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Geary WL, Tulloch AIT, Ritchie EG, Doherty TS, Nimmo DG, Maxwell MA, Wayne AF. Identifying historical and future global change drivers that place species recovery at risk. GLOBAL CHANGE BIOLOGY 2023; 29:2953-2967. [PMID: 36864646 DOI: 10.1111/gcb.16661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 11/28/2022] [Indexed: 05/03/2023]
Abstract
Ecosystem management in the face of global change requires understanding how co-occurring threats affect species and communities. Such an understanding allows for effective management strategies to be identified and implemented. An important component of this is differentiating between factors that are within (e.g. invasive predators) or outside (e.g. drought, large wildfires) of a local manager's control. In the global biodiversity hotspot of south-western Australia, small- and medium-sized mammal species are severely affected by anthropogenic threats and environmental disturbances, including invasive predators, fire, and declining rainfall. However, the relative importance of different drivers has not been quantified. We used data from a long-term monitoring program to fit Bayesian state-space models that estimated spatial and temporal changes in the relative abundance of four threatened mammal species: the woylie (Bettongia penicillata), chuditch (Dasyurus geoffroii), koomal (Trichosurus vulpecula) and quenda (Isoodon fusciventor). We then use Bayesian structural equation modelling to identify the direct and indirect drivers of population changes, and scenario analysis to forecast population responses to future environmental change. We found that habitat loss or conversion and reduced primary productivity (caused by rainfall declines) had greater effects on species' spatial and temporal population change than the range of fire and invasive predator (the red fox Vulpes vulpes) management actions observed in the study area. Scenario analysis revealed that a greater extent of severe fire and further rainfall declines predicted under climate change, operating in concert are likely to further reduce the abundance of these species, but may be mitigated partially by invasive predator control. Considering both historical and future drivers of population change is necessary to identify the factors that risk species recovery. Given that both anthropogenic pressures and environmental disturbances can undermine conservation efforts, managers must consider how the relative benefit of conservation actions will be shaped by ongoing global change.
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Affiliation(s)
- William L Geary
- School of Life and Environmental Sciences (Burwood Campus), Centre for Integrative Ecology, Deakin University, Geelong, Victoria, Australia
- Biodiversity Division, Department of Environment, Land, Water and Planning, East Melbourne, Victoria, Australia
| | - Ayesha I T Tulloch
- School of Life and Environmental Sciences, The University of Sydney, Sydney, New South Wales, Australia
- School of Biology and Environmental Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Euan G Ritchie
- School of Life and Environmental Sciences (Burwood Campus), Centre for Integrative Ecology, Deakin University, Geelong, Victoria, Australia
| | - Tim S Doherty
- School of Life and Environmental Sciences, The University of Sydney, Sydney, New South Wales, Australia
| | - Dale G Nimmo
- Gulbali Institute, School of Agricultural, Environmental and Veterinary Sciences, Charles Sturt University, New South Wales, Albury, Australia
| | - Marika A Maxwell
- Department of Biodiversity, Conservation and Attractions, Manjimup, Western Australia, Australia
| | - Adrian F Wayne
- Department of Biodiversity, Conservation and Attractions, Manjimup, Western Australia, Australia
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Cervantes F, Altwegg R, Strobbe F, Skowno A, Visser V, Brooks M, Stojanov Y, Harebottle DM, Job N. BIRDIE: A data pipeline to inform wetland and waterbird conservation at multiple scales. Front Ecol Evol 2023. [DOI: 10.3389/fevo.2023.1131120] [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
IntroductionEfforts to collect ecological data have intensified over the last decade. This is especially true for freshwater habitats, which are among the most impacted by human activity and yet lagging behind in terms of data availability. Now, to support conservation programmes and management decisions, these data need to be analyzed and interpreted; a process that can be complex and time consuming. The South African Biodiversity Data Pipeline for Wetlands and Waterbirds (BIRDIE) aims to help fast and efficient information uptake, bridging the gap between raw ecological datasets and the information final users need.MethodsBIRDIE is a full data pipeline that takes up raw data, and estimates indicators related to waterbird populations, while keeping track of their associated uncertainty. At present, we focus on the assessment of species abundance and distribution in South Africa using two citizen-science bird monitoring datasets, namely: the African Bird Atlas Project and the Coordinated Waterbird Counts. These data are analyzed with occupancy and state-space models, respectively. In addition, a suite of environmental layers help contextualize waterbird population indicators, and link these to the ecological condition of the supporting wetlands. Both data and estimated indicators are accessible to end users through an online portal and web services.Results and discussionWe have designed a modular system that includes tasks, such as: data cleaning, statistical analysis, diagnostics, and computation of indicators. Envisioned users of BIRDIE include government officials, conservation managers, researchers and the general public, all of whom have been engaged throughout the project. Acknowledging that conservation programmes run at multiple spatial and temporal scales, we have developed a granular framework in which indicators are estimated at small scales, and then these are aggregated to compute similar indicators at broader scales. Thus, the online portal is designed to provide spatial and temporal visualization of the indicators using maps, time series and pre-compiled reports for species, sites and conservation programmes. In the future, we aim to expand the geographical coverage of the pipeline to other African countries, and develop more indicators specific to the ecological structure and function of wetlands.
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Integrated Population Models: Achieving Their Potential. JOURNAL OF STATISTICAL THEORY AND PRACTICE 2023. [DOI: 10.1007/s42519-022-00302-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
AbstractPrecise and accurate estimates of abundance and demographic rates are primary quantities of interest within wildlife conservation and management. Such quantities provide insight into population trends over time and the associated underlying ecological drivers of the systems. This information is fundamental in managing ecosystems, assessing species conservation status and developing and implementing effective conservation policy. Observational monitoring data are typically collected on wildlife populations using an array of different survey protocols, dependent on the primary questions of interest. For each of these survey designs, a range of advanced statistical techniques have been developed which are typically well understood. However, often multiple types of data may exist for the same population under study. Analyzing each data set separately implicitly discards the common information contained in the other data sets. An alternative approach that aims to optimize the shared information contained within multiple data sets is to use a “model-based data integration” approach, or more commonly referred to as an “integrated model.” This integrated modeling approach simultaneously analyzes all the available data within a single, and robust, statistical framework. This paper provides a statistical overview of ecological integrated models, with a focus on integrated population models (IPMs) which include abundance and demographic rates as quantities of interest. Four main challenges within this area are discussed, namely model specification, computational aspects, model assessment and forecasting. This should encourage researchers to explore further and develop new practical tools to ensure that full utility can be made of IPMs for future studies.
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Skelly BP, Clipp HL, Landry SM, Rogers R, Phelps Q, Anderson JT, Rota CT. A flexible Bayesian approach for estimating survival probabilities from age‐at‐harvest data. Methods Ecol Evol 2023. [DOI: 10.1111/2041-210x.14077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Affiliation(s)
- Brett P. Skelly
- Division of Forestry and Natural Resources West Virginia University Morgantown West Virginia USA
- West Virginia Division of Natural Resources Elkins West Virginia USA
| | - Hannah L. Clipp
- Division of Forestry and Natural Resources West Virginia University Morgantown West Virginia USA
| | - Stephanie M. Landry
- Division of Forestry and Natural Resources West Virginia University Morgantown West Virginia USA
- Department of Wildland Resources Utah State University Logan Utah USA
| | - Rich Rogers
- West Virginia Division of Natural Resources Romney West Virginia USA
| | - Quinton Phelps
- Department of Biology Missouri State University Springfield Missouri USA
| | - James T. Anderson
- James C. Kennedy Waterfowl and Wetlands Conservation Center Belle W. Baruch Institute of Coastal Ecology and Forest Science Georgetown South Carolina USA
| | - Christopher T. Rota
- Division of Forestry and Natural Resources West Virginia University Morgantown West Virginia USA
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5
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Estimating population dynamics trajectories of raptors from a multi-species hierarchical distance sampling model. ECOL INFORM 2023. [DOI: 10.1016/j.ecoinf.2023.102024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
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Mutshinda CM, Mishra A, Finkel ZV, Irwin AJ. Density regulation amplifies environmentally induced population fluctuations. PeerJ 2023; 11:e14701. [PMID: 36751641 PMCID: PMC9899430 DOI: 10.7717/peerj.14701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 12/14/2022] [Indexed: 02/05/2023] Open
Abstract
Background Density-dependent regulation is ubiquitous in population dynamics, and its potential interaction with environmental stochasticity complicates the characterization of the random component of population dynamics. Yet, this issue has not received attention commensurate with its relevance for descriptive and predictive modeling of population dynamics. Here we use a Bayesian modeling approach to investigate the contribution of density regulation to population variability in stochastic environments. Methods We analytically derive a formula linking the stationary variance of population abundance/density under Gompertz regulation in a stochastic environment with constant variance to the environmental variance and the strength of density feedback, to investigate whether and how density regulation affects the stationary variance. We examine through simulations whether the relationship between stationary variance and density regulation inferred analytically under the Gompertz model carries over to the Ricker model, widely used in population dynamics modeling. Results The analytical decomposition of the stationary variance under stochastic Gompertz dynamics implies higher variability for strongly regulated populations. Simulation results demonstrate that the pattern of increasing population variability with increasing density feedback found under the Gompertz model holds for the Ricker model as well, and is expected to be a general phenomenon with stochastic population models. We also analytically established and empirically validated that the square of the autoregressive parameter of the Gompertz model in AR(1) form represents the proportion of stationary variance due to density dependence. Discussion Our results suggest that neither environmental stochasticity nor density regulation can alone explain the patterns of population variability in stochastic environments, as these two components of temporal variation interact, with a tendency for density regulation to amplify the magnitude of environmentally induced population fluctuations. This finding has far-reaching implications for population viability. It implies that intense intra-specific resource competition increases the risk of environment-driven population collapse at high density, making opportune harvesting a sensible practice for improving the resistance of managed populations such as fish stocks to environmental perturbations. The separation of density-dependent and density-independent processes will help improve population dynamics modeling, while providing a basis for evaluating the relative importance of these two categories of processes that remains a topic of long-standing controversy among ecologists.
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Affiliation(s)
- Crispin M Mutshinda
- Department of Mathematics & Statistics, Dalhousie University, Halifax, NS, Canada
| | | | - Zoe V Finkel
- Department of Oceanography, Dalhousie University, Halifax, NS, Canada
| | - Andrew J Irwin
- Department of Mathematics & Statistics, Dalhousie University, Halifax, NS, Canada
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Kasada M, Nakashima Y, Fukasawa K, Yajima G, Yokomizo H, Miyashita T. State‐space model combining local camera data and regional administration data reveals population dynamics of wild boar. POPUL ECOL 2022. [DOI: 10.1002/1438-390x.12138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Minoru Kasada
- Graduate School of Life Sciences Tohoku University Sendai Japan
- Department of Experimental Limnology Leibniz‐Institute of Freshwater Ecology and Inland Fisheries Stechlin Germany
| | | | - Keita Fukasawa
- Biodiversity Division National Institute for Environmental Studies Tsukuba Ibaraki Japan
| | - Gota Yajima
- College of Bioresource Science Nihon University Fujisawa Kanagawa Japan
| | - Hiroyuki Yokomizo
- Health and Environmental Risk Division National Institute for Environmental Studies Tsukuba Ibaraki Japan
| | - Tadashi Miyashita
- Graduate School of Agriculture and Life Sciences The University of Tokyo Tokyo Japan
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Mews S, Langrock R, King R, Quick N. Multistate capture–recapture models for irregularly sampled data. Ann Appl Stat 2022. [DOI: 10.1214/21-aoas1528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Sina Mews
- Department of Business Administration and Economics, Bielefeld University
| | - Roland Langrock
- Department of Business Administration and Economics, Bielefeld University
| | - Ruth King
- School of Mathematics, University of Edinburgh
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Newman K, King R, Elvira V, de Valpine P, McCrea RS, Morgan BJT. State‐space Models for Ecological Time Series Data: Practical Model‐fitting. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13833] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Ken Newman
- School of Mathematics University of Edinburgh Edinburgh UK
- Biomathematics and Statistics Scotland Edinburgh UK
| | - Ruth King
- School of Mathematics University of Edinburgh Edinburgh UK
| | - Víctor Elvira
- School of Mathematics University of Edinburgh Edinburgh UK
| | - Perry de Valpine
- Department of Environmental Science, Policy, and Management University of California Berkeley CA USA
| | - Rachel S. McCrea
- School of Mathematics, Statistics and Actuarial Science University of Kent Canterbury UK
| | - Byron J. T. Morgan
- School of Mathematics, Statistics and Actuarial Science University of Kent Canterbury UK
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10
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Zimmerman GS, Millsap BA, Abadi F, Gedir JV, Kendall WL, Sauer JR. Estimating allowable take for an increasing bald eagle population in the United States. J Wildl Manage 2022. [DOI: 10.1002/jwmg.22158] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Guthrie S. Zimmerman
- U.S. Fish and Wildlife Service, Division of Migratory Bird Management 3020 State University Drive East Modoc Hall, Suite 2007 Sacramento CA 95819 USA
| | - Brian A. Millsap
- U.S. Fish and Wildlife Service, Division of Migratory Bird Management 2105 Osuna NE Albuquerque NM 87113 USA
| | - Fitsum Abadi
- Department of Fish Wildlife, and Conservation Ecology, New Mexico State University P. O. Box 30003, MSC 4901 Las Cruces NM 88003 USA
| | - Jay V. Gedir
- Department of Fish Wildlife, and Conservation Ecology, New Mexico State University P. O. Box 30003, MSC 4901 Las Cruces NM 88003 USA
| | - William L. Kendall
- U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit Colorado State University 1484 Campus Delivery Fort Collins CO 80523 USA
| | - John R. Sauer
- U.S. Geological Survey, Eastern Ecological Science Center 12100 Beech Forest Road Laurel MD 20708 USA
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11
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Cusack JJ, Nilsen EB, Israelsen MF, Andrén H, Grainger M, Linnell JDC, Odden J, Bunnefeld N. Quantifying the checks and balances of collaborative governance systems for adaptive carnivore management. J Appl Ecol 2022; 59:1038-1049. [PMID: 35910004 PMCID: PMC9306889 DOI: 10.1111/1365-2664.14113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 12/11/2021] [Indexed: 11/28/2022]
Abstract
Recovering or threatened carnivore populations are often harvested to minimise their impact on human activities, such as livestock farming or game hunting. Increasingly, harvest quota decisions involve a set of scientific, administrative and political institutions operating at national and sub‐national levels whose interactions and collective decision‐making aim to increase the legitimacy of management and ensure population targets are met. In practice, however, assessments of how quota decisions change between these different actors and what consequences these changes have on population trends are rare. We combine a state‐space population modelling approach with an analysis of quota decisions taken at both regional and national levels between 2007 and 2018 to build a set of decision‐making models that together predict annual harvest quota values for Eurasian lynx (Lynx lynx) in Norway. We reveal a tendency for administrative decision‐makers to compensate for consistent quota increases by political actors, particularly when the lynx population size estimate is above the regional target. Using population forecasts based on the ensemble of decision‐making models, we show that such buffering of political biases ensures lynx population size remains close to regional and national targets in the long term. Our results go beyond the usual qualitative assessment of collaborative governance systems for carnivore management, revealing a system of checks and balances that, in the case of lynx in Norway, ensures both multi‐stakeholder participation and sustainable harvest quotas. Nevertheless, we highlight important inter‐regional differences in decision‐making and population forecasts, the socio‐ecological drivers of which need to be better understood to prevent future population declines. Synthesis and applications. Our work analyses the sequence of decisions leading to yearly quotas for lynx harvest in Norway, highlighting the collaborative and structural processes that together shape harvest sustainability. In doing so, we provide a predictive framework to evaluate participatory decision‐making processes in wildlife management, paving the way for scientists and decision‐makers to collaborate more widely in identifying where decision biases might lie and how institutional arrangements can be optimised to minimise them. We emphasise, however, that this is only possible if wildlife management decisions are documented and transparent.
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Affiliation(s)
- Jeremy J. Cusack
- Centro de Modelación y Monitoreo de Ecosistemas Universidad Mayor Chile
- Biological and Environmental Sciences University of Stirling UK
| | | | | | - Henrik Andrén
- Grimsö Wildlife Research Station Department of Ecology, Swedish University of Agricultural Sciences Riddarhyttan Sweden
| | | | | | - John Odden
- Norwegian Institute for Nature Research Oslo Norway
| | - Nils Bunnefeld
- Biological and Environmental Sciences University of Stirling UK
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12
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Auger‐Méthé M, Newman K, Cole D, Empacher F, Gryba R, King AA, Leos‐Barajas V, Mills Flemming J, Nielsen A, Petris G, Thomas L. A guide to state–space modeling of ecological time series. ECOL MONOGR 2021. [DOI: 10.1002/ecm.1470] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Marie Auger‐Méthé
- Department of Statistics University of British Columbia Vancouver British Columbia V6T 1Z4 Canada
- Institute for the Oceans and Fisheries University of British Columbia Vancouver British Columbia V6T 1Z4 Canada
| | - Ken Newman
- Biomathematics and Statistics Scotland Edinburgh EH9 3FD UK
- School of Mathematics University of Edinburgh Edinburgh EH9 3FD UK
| | - Diana Cole
- School of Mathematics, Statistics and Actuarial Science University of Kent Canterbury Kent CT2 7FS UK
| | - Fanny Empacher
- Centre for Research into Ecological and Environmental Modelling University of St Andrews St Andrews KY16 9LZ UK
| | - Rowenna Gryba
- Department of Statistics University of British Columbia Vancouver British Columbia V6T 1Z4 Canada
- Institute for the Oceans and Fisheries University of British Columbia Vancouver British Columbia V6T 1Z4 Canada
| | - Aaron A. King
- Center for the Study of Complex Systems and Departments of Ecology & Evolutionary Biology and Mathematics University of Michigan Ann Arbor Michigan 48109 USA
| | - Vianey Leos‐Barajas
- Department of Statistics University of Toronto Toronto Ontario M5G 1X6 Canada
- School of the Environment University of Toronto Toronto Ontario M5S 3E8 Canada
| | - Joanna Mills Flemming
- Department of Mathematics and Statistics Dalhousie University Halifax Nova Scotia B3H 4R2 Canada
| | - Anders Nielsen
- National Institute for Aquatic Resources Technical University of Denmark Kgs. Lyngby 2800 Denmark
| | - Giovanni Petris
- Department of Mathematical Sciences University of Arkansas Fayetteville Arkansas 72701 USA
| | - Len Thomas
- Centre for Research into Ecological and Environmental Modelling University of St Andrews St Andrews KY16 9LZ UK
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Canonne C, Montadert M, Besnard A. Drivers of black grouse trends in the French Alps: The prevailing contribution of climate. DIVERS DISTRIB 2021. [DOI: 10.1111/ddi.13242] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Affiliation(s)
- Coline Canonne
- DRAS OFB Juvignac France
- EPHE, PSL Research University, CNRS, UM, SupAgro, IRD, INRA Montpellier France
| | | | - Aurélien Besnard
- EPHE, PSL Research University, CNRS, UM, SupAgro, IRD, INRA Montpellier France
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Roberts AJ, Dooley JL, Ross BE, Nichols TC, Leafloor JO, Dufour KW. An Integrated Population Model for Harvest Management of Atlantic Brant. J Wildl Manage 2021. [DOI: 10.1002/jwmg.22037] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Anthony J. Roberts
- U.S. Fish and Wildlife Service, Division of Migratory Bird Management Laurel MD 20708 USA
| | - Joshua L. Dooley
- U.S. Fish and Wildlife Service, Division of Migratory Bird Management Vancouver WA 98683 USA
| | - Beth E. Ross
- U.S. Geological Survey South Carolina Cooperative Fish and Wildlife Research Unit Clemson SC 29634 USA
| | | | | | - Kevin W. Dufour
- Prairie and Northern Wildlife Research Centre, Canadian Wildlife Service Saskatoon Saskatchewan S7N 0X4 Canada
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Paterson JT, Proffitt K, Rotella J, McWhirter D, Garrott R. Drivers of variation in the population dynamics of bighorn sheep. Ecosphere 2021. [DOI: 10.1002/ecs2.3679] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
| | - Kelly Proffitt
- Montana Department of Fish, Wildlife and Parks Bozeman Montana USA
| | - Jay Rotella
- Department of Ecology Montana State University Bozeman Montana USA
| | | | - Robert Garrott
- Department of Ecology Montana State University Bozeman Montana USA
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16
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Conn PB, Chernook VI, Moreland EE, Trukhanova IS, Regehr EV, Vasiliev AN, Wilson RR, Belikov SE, Boveng PL. Aerial survey estimates of polar bears and their tracks in the Chukchi Sea. PLoS One 2021; 16:e0251130. [PMID: 33956835 PMCID: PMC8101751 DOI: 10.1371/journal.pone.0251130] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 04/20/2021] [Indexed: 11/19/2022] Open
Abstract
Polar bears are of international conservation concern due to climate change but are difficult to study because of low densities and an expansive, circumpolar distribution. In a collaborative U.S.-Russian effort in spring of 2016, we used aerial surveys to detect and estimate the abundance of polar bears on sea ice in the Chukchi Sea. Our surveys used a combination of thermal imagery, digital photography, and human observations. Using spatio-temporal statistical models that related bear and track densities to physiographic and biological covariates (e.g., sea ice extent, resource selection functions derived from satellite tags), we predicted abundance and spatial distribution throughout our study area. Estimates of 2016 abundance ([Formula: see text]) ranged from 3,435 (95% CI: 2,300-5,131) to 5,444 (95% CI: 3,636-8,152) depending on the proportion of bears assumed to be missed on the transect line during Russian surveys (g(0)). Our point estimates are larger than, but of similar magnitude to, a recent estimate for the period 2008-2016 ([Formula: see text]; 95% CI 1,522-5,944) derived from an integrated population model applied to a slightly smaller area. Although a number of factors (e.g., equipment issues, differing platforms, low sample sizes, size of the study area relative to sampling effort) required us to make a number of assumptions to generate estimates, it establishes a useful lower bound for abundance, and suggests high spring polar bear densities on sea ice in Russian waters south of Wrangell Island. With future improvements, we suggest that springtime aerial surveys may represent a plausible avenue for studying abundance and distribution of polar bears and their prey over large, remote areas.
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Affiliation(s)
- Paul B. Conn
- Alaska Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, Washington, United States of America
- * E-mail:
| | - Vladimir I. Chernook
- Ecological Center, Autonomous Non-Commercial Organization, Saint-Petersburg, Russia
| | - Erin E. Moreland
- Alaska Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, Washington, United States of America
| | - Irina S. Trukhanova
- North Pacific Wildlife Consulting, LLC, Seattle, Washington, United States of America
| | - Eric V. Regehr
- Marine Mammals Management, United States Fish and Wildlife Service, Anchorage, Alaska, United States of America
- Applied Physics Laboratory, Polar Science Center, University of Washington, Seattle, Washington, United States of America
| | | | - Ryan R. Wilson
- Marine Mammals Management, United States Fish and Wildlife Service, Anchorage, Alaska, United States of America
| | - Stanislav E. Belikov
- All-Russian Research Institute for Nature Protection (Federal State Budgetary Institution), Moscow, Russia
| | - Peter L. Boveng
- Alaska Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, Washington, United States of America
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Tinker MT, Yee JL, Laidre KL, Hatfield BB, Harris MD, Tomoleoni JA, Bell TW, Saarman E, Carswell LP, Miles AK. Habitat Features Predict Carrying Capacity of a Recovering Marine Carnivore. J Wildl Manage 2021. [DOI: 10.1002/jwmg.21985] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- M. Tim Tinker
- U.S. Geological Survey, Western Ecological Research Center Santa Cruz Field Station 2885 Mission Street Santa Cruz CA 95060 USA
| | - Julie L. Yee
- U.S. Geological Survey, Western Ecological Research Center Santa Cruz Field Station 2885 Mission Street Santa Cruz CA 95060 USA
| | - Kristin L. Laidre
- Polar Science Center, Applied Physics Laboratory University of Washington 1013 NE 40th Street Seattle WA 98105 USA
| | - Brian B. Hatfield
- U.S. Geological Survey, Western Ecological Research Center Santa Cruz Field Station 2885 Mission Street Santa Cruz CA 95060 USA
| | - Michael D. Harris
- California Department of Fish and Wildlife Office of Spill Prevention and Response—Veterinary Services 1385 Main Street Morro Bay CA 93442 USA
| | - Joseph A. Tomoleoni
- U.S. Geological Survey, Western Ecological Research Center Santa Cruz Field Station 2885 Mission Street Santa Cruz CA 95060 USA
| | - Tom W. Bell
- Earth Research Institute University of California, Santa Barbara, Santa Barbara California 93106 USA
| | - Emily Saarman
- Partnership for Interdisciplinary Studies of Coastal Oceans (PISCO), Long Marine Laboratory, 115 McAllister Way University of California Santa Cruz CA 95060 USA
| | | | - A. Keith Miles
- U.S. Geological Survey Western Ecological Research Center 3020 State University Drive Sacramento CA 95819 USA
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Separating the effects of climate, bycatch, predation and harvesting on tītī (Ardenna grisea) population dynamics in New Zealand: A model-based assessment. PLoS One 2020; 15:e0243794. [PMID: 33315952 PMCID: PMC7735597 DOI: 10.1371/journal.pone.0243794] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Accepted: 11/26/2020] [Indexed: 11/19/2022] Open
Abstract
A suite of factors may have contributed to declines in the tītī (sooty shearwater; Ardenna grisea) population in the New Zealand region since at least the 1960s. Recent estimation of the magnitude of most sources of non-natural mortality has presented the opportunity to quantitatively assess the relative importance of these factors. We fit a range of population dynamics models to a time-series of relative abundance data from 1976 until 2005, with the various sources of mortality being modelled at the appropriate part of the life-cycle. We present estimates of effects obtained from the best-fitting model and using model averaging. The best-fitting models explained much of the variation in the abundance index when survival and fecundity were linked to the Southern Oscillation Index, with strong decreases in adult survival, juvenile survival and fecundity being related to El Niño-Southern Oscillation (ENSO) events. Predation by introduced animals, harvesting by humans, and bycatch in fisheries also appear to have contributed to the population decline. It is envisioned that the best-fitting models will form the basis for quantitative assessments of competing management strategies. Our analysis suggests that sustainability of the New Zealand tītī population will be most influenced by climate, in particular by how climate change will affect the frequency and intensity of ENSO events in the future. Removal of the effects of both depredation by introduced predators and harvesting by humans is likely to have fewer benefits for the population than alleviating climate effects.
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Fukasawa K, Osada Y, Iijima H. Is harvest size a valid indirect measure of abundance for evaluating the population size of game animals using harvest-based estimation? WILDLIFE BIOLOGY 2020. [DOI: 10.2981/wlb.00708] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Affiliation(s)
- Keita Fukasawa
- K. Fukasawa (https://orcid.org/0000-0002-9563-457X) ✉ , Center for Environmental Biology and Ecosystem Studies, National Inst. for Environmental Studies, Tsukuba, Ibaraki, Japan
| | - Yutaka Osada
- Y. Osada, (https://orcid.org/0000-0001-5967-194X), Fisheries Resources Inst., Fisheries Research and Education Agency, Fukuura, Kanazawa, Yokohama, Kanagawa, Japan
| | - Hayato Iijima
- H. Iijima (https://orcid.org/0000-0003-1064-9420), Forestry and Forest Products Research Inst., Tsukuba, Ibaraki, Japan
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McClintock BT, Langrock R, Gimenez O, Cam E, Borchers DL, Glennie R, Patterson TA. Uncovering ecological state dynamics with hidden Markov models. Ecol Lett 2020; 23:1878-1903. [PMID: 33073921 PMCID: PMC7702077 DOI: 10.1111/ele.13610] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 08/13/2020] [Accepted: 08/25/2020] [Indexed: 01/03/2023]
Abstract
Ecological systems can often be characterised by changes among a finite set of underlying states pertaining to individuals, populations, communities or entire ecosystems through time. Owing to the inherent difficulty of empirical field studies, ecological state dynamics operating at any level of this hierarchy can often be unobservable or 'hidden'. Ecologists must therefore often contend with incomplete or indirect observations that are somehow related to these underlying processes. By formally disentangling state and observation processes based on simple yet powerful mathematical properties that can be used to describe many ecological phenomena, hidden Markov models (HMMs) can facilitate inferences about complex system state dynamics that might otherwise be intractable. However, HMMs have only recently begun to gain traction within the broader ecological community. We provide a gentle introduction to HMMs, establish some common terminology, review the immense scope of HMMs for applied ecological research and provide a tutorial on implementation and interpretation. By illustrating how practitioners can use a simple conceptual template to customise HMMs for their specific systems of interest, revealing methodological links between existing applications, and highlighting some practical considerations and limitations of these approaches, our goal is to help establish HMMs as a fundamental inferential tool for ecologists.
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Affiliation(s)
| | - Roland Langrock
- Department of Business Administration and EconomicsBielefeld UniversityBielefeldGermany
| | - Olivier Gimenez
- CNRS Centre d'Ecologie Fonctionnelle et EvolutiveMontpellierFrance
| | - Emmanuelle Cam
- Laboratoire des Sciences de l'Environnement MarinInstitut Universitaire Européen de la MerUniv. BrestCNRS, IRDIfremerFrance
| | - David L. Borchers
- School of Mathematics and StatisticsUniversity of St AndrewsSt AndrewsUK
| | - Richard Glennie
- School of Mathematics and StatisticsUniversity of St AndrewsSt AndrewsUK
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Chen Y, Shertzer KW, Viehman TS. Spatio‐temporal dynamics of the threatened elkhorn coral
Acropora palmata
: Implications for conservation. DIVERS DISTRIB 2020. [DOI: 10.1111/ddi.13143] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Affiliation(s)
- Yi‐Hsiu Chen
- National Academies of Sciences Engineering and Medicine National Research Council Washington DC USA
- National Centers for Costal Ocean Science NOAA National Ocean Service Beaufort NC USA
| | - Kyle W. Shertzer
- Southeast Fisheries Science Center National Marine Fisheries Service Beaufort NC USA
| | - T. Shay Viehman
- National Centers for Costal Ocean Science NOAA National Ocean Service Beaufort NC USA
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Schlossberg S, Chase MJ, Gobush KS, Wasser SK, Lindsay K. State-space models reveal a continuing elephant poaching problem in most of Africa. Sci Rep 2020; 10:10166. [PMID: 32576862 PMCID: PMC7311459 DOI: 10.1038/s41598-020-66906-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Accepted: 05/26/2020] [Indexed: 11/23/2022] Open
Abstract
The most comprehensive data on poaching of African elephants comes from the Monitoring the Illegal Killing of Elephants (MIKE) program, which reports numbers of illegally killed carcasses encountered by rangers. Recent studies utilizing MIKE data have reported that poaching of African elephants peaked in 2011 and has been decreasing through 2018. Closer examination of these studies, however, raises questions about the conclusion that poaching is decreasing throughout the continent. To provide more accurate information on trends in elephant poaching, we analyzed MIKE data using state-space models. State-space models account for missing data and the error inherent when sampling carcasses. Using the state-space model, for 2011–2018, we found no significant temporal trends in rates of illegal killing for Southern, Central and Western Africa. Only in Eastern Africa have poaching rates decreased substantially since 2011. For Africa as a whole, poaching did decline for 2011–2018, but the decline was entirely due to Eastern African sites. Our results suggest that poaching for ivory has not diminished across most of Africa since 2011. Continued vigilance and anti-poaching efforts will be necessary to combat poaching and to conserve African elephants.
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Affiliation(s)
| | | | - Kathleen S Gobush
- Center for Conservation Biology, Department of Biology, University of Washington, Seattle, WA, 98195, USA
| | - Samuel K Wasser
- Center for Conservation Biology, Department of Biology, University of Washington, Seattle, WA, 98195, USA
| | - Keith Lindsay
- Amboseli Trust for Elephants, PO Box 15135, Langata, Nairobi, 00509, Kenya
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Whitlock SL, Womble JN, Peterson JT. Modelling pinniped abundance and distribution by combining counts at terrestrial sites and in-water sightings. Ecol Modell 2020. [DOI: 10.1016/j.ecolmodel.2020.108965] [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]
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25
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Ruprecht JS, Koons DN, Hersey KR, Hobbs NT, MacNulty DR. The effect of climate on population growth in a cold‐adapted ungulate at its equatorial range limit. Ecosphere 2020. [DOI: 10.1002/ecs2.3058] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Affiliation(s)
- Joel S. Ruprecht
- Department of Wildland Resources and the Ecology Center Utah State University 5230 Old Main Hill Logan Utah 84322‐5230 USA
| | - David N. Koons
- Department of Wildland Resources and the Ecology Center Utah State University 5230 Old Main Hill Logan Utah 84322‐5230 USA
- Department of Fish, Wildlife, and Conservation Biology Colorado State University Fort Collins Colorado 80523‐1474 USA
| | - Kent R. Hersey
- Utah Division of Wildlife Resources Box 146301 Salt Lake City Utah84114 USA
| | - N. Thompson Hobbs
- Natural Resource Ecology Laboratory Department of Ecosystem Science and Sustainability, and Graduate Degree Program in Ecology Colorado State University Fort Collins Colorado 80523‐1474 USA
| | - Daniel R. MacNulty
- Department of Wildland Resources and the Ecology Center Utah State University 5230 Old Main Hill Logan Utah 84322‐5230 USA
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26
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Hoy SR, MacNulty DR, Smith DW, Stahler DR, Lambin X, Peterson RO, Ruprecht JS, Vucetich JA. Fluctuations in age structure and their variable influence on population growth. Funct Ecol 2020. [DOI: 10.1111/1365-2435.13431] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Sarah R. Hoy
- School of Forest Resources and Environmental Science Michigan Technological University Houghton MI USA
| | - Daniel R. MacNulty
- Department of Wildland Resources and Ecology Center Utah State University Logan UT USA
| | - Douglas W. Smith
- Yellowstone Centre for Resources Yellowstone National Park WY USA
| | | | - Xavier Lambin
- School of Biological Sciences University of Aberdeen Aberdeen UK
| | - Rolf O. Peterson
- School of Forest Resources and Environmental Science Michigan Technological University Houghton MI USA
| | - Joel S. Ruprecht
- Department of Fisheries and Wildlife Oregon State University Corvallis OR USA
| | - John A. Vucetich
- School of Forest Resources and Environmental Science Michigan Technological University Houghton MI USA
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Paterson JT, Proffitt K, Rotella J, Garrott R. An improved understanding of ungulate population dynamics using count data: Insights from western Montana. PLoS One 2019; 14:e0226492. [PMID: 31869366 PMCID: PMC6927647 DOI: 10.1371/journal.pone.0226492] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Accepted: 11/27/2019] [Indexed: 11/18/2022] Open
Abstract
Understanding the dynamics of ungulate populations is critical given their ecological and economic importance. In particular, the ability to evaluate the evidence for potential drivers of variation in population trajectories is important for informed management. However, the use of age ratio data (e.g., juveniles:adult females) as an index of variation in population dynamics is hindered by a lack of statistical power and difficult interpretation. Here, we show that the use of a population model based on count, classification and harvest data can dramatically improve the understanding of ungulate population dynamics by: 1) providing estimates of vital rates (e.g., per capita recruitment and population growth) that are easier to interpret and more useful to managers than age ratios and 2) increasing the power to assess potential sources of variation in key vital rates. We used a time series of elk (Cervus canadensis) spring count and classification data (2004 to 2016) and fall harvest data from hunting districts in western Montana to construct a population model to estimate vital rates and assess evidence for an association between a series of environmental covariates and indices of predator abundance on per capita recruitment rates of elk calves. Our results suggest that per capita recruitment rates were negatively associated with cold and wet springs, and severe winters, and positively associated with summer precipitation. In contrast, an analysis of the raw age ratio data failed to detect these relationships. Our approach based on a population model provided estimates of the region-wide mean per capita recruitment rate (mean = 0.25, 90% CI = 0.21, 0.29), temporal variation in hunting-district-specific recruitment rates (minimum = 0.09; 90% CI = [0.07, 0.11], maximum = 0.43; 90% CI = [0.38, 0.48]), and annual population growth rates (minimum = 0.83; 90% CI = [0.78, 0.87], maximum = 1.20; 90% CI = [1.11, 1.29]). We recommend using routinely collected population count and classification data and a population modeling approach rather than interpreting estimated age ratios as a substantial improvement in understanding population dynamics.
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Affiliation(s)
- J. Terrill Paterson
- Department of Ecology, Montana State University, Bozeman, Montana, United States of America
- * E-mail:
| | - Kelly Proffitt
- Montana Department of Fish, Wildlife and Parks, Bozeman, Montana, United States of America
| | - Jay Rotella
- Department of Ecology, Montana State University, Bozeman, Montana, United States of America
| | - Robert Garrott
- Department of Ecology, Montana State University, Bozeman, Montana, United States of America
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Isaac NJB, Jarzyna MA, Keil P, Dambly LI, Boersch-Supan PH, Browning E, Freeman SN, Golding N, Guillera-Arroita G, Henrys PA, Jarvis S, Lahoz-Monfort J, Pagel J, Pescott OL, Schmucki R, Simmonds EG, O'Hara RB. Data Integration for Large-Scale Models of Species Distributions. Trends Ecol Evol 2019; 35:56-67. [PMID: 31676190 DOI: 10.1016/j.tree.2019.08.006] [Citation(s) in RCA: 106] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 08/08/2019] [Accepted: 08/12/2019] [Indexed: 01/23/2023]
Abstract
With the expansion in the quantity and types of biodiversity data being collected, there is a need to find ways to combine these different sources to provide cohesive summaries of species' potential and realized distributions in space and time. Recently, model-based data integration has emerged as a means to achieve this by combining datasets in ways that retain the strengths of each. We describe a flexible approach to data integration using point process models, which provide a convenient way to translate across ecological currencies. We highlight recent examples of large-scale ecological models based on data integration and outline the conceptual and technical challenges and opportunities that arise.
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Affiliation(s)
- Nick J B Isaac
- Centre for Ecology and Hydrology, Benson Lane, Crowmarsh Gifford, Wallingford, OX10 8BB, UK; Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, WC1E 6BT, UK.
| | - Marta A Jarzyna
- Department of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, OH 43210, USA
| | - Petr Keil
- German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, 04103 Leipzig, Germany; Institute of Computer Science, Martin Luther University Halle-Wittenberg, 06120, Halle (Saale), Germany
| | - Lea I Dambly
- Centre for Ecology and Hydrology, Benson Lane, Crowmarsh Gifford, Wallingford, OX10 8BB, UK; Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, WC1E 6BT, UK
| | - Philipp H Boersch-Supan
- British Trust for Ornithology, Thetford, IP24 2PU, UK; Department of Geography, University of Florida, Gainesville, FL 32611, USA
| | - Ella Browning
- Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, WC1E 6BT, UK; Institute of Zoology, Zoological Society of London, London, NW1 4RY, UK
| | - Stephen N Freeman
- Centre for Ecology and Hydrology, Benson Lane, Crowmarsh Gifford, Wallingford, OX10 8BB, UK
| | - Nick Golding
- School of BioSciences, University of Melbourne, Parkville, VIC 3010, Australia
| | | | - Peter A Henrys
- Centre for Ecology and Hydrology, Bailrigg, Lancaster, LA1 4AP, UK
| | - Susan Jarvis
- Centre for Ecology and Hydrology, Bailrigg, Lancaster, LA1 4AP, UK
| | - José Lahoz-Monfort
- School of BioSciences, University of Melbourne, Parkville, VIC 3010, Australia
| | - Jörn Pagel
- Institute of Landscape and Plant Ecology, University of Hohenheim, 70599 Stuttgart, Germany
| | - Oliver L Pescott
- Centre for Ecology and Hydrology, Benson Lane, Crowmarsh Gifford, Wallingford, OX10 8BB, UK
| | - Reto Schmucki
- Centre for Ecology and Hydrology, Benson Lane, Crowmarsh Gifford, Wallingford, OX10 8BB, UK
| | - Emily G Simmonds
- Department of Mathematical Sciences, Centre for Biodiversity Dynamics, Norwegian University of Science and Technology, N-7491 Trondheim, Norway
| | - Robert B O'Hara
- Department of Mathematical Sciences, Centre for Biodiversity Dynamics, Norwegian University of Science and Technology, N-7491 Trondheim, Norway
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29
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Jansen DYM, Pradel R, Mares R, Doutrelant C, Spottiswoode CN, Covas R, Altwegg R. An integrated population model sheds light on the complex population dynamics of a unique colonial breeder. POPUL ECOL 2019. [DOI: 10.1002/1438-390x.12010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Dorine Y. M. Jansen
- Centre for Statistics in Ecology, Environment and Conservation, Department of Statistical Sciences University of Cape Town Rondebosch South Africa
- Applied Biodiversity Research Division, South African National Biodiversity Institute Claremont South Africa
| | - Roger Pradel
- CEFE, CNRS Université de Montpellier, Université Paul Valéry Montpellier 3, EPHE, IRD Montpellier France
| | - Rafael Mares
- CIBIO Research Centre in Biodiversity and Genetic Resources Vairão Portugal
| | - Claire Doutrelant
- CEFE, CNRS Université de Montpellier, Université Paul Valéry Montpellier 3, EPHE, IRD Montpellier France
- DST‐NRF Centre of Excellence, FitzPatrick Institute of African Ornithology University of Cape Town Rondebosch South Africa
| | - Claire N. Spottiswoode
- DST‐NRF Centre of Excellence, FitzPatrick Institute of African Ornithology University of Cape Town Rondebosch South Africa
- Department of Zoology University of Cambridge Cambridge UK
| | - Rita Covas
- CIBIO Research Centre in Biodiversity and Genetic Resources Vairão Portugal
- DST‐NRF Centre of Excellence, FitzPatrick Institute of African Ornithology University of Cape Town Rondebosch South Africa
| | - Res Altwegg
- Centre for Statistics in Ecology, Environment and Conservation, Department of Statistical Sciences University of Cape Town Rondebosch South Africa
- Applied Biodiversity Research Division, South African National Biodiversity Institute Claremont South Africa
- African Climate and Development Initiative University of Cape Town Rondebosch South Africa
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Bird T, Lyon J, Wotherspoon S, Todd C, Tonkin Z, McCarthy M. Combining capture-recapture data and known ages allows estimation of age-dependent survival rates. Ecol Evol 2019; 9:90-99. [PMID: 30680098 PMCID: PMC6342135 DOI: 10.1002/ece3.4633] [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: 06/13/2017] [Revised: 08/06/2018] [Accepted: 08/17/2018] [Indexed: 11/13/2022] Open
Abstract
In many animal populations, demographic parameters such as survival and recruitment vary markedly with age, as do parameters related to sampling, such as capture probability. Failing to account for such variation can result in biased estimates of population-level rates. However, estimating age-dependent survival rates can be challenging because ages of individuals are rarely known unless tagging is done at birth. For many species, it is possible to infer age based on size. In capture-recapture studies of such species, it is possible to use a growth model to infer the age at first capture of individuals. We show how to build estimates of age-dependent survival into a capture-mark-recapture model based on data obtained in a capture-recapture study. We first show how estimates of age based on length increments closely match those based on definitive aging methods. In simulated analyses, we show that both individual ages and age-dependent survival rates estimated from simulated data closely match true values. With our approach, we are able to estimate the age-specific apparent survival rates of Murray and trout cod in the Murray River, Australia. Our model structure provides a flexible framework within which to investigate various aspects of how survival varies with age and will have extensions within a wide range of ecological studies of animals where age can be estimated based on size.
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Affiliation(s)
- Tomas Bird
- Department of BotanyCenter for Excellence in Environmental DecisionsUniversity of MelbourneMelbourneVic.Australia
- Department of Fisheries and OceansNorthWest Atlantic Fisheries CentreSt John's NewfoundlandCanada
| | - Jarod Lyon
- Department of Sustainability and EnvironmentArthur Rylah InstituteMelbourneVic.Australia
| | - Simon Wotherspoon
- Institute of Marine and Antarctic StudiesUniversity of TasmaniaHobartTas.Australia
| | - Charles Todd
- Department of Sustainability and EnvironmentArthur Rylah InstituteMelbourneVic.Australia
| | - Zeb Tonkin
- Department of Sustainability and EnvironmentArthur Rylah InstituteMelbourneVic.Australia
| | - Michael McCarthy
- Department of BotanyCenter for Excellence in Environmental DecisionsUniversity of MelbourneMelbourneVic.Australia
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32
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Sanderlin JS, Block WM, Strohmeyer BE, Saab VA, Ganey JL. Precision gain versus effort with joint models using detection/non-detection and banding data. Ecol Evol 2019; 9:804-817. [PMID: 30766670 PMCID: PMC6362443 DOI: 10.1002/ece3.4825] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Accepted: 08/02/2018] [Indexed: 11/08/2022] Open
Abstract
Capture-recapture techniques provide valuable information, but are often more cost-prohibitive at large spatial and temporal scales than less-intensive sampling techniques. Model development combining multiple data sources to leverage data source strengths and for improved parameter precision has increased, but with limited discussion on precision gain versus effort. We present a general framework for evaluating trade-offs between precision gained and costs associated with acquiring multiple data sources, useful for designing future or new phases of current studies.We illustrated how Bayesian hierarchical joint models using detection/non-detection and banding data can improve abundance, survival, and recruitment inference, and quantified data source costs in a northern Arizona, USA, western bluebird (Sialia mexicana) population. We used an 8-year detection/non-detection (distributed across the landscape) and banding (subset of locations within landscape) data set to estimate parameters. We constructed separate models using detection/non-detection and banding data, and a joint model using both data types to evaluate parameter precision gain relative to effort.Joint model parameter estimates were more precise than single data model estimates, but parameter precision varied (apparent survival > abundance > recruitment). Banding provided greater apparent survival precision than detection/non-detection data. Therefore, little precision was gained when detection/non-detection data were added to banding data. Additional costs were minimal; however, additional spatial coverage and ability to estimate abundance and recruitment improved inference. Conversely, more precision was gained when adding banding to detection/non-detection data at higher cost. Spatial coverage was identical, yet survival and abundance estimates were more precise. Justification of increased costs associated with additional data types depends on project objectives.We illustrate a general framework for evaluating precision gain relative to effort, applicable to joint data models with any data type combination. This framework evaluates costs and benefits from and effort levels between multiple data types, thus improving population monitoring designs.
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Affiliation(s)
| | - William M. Block
- Rocky Mountain Research StationU.S.D.A. Forest ServiceFlagstaffArizona
| | | | - Victoria A. Saab
- Rocky Mountain Research StationU.S.D.A. Forest ServiceBozemanMontana
| | - Joseph L. Ganey
- Rocky Mountain Research StationU.S.D.A. Forest ServiceFlagstaffArizona
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33
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Kindsvater HK, Dulvy NK, Horswill C, Juan-Jordá MJ, Mangel M, Matthiopoulos J. Overcoming the Data Crisis in Biodiversity Conservation. Trends Ecol Evol 2018; 33:676-688. [DOI: 10.1016/j.tree.2018.06.004] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Revised: 05/11/2018] [Accepted: 06/12/2018] [Indexed: 11/27/2022]
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34
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Scroggie MP, Forsyth DM, McPhee SR, Matthews J, Stuart IG, Stamation KA, Lindeman M, Ramsey DSL. Invasive prey controlling invasive predators? European rabbit abundance does not determine red fox population dynamics. J Appl Ecol 2018. [DOI: 10.1111/1365-2664.13253] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Michael P. Scroggie
- Department of Environment, Land, Water and PlanningArthur Rylah Institute for Environmental Research Heidelberg Vic. Australia
| | - David M. Forsyth
- Vertebrate Pest Research UnitNSW Department of Primary Industries Orange NSW Australia
| | | | - John Matthews
- Department of Economic Development, Jobs, Transport and Resources Hamilton Vic. Australia
| | - Ivor G. Stuart
- Department of Environment, Land, Water and PlanningArthur Rylah Institute for Environmental Research Heidelberg Vic. Australia
| | - Kasey A. Stamation
- Department of Environment, Land, Water and PlanningArthur Rylah Institute for Environmental Research Heidelberg Vic. Australia
| | - Michael Lindeman
- Department of Environment, Land, Water and PlanningArthur Rylah Institute for Environmental Research Heidelberg Vic. Australia
| | - David S. L. Ramsey
- Department of Environment, Land, Water and PlanningArthur Rylah Institute for Environmental Research Heidelberg Vic. Australia
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35
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Allen ML, Norton AS, Stauffer G, Roberts NM, Luo Y, Li Q, MacFarland D, Van Deelen TR. A Bayesian state-space model using age-at-harvest data for estimating the population of black bears (Ursus americanus) in Wisconsin. Sci Rep 2018; 8:12440. [PMID: 30127405 PMCID: PMC6102245 DOI: 10.1038/s41598-018-30988-4] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Accepted: 08/07/2018] [Indexed: 11/29/2022] Open
Abstract
Population estimation is essential for the conservation and management of fish and wildlife, but accurate estimates are often difficult or expensive to obtain for cryptic species across large geographical scales. Accurate statistical models with manageable financial costs and field efforts are needed for hunted populations and using age-at-harvest data may be the most practical foundation for these models. Several rigorous statistical approaches that use age-at-harvest and other data to accurately estimate populations have recently been developed, but these are often dependent on (a) accurate prior knowledge about demographic parameters of the population, (b) auxiliary data, and (c) initial population size. We developed a two-stage state-space Bayesian model for a black bear (Ursus americanus) population with age-at-harvest data, but little demographic data and no auxiliary data available, to create a statewide population estimate and test the sensitivity of the model to bias in the prior distributions of parameters and initial population size. The posterior abundance estimate from our model was similar to an independent capture-recapture estimate from tetracycline sampling and the population trend was similar to the catch-per-unit-effort for the state. Our model was also robust to bias in the prior distributions for all parameters, including initial population size, except for reporting rate. Our state-space model created a precise estimate of the black bear population in Wisconsin based on age-at-harvest data and potentially improves on previous models by using little demographic data, no auxiliary data, and not being sensitive to initial population size.
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Affiliation(s)
- Maximilian L Allen
- Illinois Natural History Survey, University of Illinois, 1816 S. Oak Street, Champaign, IL, 61820, USA. .,Wisconsin Department of Natural Resources, 107 Sutliff Avenue, Rhinelander, WI, 54501, USA. .,Department of Forest and Wildlife Ecology, University of Wisconsin, 1630 Linden Drive, Madison, WI, 53706, USA.
| | - Andrew S Norton
- Minnesota Department of Natural Resources, 35365 800th Avenue, Madelia, MN, 56062, USA
| | - Glenn Stauffer
- Wisconsin Department of Natural Resources, 107 Sutliff Avenue, Rhinelander, WI, 54501, USA
| | - Nathan M Roberts
- Wisconsin Department of Natural Resources, 107 Sutliff Avenue, Rhinelander, WI, 54501, USA
| | - Yanshi Luo
- Department of Statistics, University of Wisconsin, 1300 University Ave, Madison, WI, 53706, USA
| | - Qing Li
- Department of Statistics, University of Wisconsin, 1300 University Ave, Madison, WI, 53706, USA.,Department of Industrial & Manufacturing Systems Engineering, Iowa State University, 3025 Black Engineering Building, Ames, IA, 50011, USA
| | - David MacFarland
- Wisconsin Department of Natural Resources, 107 Sutliff Avenue, Rhinelander, WI, 54501, USA
| | - Timothy R Van Deelen
- Department of Forest and Wildlife Ecology, University of Wisconsin, 1630 Linden Drive, Madison, WI, 53706, USA
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36
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Jamnia AR, Keikha AA, Ahmadpour M, Cissé AA, Rokouei M. Applying bayesian population assessment models to artisanal, multispecies fisheries in the Northern Mokran Sea, Iran. NATURE CONSERVATION 2018. [DOI: 10.3897/natureconservation.28.25212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Small-scale fisheries substantially contribute to the reduction of poverty, local economies and food safety in many countries. However, limited and low-quality catches and effort data for small-scale fisheries complicate the stock assessment and management. Bayesian modelling has been advocated when assessing fisheries with limited data. Specifically, Bayesian models can incorporate information of the multiple sources, improve precision in the stock assessments and provide specific levels of uncertainty for estimating the relevant parameters. In this study, therefore, the state-space Bayesian generalised surplus production models will be used in order to estimate the stock status of fourteen Demersal fish species targeted by small-scale fisheries in Sistan and Baluchestan, Iran. The model was estimated using Markov chain Monte Carlo (MCMC) and Gibbs Sampling. Model parameter estimates were evaluated by the formal convergence and stationarity diagnostic tests, indicating convergence and accuracy. They were also aligned with existing parameter estimates for fourteen species of the other locations. This suggests model reliability and demonstrates the utility of Bayesian models. According to estimated fisheries’ management reference points, all assessed fish stocks appear to be overfished. Overfishing considered, the current fisheries management strategies for the small-scale fisheries may need some adjustments to warrant the long-term viability of the fisheries.
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37
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Iijima H. Seasonal Change of Deer Occurrence and Damage of Plant Biomass in the Mosaic Landscape of Artificial Grasslands and Forests. MAMMAL STUDY 2018. [DOI: 10.3106/ms2017-0087] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Affiliation(s)
- Hayato Iijima
- Forestry and Forest Products Research Institute, 1, Matsunosato, Tsukuba, Ibaraki 305-8687, Japan
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38
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Westcott DA, Caley P, Heersink DK, McKeown A. A state-space modelling approach to wildlife monitoring with application to flying-fox abundance. Sci Rep 2018; 8:4038. [PMID: 29511249 PMCID: PMC5840426 DOI: 10.1038/s41598-018-22294-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Accepted: 02/06/2018] [Indexed: 11/13/2022] Open
Abstract
Monitoring flying-foxes is challenging as their extreme mobility produces highly dynamic population processes, considerable logistic difficulty, and variability in estimated population size. We report on methods for inferring population trend for the population of the spectacled flying-fox (Pteropus conspicillatus) in Australia. Monthly monitoring is conducted at all known roost sites across the species’ range in the Wet Tropics Region. The proportion of animals in camps varies seasonally and stochastic environmental events appear to be influential. We develop a state-space model that incorporates these processes and enables inference on total population trends and uses early warning analysis to identify the causes of population dynamics. The model suggests that population growth rate is stable in the absence of cyclones, however, cyclones appear to impact on both survival and reproduction. The population recovered after two cyclones but declined after a third. The modelling estimates a population decline over 15 years of c. 75% (mean r = − 0.12yr−1 and belief of negative trend is c. 83%) suggesting that conservation action is warranted. Our work shows that a state-space modelling approach is a significant improvement on inference from raw counts from surveys and demonstrates that this approach is a workable alternative to other methods.
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Affiliation(s)
- David A Westcott
- CSIRO Land and Water, PO Box 780, Atherton, Queensland, Australia.
| | - Peter Caley
- CSIRO Data61, GPO Box 1700, Canberra, ACT 2601, Australia
| | | | - Adam McKeown
- CSIRO Land and Water, PO Box 12139, Earlville BC, Qld, 4870, Australia
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39
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Saunders SP, Cuthbert FJ, Zipkin EF. Evaluating population viability and efficacy of conservation management using integrated population models. J Appl Ecol 2018. [DOI: 10.1111/1365-2664.13080] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Sarah P. Saunders
- Department of Integrative Biology College of Natural Science Michigan State University East Lansing MI USA
| | - Francesca J. Cuthbert
- Department of Fisheries, Wildlife, and Conservation Biology University of Minnesota St. Paul MN USA
| | - Elise F. Zipkin
- Department of Integrative Biology College of Natural Science Michigan State University East Lansing MI USA
- Ecology, Evolutionary Biology, and Behavior Program Michigan State University East Lansing MI USA
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40
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Whitlock R, Mäntyniemi S, Palm S, Koljonen M, Dannewitz J, Östergren J. Integrating genetic analysis of mixed populations with a spatially explicit population dynamics model. Methods Ecol Evol 2018. [DOI: 10.1111/2041-210x.12946] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Rebecca Whitlock
- Institute of Freshwater ResearchSwedish University of Agricultural Sciences Drottningholm Sweden
| | - Samu Mäntyniemi
- Department of Environmental SciencesBayesian Environmental Modelling GroupUniversity of Helsinki Helsinki Finland
| | - Stefan Palm
- Institute of Freshwater ResearchSwedish University of Agricultural Sciences Drottningholm Sweden
| | | | - Johan Dannewitz
- Institute of Freshwater ResearchSwedish University of Agricultural Sciences Drottningholm Sweden
| | - Johan Östergren
- Institute of Freshwater ResearchSwedish University of Agricultural Sciences Drottningholm Sweden
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41
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Amburgey SM, Miller DAW, Campbell Grant EH, Rittenhouse TAG, Benard MF, Richardson JL, Urban MC, Hughson W, Brand AB, Davis CJ, Hardin CR, Paton PWC, Raithel CJ, Relyea RA, Scott AF, Skelly DK, Skidds DE, Smith CK, Werner EE. Range position and climate sensitivity: The structure of among-population demographic responses to climatic variation. GLOBAL CHANGE BIOLOGY 2018; 24:439-454. [PMID: 28833972 DOI: 10.1111/gcb.13817] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Accepted: 06/26/2017] [Indexed: 05/28/2023]
Abstract
Species' distributions will respond to climate change based on the relationship between local demographic processes and climate and how this relationship varies based on range position. A rarely tested demographic prediction is that populations at the extremes of a species' climate envelope (e.g., populations in areas with the highest mean annual temperature) will be most sensitive to local shifts in climate (i.e., warming). We tested this prediction using a dynamic species distribution model linking demographic rates to variation in temperature and precipitation for wood frogs (Lithobates sylvaticus) in North America. Using long-term monitoring data from 746 populations in 27 study areas, we determined how climatic variation affected population growth rates and how these relationships varied with respect to long-term climate. Some models supported the predicted pattern, with negative effects of extreme summer temperatures in hotter areas and positive effects on recruitment for summer water availability in drier areas. We also found evidence of interacting temperature and precipitation influencing population size, such as extreme heat having less of a negative effect in wetter areas. Other results were contrary to predictions, such as positive effects of summer water availability in wetter parts of the range and positive responses to winter warming especially in milder areas. In general, we found wood frogs were more sensitive to changes in temperature or temperature interacting with precipitation than to changes in precipitation alone. Our results suggest that sensitivity to changes in climate cannot be predicted simply by knowing locations within the species' climate envelope. Many climate processes did not affect population growth rates in the predicted direction based on range position. Processes such as species-interactions, local adaptation, and interactions with the physical landscape likely affect the responses we observed. Our work highlights the need to measure demographic responses to changing climate.
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Affiliation(s)
- Staci M Amburgey
- Department of Ecosystem Sciences and Management, The Pennsylvania State University, University Park, PA, USA
- Intercollege Graduate Ecology Program, The Pennsylvania State University, University Park, PA, USA
| | - David A W Miller
- Department of Ecosystem Sciences and Management, The Pennsylvania State University, University Park, PA, USA
| | - Evan H Campbell Grant
- USGS Patuxent Wildlife Research Center, SO Conte Anadromous Fish Research Center, Turners Falls, MA, USA
| | - Tracy A G Rittenhouse
- Department of Natural Resources and the Environment, University of Connecticut, Storrs, CT, USA
| | - Michael F Benard
- Department of Biology, Case Western Reserve University, Cleveland, OH, USA
| | | | - Mark C Urban
- Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USA
| | | | - Adrianne B Brand
- USGS Patuxent Wildlife Research Center, SO Conte Anadromous Fish Research Center, Turners Falls, MA, USA
| | - Christopher J Davis
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | - Carmen R Hardin
- Forestry Division, Wisconsin Department of Natural Resources, Madison, WI, USA
| | - Peter W C Paton
- Department of Natural Resources Science, University of Rhode Island, Kingston, RI, USA
| | - Christopher J Raithel
- Division of Fish and Wildlife, Rhode Island Department of Environmental Management, West Kingston, RI, USA
| | - Rick A Relyea
- Department of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - A Floyd Scott
- Department of Biology, Austin Peay State University, Clarksville, TN, USA
| | - David K Skelly
- School of Forestry and Environmental Studies, Yale University, New Haven, CT, USA
| | - Dennis E Skidds
- Northeast Coastal and Barrier Network, National Parks Service, Kingston, RI, USA
| | - Charles K Smith
- Department of Biology, High Point University, High Point, NC, USA
| | - Earl E Werner
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA
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42
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Barshep Y, Erni B, Underhill LG, Altwegg R. Identifying ecological and life-history drivers of population dynamics of wetland birds in South Africa. Glob Ecol Conserv 2017. [DOI: 10.1016/j.gecco.2017.09.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
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43
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Nichols J, Spendelow J, Nichols J. Using Optimal Transport Theory to Estimate Transition Probabilities in Metapopulation Dynamics. Ecol Modell 2017. [DOI: 10.1016/j.ecolmodel.2017.06.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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44
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Takeshita K, Tanikawa K, Kaji K. Applicability of a Bayesian state-space model for evaluating the effects of localized culling on subsequent density changes: sika deer as a case study. EUR J WILDLIFE RES 2017. [DOI: 10.1007/s10344-017-1128-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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45
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McElderry RM. Estimating Density and Temperature Dependence of Juvenile Vital Rates Using a Hidden Markov Model. INSECTS 2017; 8:insects8020051. [PMID: 28505138 PMCID: PMC5492065 DOI: 10.3390/insects8020051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2016] [Revised: 03/27/2017] [Accepted: 05/03/2017] [Indexed: 11/16/2022]
Abstract
Organisms in the wild have cryptic life stages that are sensitive to changing environmental conditions and can be difficult to survey. In this study, I used mark-recapture methods to repeatedly survey Anaea aidea (Nymphalidae) caterpillars in nature, then modeled caterpillar demography as a hidden Markov process to assess if temporal variability in temperature and density influence the survival and growth of A. aidea over time. Individual encounter histories result from the joint likelihood of being alive and observed in a particular stage, and I have included hidden states by separating demography and observations into parallel and independent processes. I constructed a demographic matrix containing the probabilities of all possible fates for each stage, including hidden states, e.g., eggs and pupae. I observed both dead and live caterpillars with high probability. Peak caterpillar abundance attracted multiple predators, and survival of fifth instars declined as per capita predation rate increased through spring. A time lag between predator and prey abundance was likely the cause of improved fifth instar survival estimated at high density. Growth rates showed an increase with temperature, but the preferred model did not include temperature. This work illustrates how state-space models can include unobservable stages and hidden state processes to evaluate how environmental factors influence vital rates of cryptic life stages in the wild.
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Affiliation(s)
- Robert M McElderry
- Department of Biology, University of Miami, Coral Gables, FL 33146, USA.
- Fairchild Tropical Botanic Garden, Miami, FL 33156, USA.
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46
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Tolimieri N, Holmes EE, Williams GD, Pacunski R, Lowry D. Population assessment using multivariate time-series analysis: A case study of rockfishes in Puget Sound. Ecol Evol 2017; 7:2846-2860. [PMID: 28428874 PMCID: PMC5395462 DOI: 10.1002/ece3.2901] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Revised: 02/13/2017] [Accepted: 02/21/2017] [Indexed: 11/06/2022] Open
Abstract
Estimating a population's growth rate and year-to-year variance is a key component of population viability analysis (PVA). However, standard PVA methods require time series of counts obtained using consistent survey methods over many years. In addition, it can be difficult to separate observation and process variance, which is critical for PVA. Time-series analysis performed with multivariate autoregressive state-space (MARSS) models is a flexible statistical framework that allows one to address many of these limitations. MARSS models allow one to combine surveys with different gears and across different sites for estimation of PVA parameters, and to implement replication, which reduces the variance-separation problem and maximizes informational input for mean trend estimation. Even data that are fragmented with unknown error levels can be accommodated. We present a practical case study that illustrates MARSS analysis steps: data choice, model set-up, model selection, and parameter estimation. Our case study is an analysis of the long-term trends of rockfish in Puget Sound, Washington, based on citizen science scuba surveys, a fishery-independent trawl survey, and recreational fishery surveys affected by bag-limit reductions. The best-supported models indicated that the recreational and trawl surveys tracked different, temporally independent assemblages that declined at similar rates (an average of -3.8% to -3.9% per year). The scuba survey tracked a separate increasing and temporally independent assemblage (an average of 4.1% per year). Three rockfishes (bocaccio, canary, and yelloweye) are listed in Puget Sound under the US Endangered Species Act (ESA). These species are associated with deep water, which the recreational and trawl surveys sample better than the scuba survey. All three ESA-listed rockfishes declined as a proportion of recreational catch between the 1970s and 2010s, suggesting that they experienced similar or more severe reductions in abundance than the 3.8-3.9% per year declines that were estimated for rockfish populations sampled by the recreational and trawl surveys.
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Affiliation(s)
- Nick Tolimieri
- Conservation Biology DivisionNorthwest Fisheries Science CenterNational Marine Fisheries ServiceNational Oceanic and Atmospheric AdministrationSeattleWAUSA
| | - Elizabeth E. Holmes
- Conservation Biology DivisionNorthwest Fisheries Science CenterNational Marine Fisheries ServiceNational Oceanic and Atmospheric AdministrationSeattleWAUSA
| | - Gregory D. Williams
- Pacific States Marine Fisheries Commission, Under Contract to Northwest Fisheries Science CenterNational Marine Fisheries ServiceNational Oceanic and Atmospheric AdministrationSeattleWAUSA
| | - Robert Pacunski
- Marine Fish Science UnitFish Management DivisionWashington Department of Fish and WildlifeMill CreekWAUSA
| | - Dayv Lowry
- Marine Fish Science UnitFish Management DivisionWashington Department of Fish and WildlifeOlympiaWAUSA
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47
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Bayesian Methods for Estimating Animal Abundance at Large Spatial Scales Using Data from Multiple Sources. JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS 2017. [DOI: 10.1007/s13253-017-0276-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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48
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Dunham K, Grand JB. Evaluating models of population process in a threatened population of Steller's eiders: a retrospective approach. Ecosphere 2017. [DOI: 10.1002/ecs2.1720] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Affiliation(s)
- Kylee Dunham
- School of Forestry and Wildlife Sciences; Auburn University; Auburn Alabama 36849 USA
| | - James B. Grand
- U.S. Geological Survey; Alabama Cooperative Fish and Wildlife Research Unit; Auburn University; Auburn Alabama 36849 USA
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49
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Brommer JE, Wistbacka R, Selonen V. Immigration ensures population survival in the Siberian flying squirrel. Ecol Evol 2017; 7:1858-1868. [PMID: 28331593 PMCID: PMC5355189 DOI: 10.1002/ece3.2807] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Revised: 11/10/2016] [Accepted: 01/11/2017] [Indexed: 11/24/2022] Open
Abstract
Linking dispersal to population growth remains a challenging task and is a major knowledge gap, for example, for conservation management. We studied relative roles of different demographic rates behind population growth in Siberian flying squirrels in two nest-box breeding populations in western Finland. Adults and offspring were captured and individually identifiable. We constructed an integrated population model, which estimated all relevant annual demographic rates (birth, local [apparent] survival, and immigration) as well as population growth rates. One population (studied 2002-2014) fluctuated around a steady-state equilibrium, whereas the other (studied 1995-2014) showed a numerical decline. Immigration was the demographic rate which showed clear correlations to annual population growth rates in both populations. Population growth rate was density dependent in both populations. None of the demographic rates nor the population growth rate correlated across the two study populations, despite their proximity suggesting that factors regulating the dynamics are determined locally. We conclude that flying squirrels may persist in a network of uncoupled subpopulations, where movement between subpopulations is of critical importance. Our study supports the view that dispersal has the key role in population survival of a small forest rodent.
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Affiliation(s)
| | | | - Vesa Selonen
- Department of BiologyUniversity of TurkuTurkuFinland
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50
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Flowerdew JR, Amano T, Sutherland WJ. Strong "bottom-up" influences on small mammal populations: State-space model analyses from long-term studies. Ecol Evol 2017; 7:1699-1711. [PMID: 28331581 PMCID: PMC5355190 DOI: 10.1002/ece3.2725] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Revised: 11/25/2016] [Accepted: 12/18/2016] [Indexed: 11/07/2022] Open
Abstract
“Bottom‐up” influences, that is, masting, plus population density and climate, commonly influence woodland rodent demography. However, “top‐down” influences (predation) also intervene. Here, we assess the impacts of masting, climate, and density on rodent populations placed in the context of what is known about “top‐down” influences. To explain between‐year variations in bank vole Myodes glareolus and wood mouse Apodemus sylvaticus population demography, we applied a state‐space model to 33 years of catch‐mark‐release live‐trapping, winter temperature, and precise mast‐collection data. Experimental mast additions aided interpretation. Rodent numbers in European ash Fraxinus excelsior woodland were estimated (May/June, November/December). December–March mean minimum daily temperature represented winter severity. Total marked adult mice/voles (and juveniles in May/June) provided density indices validated against a model‐generated population estimate; this allowed estimation of the structure of a time‐series model and the demographic impacts of the climatic/biological variables. During two winters of insignificant fruit‐fall, 6.79 g/m2 sterilized ash seed (as fruit) was distributed over an equivalent woodland similarly live‐trapped. September–March fruit‐fall strongly increased bank vole spring reproductive rate and winter and summer population growth rates; colder winters weakly reduced winter population growth. September–March fruit‐fall and warmer winters marginally increased wood mouse spring reproductive rate and September–December fruit‐fall weakly elevated summer population growth. Density dependence significantly reduced both species' population growth. Fruit‐fall impacts on demography still appeared after a year. Experimental ash fruit addition confirmed its positive influence on bank vole winter population growth with probable moderation by colder temperatures. The models show the strong impact of masting as a “bottom‐up” influence on rodent demography, emphasizing independent masting and weather influences; delayed effects of masting; and the importance of density dependence and its interaction with masting. We conclude that these rodents show strong “bottom‐up” and density‐dependent influences on demography moderated by winter temperature. “Top‐down” influences appear weak and need further investigation.
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Affiliation(s)
| | - Tatsuya Amano
- Department of Zoology University of Cambridge Cambridge UK
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