Hidden Markov models identify major movement modes in accelerometer and magnetometer data from four albatross species. MOVEMENT ECOLOGY 2021; 9:7. [PMID: 33618773 PMCID: PMC7901071 DOI: 10.1186/s40462-021-00243-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 02/03/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Inertial measurement units (IMUs) with high-resolution sensors such as accelerometers are now used extensively to study fine-scale behavior in a wide range of marine and terrestrial animals. Robust and practical methods are required for the computationally-demanding analysis of the resulting large datasets, particularly for automating classification routines that construct behavioral time series and time-activity budgets. Magnetometers are used increasingly to study behavior, but it is not clear how these sensors contribute to the accuracy of behavioral classification methods. Development of effective classification methodology is key to understanding energetic and life-history implications of foraging and other behaviors. METHODS We deployed accelerometers and magnetometers on four species of free-ranging albatrosses and evaluated the ability of unsupervised hidden Markov models (HMMs) to identify three major modalities in their behavior: 'flapping flight', 'soaring flight', and 'on-water'. The relative contribution of each sensor to classification accuracy was measured by comparing HMM-inferred states with expert classifications identified from stereotypic patterns observed in sensor data. RESULTS HMMs provided a flexible and easily interpretable means of classifying behavior from sensor data. Model accuracy was high overall (92%), but varied across behavioral states (87.6, 93.1 and 91.7% for 'flapping flight', 'soaring flight' and 'on-water', respectively). Models built on accelerometer data alone were as accurate as those that also included magnetometer data; however, the latter were useful for investigating slow and periodic behaviors such as dynamic soaring at a fine scale. CONCLUSIONS The use of IMUs in behavioral studies produces large data sets, necessitating the development of computationally-efficient methods to automate behavioral classification in order to synthesize and interpret underlying patterns. HMMs provide an accessible and robust framework for analyzing complex IMU datasets and comparing behavioral variation among taxa across habitats, time and space.
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Affiliation(s)
- Melinda G Conners
- School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, 11794, USA.
| | - Théo Michelot
- Centre for Research into Ecological and Environmental Modelling, University of St Andrews, St Andrews, KY169LZ, UK
| | - Eleanor I Heywood
- School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Rachael A Orben
- Department of Fisheries and Wildlife, Oregon State University, Hatfield Marine Science Center, 2030 SE Marine Science Dr., Newport, OR, 97365, USA
| | - Richard A Phillips
- British Antarctic Survey, Natural Environment Research Council, High Cross, Madingley Road, Cambridge, CB3 0ET, UK
| | - Alexei L Vyssotski
- Institute of Neuroinformatics, University of Zurich and Swiss Federal Institute of Technology (ETH), 8057, Zurich, Switzerland
| | - Scott A Shaffer
- Department of Biological Sciences, San Jose State University, San Jose, CA, 95192-0100, USA
| | - Lesley H Thorne
- School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, 11794, USA
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14
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Incorporating RDA Outputs in the Design of a European Research Infrastructure for Natural Science Collections. DATA SCIENCE JOURNAL 2020. [DOI: 10.5334/dsj-2020-050] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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15
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Webber QM, Schneider DC, Vander Wal E. Is less more? A commentary on the practice of ‘metric hacking’ in animal social network analysis. Anim Behav 2020. [DOI: 10.1016/j.anbehav.2020.08.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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16
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Pan SL, Li M, Pee L, Sandeep M. Sustainability Design Principles for a Wildlife Management Analytics System: An Action Design Research. EUR J INFORM SYST 2020. [DOI: 10.1080/0960085x.2020.1811786] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Shan L. Pan
- School of Information Systems and Technology Management, The University of New South Wales , Sydney, Australia
| | - Mingwei Li
- Qingdao University, Business Shool , Qingdao, China
| | - L.G. Pee
- Wee Kim Wee School of Communication and Information, Nanyang Technological University , Singapore, Singapore
| | - M.S. Sandeep
- School of Information Systems and Technology Management, The University of New South Wales , Sydney, Australia
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17
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Archmiller AA, Johnson AD, Nolan J, Edwards M, Elliott LH, Ferguson JM, Iannarilli F, Vélez J, Vitense K, Johnson DH, Fieberg J. Computational Reproducibility in The Wildlife Society's Flagship Journals. J Wildl Manage 2020. [DOI: 10.1002/jwmg.21855] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
| | - Andrew D. Johnson
- Biology Department Concordia College 901 8th St S Moorhead MN 56562 USA
| | - Jane Nolan
- Concordia College 901 8th St S Moorhead MN 56562 USA
| | - Margaret Edwards
- Department of Fisheries, Wildlife and Conservation Biology University of Minnesota 2003 Upper Buford Circle, Suite 135 Saint Paul MN 55108 USA
| | - Lisa H. Elliott
- Department of Fisheries, Wildlife and Conservation Biology University of Minnesota 2003 Upper Buford Circle, Suite 135 Saint Paul MN 55108 USA
| | - Jake M. Ferguson
- Department of Biology University of Hawaiʻi at Mānoa 2538 McCarthy Mall Honolulu HI 96822 USA
| | - Fabiola Iannarilli
- Department of Fisheries, Wildlife and Conservation Biology University of Minnesota 2003 Upper Buford Circle, Suite 135 Saint Paul MN 55108 USA
| | - Juliana Vélez
- Department of Fisheries, Wildlife and Conservation Biology University of Minnesota 2003 Upper Buford Circle, Suite 135 Saint Paul MN 55108 USA
| | - Kelsey Vitense
- Department of Fisheries, Wildlife and Conservation Biology University of Minnesota 2003 Upper Buford Circle, Suite 135 Saint Paul MN 55108 USA
| | - Douglas H. Johnson
- Department of Fisheries, Wildlife and Conservation Biology University of Minnesota 2003 Upper Buford Circle, Suite 135 Saint Paul MN 55108 USA
| | - John Fieberg
- Department of Fisheries, Wildlife and Conservation Biology University of Minnesota 2003 Upper Buford Circle, Suite 135 Saint Paul MN 55108 USA
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Hackett RA, Belitz MW, Gilbert EE, Monfils AK. A data management workflow of biodiversity data from the field to data users. APPLICATIONS IN PLANT SCIENCES 2019; 7:e11310. [PMID: 31890356 PMCID: PMC6923704 DOI: 10.1002/aps3.11310] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2019] [Accepted: 10/21/2019] [Indexed: 06/10/2023]
Abstract
PREMISE Heterogeneity of biodiversity data from the collections, research, and management communities presents challenges for data findability, accessibility, interoperability, and reusability. Workflows designed with data collection, standards, dissemination, and reuse in mind will generate better information across geopolitical, administrative, and institutional boundaries. Here, we present our data workflow as a case study of how we collected, shared, and used data from multiple sources. METHODS In 2012, we initiated the collection of biodiversity data relating to Michigan prairie fens, including data on plant communities and the federally endangered Poweshiek skipperling (Oarisma poweshiek). RESULTS Over 23,000 occurrence records were compiled in a database following Darwin Core standards. The records were linked with media and biological, chemical, and geometric measurements. We published the data as Global Biodiversity Information Facility data sets and in Symbiota SEINet portals. DISCUSSION We highlight data collection techniques that optimized transcription time, including the use of predetermined and controlled vocabulary, Darwin Core terms, and data dictionaries. The validity and longevity of our data were supported by voucher specimens, metadata with measurement records, and published manuscripts detailing methods and data sets. Key to our data dissemination was cooperation among partners and the utilization of dynamic tools. To increase data interoperability, we need flexible and customizable data collection templates, coding, and enhanced communication among communities using biodiversity data.
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Affiliation(s)
- Rachel A. Hackett
- Department of BiologyInstitute for Great Lakes ResearchCentral Michigan UniversityBioscience Building 2100, 1455 Calumet CourtMount PleasantMichigan48859USA
- Michigan Natural Features InventoryMichigan State University ExtensionP.O. Box 13036LansingMichigan48901‐3036USA
| | - Michael W. Belitz
- Department of BiologyInstitute for Great Lakes ResearchCentral Michigan UniversityBioscience Building 2100, 1455 Calumet CourtMount PleasantMichigan48859USA
- Florida Museum of Natural HistoryUniversity of FloridaGainesvilleFlorida32611USA
| | | | - Anna K. Monfils
- Department of BiologyInstitute for Great Lakes ResearchCentral Michigan UniversityBioscience Building 2100, 1455 Calumet CourtMount PleasantMichigan48859USA
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Williams HJ, Taylor LA, Benhamou S, Bijleveld AI, Clay TA, de Grissac S, Demšar U, English HM, Franconi N, Gómez-Laich A, Griffiths RC, Kay WP, Morales JM, Potts JR, Rogerson KF, Rutz C, Spelt A, Trevail AM, Wilson RP, Börger L. Optimizing the use of biologgers for movement ecology research. J Anim Ecol 2019; 89:186-206. [PMID: 31424571 DOI: 10.1111/1365-2656.13094] [Citation(s) in RCA: 99] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2018] [Accepted: 08/08/2019] [Indexed: 10/26/2022]
Abstract
The paradigm-changing opportunities of biologging sensors for ecological research, especially movement ecology, are vast, but the crucial questions of how best to match the most appropriate sensors and sensor combinations to specific biological questions and how to analyse complex biologging data, are mostly ignored. Here, we fill this gap by reviewing how to optimize the use of biologging techniques to answer questions in movement ecology and synthesize this into an Integrated Biologging Framework (IBF). We highlight that multisensor approaches are a new frontier in biologging, while identifying current limitations and avenues for future development in sensor technology. We focus on the importance of efficient data exploration, and more advanced multidimensional visualization methods, combined with appropriate archiving and sharing approaches, to tackle the big data issues presented by biologging. We also discuss the challenges and opportunities in matching the peculiarities of specific sensor data to the statistical models used, highlighting at the same time the large advances which will be required in the latter to properly analyse biologging data. Taking advantage of the biologging revolution will require a large improvement in the theoretical and mathematical foundations of movement ecology, to include the rich set of high-frequency multivariate data, which greatly expand the fundamentally limited and coarse data that could be collected using location-only technology such as GPS. Equally important will be the establishment of multidisciplinary collaborations to catalyse the opportunities offered by current and future biologging technology. If this is achieved, clear potential exists for developing a vastly improved mechanistic understanding of animal movements and their roles in ecological processes and for building realistic predictive models.
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Affiliation(s)
- Hannah J Williams
- Department of Biosciences, College of Science, Swansea University, Swansea, UK
| | - Lucy A Taylor
- Save the Elephants, Nairobi, Kenya.,Department of Zoology, University of Oxford, Oxford, UK
| | - Simon Benhamou
- Centre d'Ecologie Fonctionnelle et Evolutive, CNRS Montpellier, Montpellier, France
| | - Allert I Bijleveld
- NIOZ Royal Netherlands Institute for Sea Research, Department of Coastal Systems, Utrecht University, Den Burg, The Netherlands
| | - Thomas A Clay
- School of Environmental Sciences, University of Liverpool, Liverpool, UK
| | - Sophie de Grissac
- Department of Biosciences, College of Science, Swansea University, Swansea, UK
| | - Urška Demšar
- School of Geography & Sustainable Development, University of St Andrews, St Andrews, UK
| | - Holly M English
- Department of Biosciences, College of Science, Swansea University, Swansea, UK
| | - Novella Franconi
- Department of Biosciences, College of Science, Swansea University, Swansea, UK
| | - Agustina Gómez-Laich
- Instituto de Biología de Organismos Marinos (IBIOMAR), CONICET, Puerto Madryn, Chubut, Argentina
| | - Rachael C Griffiths
- Department of Biosciences, College of Science, Swansea University, Swansea, UK
| | - William P Kay
- Department of Biosciences, College of Science, Swansea University, Swansea, UK
| | - Juan Manuel Morales
- Grupo de Ecología Cuantitativa, INIBIOMA-Universidad Nacional del Comahue, CONICET, Bariloche, Argentina
| | - Jonathan R Potts
- School of Mathematics and Statistics, University of Sheffield, Sheffield, UK
| | | | - Christian Rutz
- Centre for Biological Diversity, School of Biology, University of St Andrews, St Andrews, UK
| | - Anouk Spelt
- Department of Aerospace Engineering, University of Bristol, University Walk, UK
| | - Alice M Trevail
- School of Environmental Sciences, University of Liverpool, Liverpool, UK
| | - Rory P Wilson
- Department of Biosciences, College of Science, Swansea University, Swansea, UK
| | - Luca Börger
- Department of Biosciences, College of Science, Swansea University, Swansea, UK
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Eacker DR, Hebblewhite M, Steenweg R, Russell M, Flasko A, Hervieux D. Web-based application for threatened woodland caribou population modeling. WILDLIFE SOC B 2019; 43:167-177. [PMID: 31007303 PMCID: PMC6472330 DOI: 10.1002/wsb.950] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Accepted: 12/28/2018] [Indexed: 11/19/2022]
Abstract
Woodland caribou (Rangifer tarandus caribou) are threatened in Canada, with population and distribution declines evident in most regions of the country. Causes of declines are linked to landscape change from forest fires and human development, notably forestry oil and gas activities, which result in caribou habitat loss and affect ecosystem food webs. The Federal Species at Risk Act requires effective protection and restoration of caribou habitat, with actions to increase caribou survival. These requirements call for effective monitoring of caribou population trends to gauge success. Many woodland caribou populations are nearly impossible to count using traditional aerial survey methods, but demographic‐based monitoring approaches can be used to estimate population trends based on population modeling of vital rates from marked animals. Monitoring programs have used a well‐known simple population model (the Recruitment‐Mortality [R/M] equation) to estimate demographic rates for woodland caribou, but have faced challenges in managing large data streams and providing transparency in the demographic estimation process. We present a stand‐alone statistical software application using open‐source software to permit efficient, transparent, and replicable demographic estimation for woodland caribou populations. We developed an easy‐to‐use, interactive web‐based application for the R/M population model that uses a Bayesian estimation approach and provides the user flexibility in choice of prior distributions and other output features. We illustrate the web‐application to the A la Pêche Southern Mountain (Central Group) woodland caribou population in west‐central Alberta, Canada, during 1998–2017. Our estimates of population demographics are consistent with previous research on this population and highlight the utility of the application in assessing caribou population responses to species recovery actions. We provide example data, computer code, the web‐based application package, and a user manual to guide installation and use. We also review underlying assumptions and challenges of population monitoring in this case study. We expect our software will contribute to efficient monitoring of woodland caribou and help in the assessment of recovery actions for this species. © 2019 The Authors. Wildlife Society Bulletin Published by Wiley Periodicals, Inc. We developed a stand‐alone Web‐Application to support population trend estimation for endangered and threatened woodland caribou populations.
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Affiliation(s)
- Daniel R Eacker
- Wildlife Biology Program Department of Ecosystem and Conservation Sciences W. A. Franke College of Forestry and Conservation University of Montana Missoula MT 59812 USA
| | - Mark Hebblewhite
- Wildlife Biology Program Department of Ecosystem and Conservation Sciences W. A. Franke College of Forestry and Conservation University of Montana Missoula MT 59812 USA
| | - Robin Steenweg
- Alberta Environment and Parks - Operations Division 1601 Provincial Building, 10320-99 Street Grande Prairie AB T8V 6J4 Canada
| | - Mike Russell
- Alberta Environment and Parks - Operations Division 1601 Provincial Building, 10320-99 Street Grande Prairie AB T8V 6J4 Canada
| | - Amy Flasko
- Alberta Environment and Parks - Policy Division 1601 Provincial Building, 10320-99 Street Grande Prairie AB T8V 6J4 Canada
| | - Dave Hervieux
- Alberta Environment and Parks - Operations Division 1601 Provincial Building, 10320-99 Street Grande Prairie AB T8V 6J4 Canada
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Webber QM, Vander Wal E. Trends and perspectives on the use of animal social network analysis in behavioural ecology: a bibliometric approach. Anim Behav 2019. [DOI: 10.1016/j.anbehav.2019.01.010] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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Signer J, Fieberg J, Avgar T. Animal movement tools (amt): R package for managing tracking data and conducting habitat selection analyses. Ecol Evol 2019; 9:880-890. [PMID: 30766677 PMCID: PMC6362447 DOI: 10.1002/ece3.4823] [Citation(s) in RCA: 191] [Impact Index Per Article: 38.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Accepted: 07/20/2018] [Indexed: 11/07/2022] Open
Abstract
Advances in tracking technology have led to an exponential increase in animal location data, greatly enhancing our ability to address interesting questions in movement ecology, but also presenting new challenges related to data management and analysis. Step-selection functions (SSFs) are commonly used to link environmental covariates to animal location data collected at fine temporal resolution. SSFs are estimated by comparing observed steps connecting successive animal locations to random steps, using a likelihood equivalent of a Cox proportional hazards model. By using common statistical distributions to model step length and turn angle distributions, and including habitat- and movement-related covariates (functions of distances between points, angular deviations), it is possible to make inference regarding habitat selection and movement processes or to control one process while investigating the other. The fitted model can also be used to estimate utilization distributions and mechanistic home ranges. Here, we present the R package amt (animal movement tools) that allows users to fit SSFs to data and to simulate space use of animals from fitted models. The amt package also provides tools for managing telemetry data. Using fisher (Pekania pennanti) data as a case study, we illustrate a four-step approach to the analysis of animal movement data, consisting of data management, exploratory data analysis, fitting of models, and simulating from fitted models.
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Affiliation(s)
| | - John Fieberg
- Department of Fisheries, Wildlife and Conservation BiologyUniversity of MinnesotaSt. PaulMinnesota
| | - Tal Avgar
- Department of Integrative BiologyUniversity of GuelphGuelphOntarioCanada
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Thomson R, Potgieter GC, Bahaa-el-din L. Closing the gap between camera trap software development and the user community. Afr J Ecol 2018. [DOI: 10.1111/aje.12550] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
| | | | - Laila Bahaa-el-din
- School of Life Sciences, University of KwaZulu-Natal; Durban South Africa
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