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Hjeljord O, Loe LE. The roles of climate and alternative prey in explaining 142 years of declining willow ptarmigan hunting yield. WILDLIFE BIOLOGY 2022. [DOI: 10.1002/wlb3.01058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Olav Hjeljord
- Faculty of Environmental Sciences and Natural Resource Management, Norwegian Univ. of Life Sciences Aas Norway
| | - Leif Egil Loe
- Faculty of Environmental Sciences and Natural Resource Management, Norwegian Univ. of Life Sciences Aas Norway
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2
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Lewis ASL, Rollinson CR, Allyn AJ, Ashander J, Brodie S, Brookson CB, Collins E, Dietze MC, Gallinat AS, Juvigny‐Khenafou N, Koren G, McGlinn DJ, Moustahfid H, Peters JA, Record NR, Robbins CJ, Tonkin J, Wardle GM. The power of forecasts to advance ecological theory. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13955] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
| | | | | | - Jaime Ashander
- U.S. Geological Survey, Eastern Ecological Science Center, Patuxent Research Refuge Laurel MD USA
| | - Stephanie Brodie
- Institute of Marine Science University of California Santa Cruz Monterey CA USA
- Environmental Research Division, Southwest Fisheries Science Center, National Oceanic and Atmospheric Administration Monterey CA USA
| | - Cole B. Brookson
- Department of Biological Sciences University of Alberta Edmonton AB Canada
| | - Elyssa Collins
- Center for Geospatial Analytics North Carolina State University Raleigh NC USA
| | - Michael C. Dietze
- Department of Earth & Environment Boston University Boston MA United States
| | | | - Noel Juvigny‐Khenafou
- iES—Institute for Environmental Sciences University of Koblenz‐Landau Landau i. d. Pfalz Germany
| | - Gerbrand Koren
- Copernicus Institute of Sustainable Development Utrecht University Utrecht The Netherlands
| | | | | | | | | | - Caleb J. Robbins
- Department of Biology, Center for Reservoir and Aquatic Systems Research Baylor University Waco TX USA
| | - Jonathan Tonkin
- School of Biological Sciences University of Canterbury Christchurch New Zealand
- Te Pūnaha Matatini, Centre of Research Excellence in Complex Systems New Zealand
- Bioprotection Aotearoa, Centre of Research Excellence New Zealand
| | - Glenda M. Wardle
- School of Life and Environmental Sciences University of Sydney Sydney New South Wales Australia
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Lewis ASL, Woelmer WM, Wander HL, Howard DW, Smith JW, McClure RP, Lofton ME, Hammond NW, Corrigan RS, Thomas RQ, Carey CC. Increased adoption of best practices in ecological forecasting enables comparisons of forecastability. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2022; 32:e2500. [PMID: 34800082 PMCID: PMC9285336 DOI: 10.1002/eap.2500] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 07/21/2021] [Accepted: 10/05/2021] [Indexed: 05/24/2023]
Abstract
Near-term iterative forecasting is a powerful tool for ecological decision support and has the potential to transform our understanding of ecological predictability. However, to this point, there has been no cross-ecosystem analysis of near-term ecological forecasts, making it difficult to synthesize diverse research efforts and prioritize future developments for this emerging field. In this study, we analyzed 178 near-term (≤10-yr forecast horizon) ecological forecasting papers to understand the development and current state of near-term ecological forecasting literature and to compare forecast accuracy across scales and variables. Our results indicated that near-term ecological forecasting is widespread and growing: forecasts have been produced for sites on all seven continents and the rate of forecast publication is increasing over time. As forecast production has accelerated, some best practices have been proposed and application of these best practices is increasing. In particular, data publication, forecast archiving, and workflow automation have all increased significantly over time. However, adoption of proposed best practices remains low overall: for example, despite the fact that uncertainty is often cited as an essential component of an ecological forecast, only 45% of papers included uncertainty in their forecast outputs. As the use of these proposed best practices increases, near-term ecological forecasting has the potential to make significant contributions to our understanding of forecastability across scales and variables. In this study, we found that forecastability (defined here as realized forecast accuracy) decreased in predictable patterns over 1-7 d forecast horizons. Variables that were closely related (i.e., chlorophyll and phytoplankton) displayed very similar trends in forecastability, while more distantly related variables (i.e., pollen and evapotranspiration) exhibited significantly different patterns. Increasing use of proposed best practices in ecological forecasting will allow us to examine the forecastability of additional variables and timescales in the future, providing a robust analysis of the fundamental predictability of ecological variables.
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Affiliation(s)
| | | | | | - Dexter W. Howard
- Department of Biological SciencesVirginia TechBlacksburgVirginiaUSA
| | - John W. Smith
- Department of StatisticsVirginia TechBlacksburgVirginiaUSA
| | - Ryan P. McClure
- Department of Biological SciencesVirginia TechBlacksburgVirginiaUSA
| | - Mary E. Lofton
- Department of Biological SciencesVirginia TechBlacksburgVirginiaUSA
| | | | - Rachel S. Corrigan
- Department of Forest Resources and Environmental ConservationVirginia TechBlacksburgVirginiaUSA
| | - R. Quinn Thomas
- Department of Forest Resources and Environmental ConservationVirginia TechBlacksburgVirginiaUSA
| | - Cayelan C. Carey
- Department of Biological SciencesVirginia TechBlacksburgVirginiaUSA
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4
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Tombre IM, Fredriksen F, Jerpstad O, Østnes JE, Eythórsson E. Population control by means of organised hunting effort: Experiences from a voluntary goose hunting arrangement. AMBIO 2022; 51:728-742. [PMID: 34185252 PMCID: PMC8800999 DOI: 10.1007/s13280-021-01590-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 05/27/2021] [Accepted: 06/04/2021] [Indexed: 05/20/2023]
Abstract
Implementing management objectives may be challenging when decisions are made at different scales than where they are supposed to be carried out. In this study we present a situation where local goose hunting arrangements respond to objectives in an international management plan for pink-footed geese (Anser brachyrhynchus) and a local wish to reduce goose numbers as means to reduce grazing damage on farmland. A unique ten-year dataset provides an evaluation of the efficiency of voluntary actions at a local scale for implementing a policy of population control of geese, and general lessons are drawn for collaboration and co-production of knowledge for adaptive management. The study demonstrates how both the hunters and geese adapt in a situation where increasing the harvest of geese is the main objective. Introducing hunting-free days and safe foraging areas significantly increased goose numbers in the study area, with a corresponding increase in hunting success in terms of number of harvested geese. The geese's behavioural response to hunting also triggered the hunters to adapt accordingly by optimal timing and placement in the landscape. Based on the results of the present study we suggest a framework for local implementation of management actions. Bringing end-users on board, facilitates processes and strengthens the achievements, as they represent the actors where implementation occurs. Specifically, our findings demonstrate how optimal goose hunting can be practiced by the use of an adaptive framework with active stakeholder participation.
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Affiliation(s)
- Ingunn M. Tombre
- Department of Arctic Ecology, The Fram Centre, Norwegian Institute for Nature Research, Langnes, P.O. Box 6606, 9007 Tromsø, Norway
| | - Fredrik Fredriksen
- NORD University, P.O. Box 2510, 7729 Steinkjer, Norway
- Forum for Nature and Outdoor Life in Trøndelag, Kjøpmannsgata 12, 7500 Stjørdal, Norway
| | | | - Jan Eivind Østnes
- Faculty of Biosciences and Aquaculture, NORD University, P.O. Box 2510, 7729 Steinkjer, Norway
| | - Einar Eythórsson
- High North Department, The Fram Centre, Norwegian Institute for Cultural Heritage Research, Langnes, P.O. Box 6606, 9007 Tromsø, Norway
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Tirronen K, Ehrich D, Panchenko D, Dalén L, Angerbjörn A. The Arctic fox (Vulpes lagopus L.) on the Kola Peninsula (Russia): silently disappearing in the mist of data deficiency? Polar Biol 2021. [DOI: 10.1007/s00300-021-02847-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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6
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Marolla F, Henden JA, Fuglei E, Pedersen ÅØ, Itkin M, Ims RA. Iterative model predictions for wildlife populations impacted by rapid climate change. GLOBAL CHANGE BIOLOGY 2021; 27:1547-1559. [PMID: 33448074 DOI: 10.1111/gcb.15518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 12/15/2020] [Indexed: 06/12/2023]
Abstract
To improve understanding and management of the consequences of current rapid environmental change, ecologists advocate using long-term monitoring data series to generate iterative near-term predictions of ecosystem responses. This approach allows scientific evidence to increase rapidly and management strategies to be tailored simultaneously. Iterative near-term forecasting may therefore be particularly useful for adaptive monitoring of ecosystems subjected to rapid climate change. Here, we show how to implement near-term forecasting in the case of a harvested population of rock ptarmigan in high-arctic Svalbard, a region subjected to the largest and most rapid climate change on Earth. We fitted state-space models to ptarmigan counts from point transect distance sampling during 2005-2019 and developed two types of predictions: (1) explanatory predictions to quantify the effect of potential drivers of ptarmigan population dynamics, and (2) anticipatory predictions to assess the ability of candidate models of increasing complexity to forecast next-year population density. Based on the explanatory predictions, we found that a recent increasing trend in the Svalbard rock ptarmigan population can be attributed to major changes in winter climate. Currently, a strong positive effect of increasing average winter temperature on ptarmigan population growth outweighs the negative impacts of other manifestations of climate change such as rain-on-snow events. Moreover, the ptarmigan population may compensate for current harvest levels. Based on the anticipatory predictions, the near-term forecasting ability of the models improved nonlinearly with the length of the time series, but yielded good forecasts even based on a short time series. The inclusion of ecological predictors improved forecasts of sharp changes in next-year population density, demonstrating the value of ecosystem-based monitoring. Overall, our study illustrates the power of integrating near-term forecasting in monitoring systems to aid understanding and management of wildlife populations exposed to rapid climate change. We provide recommendations for how to improve this approach.
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Affiliation(s)
- Filippo Marolla
- Department of Arctic and Marine Biology, UiT The Arctic University of Norway, Tromsø, Norway
| | - John-André Henden
- Department of Arctic and Marine Biology, UiT The Arctic University of Norway, Tromsø, Norway
| | - Eva Fuglei
- Norwegian Polar Institute, Fram Centre, Tromsø, Norway
| | | | - Mikhail Itkin
- Norwegian Polar Institute, Fram Centre, Tromsø, Norway
| | - Rolf A Ims
- Department of Arctic and Marine Biology, UiT The Arctic University of Norway, Tromsø, Norway
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7
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Bowler DE, Kvasnes MAJ, Pedersen HC, Sandercock BK, Nilsen EB. Impacts of predator-mediated interactions along a climatic gradient on the population dynamics of an alpine bird. Proc Biol Sci 2020; 287:20202653. [PMID: 33352076 PMCID: PMC7779518 DOI: 10.1098/rspb.2020.2653] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
According to classic theory, species' population dynamics and distributions are less influenced by species interactions under harsh climatic conditions compared to under more benign climatic conditions. In alpine and boreal ecosystems in Fennoscandia, the cyclic dynamics of rodents strongly affect many other species, including ground-nesting birds such as ptarmigan. According to the ‘alternative prey hypothesis’ (APH), the densities of ground-nesting birds and rodents are positively associated due to predator–prey dynamics and prey-switching. However, it remains unclear how the strength of these predator-mediated interactions change along a climatic harshness gradient in comparison with the effects of climatic variation. We built a hierarchical Bayesian model to estimate the sensitivity of ptarmigan populations to interannual variation in climate and rodent occurrence across Norway during 2007–2017. Ptarmigan abundance was positively linked with rodent occurrence, consistent with the APH. Moreover, we found that the link between ptarmigan abundance and rodent dynamics was strongest in colder regions. Our study highlights how species interactions play an important role in population dynamics of species at high latitudes and suggests that they can become even more important in the most climatically harsh regions.
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Affiliation(s)
- Diana E Bowler
- Department of Ecosystem Services, German Centre for Integrative Biodiversity Research (iDiv), Putschstr. 4, 04103 Leipzig, Germany.,Institute of Biodiversity, Friedrich Schiller University Jena, Dornburger Straße 159, 07743 Jena, Germany.,Department of Ecosystem Services, Helmholtz Center for Environmental Research-UFZ, Permoserstraße 15, 04318 Leipzig, Germany.,Department of Terrestrial Biodiversity, Norwegian Institute for Nature Research, P.O. 5685 Torgarden, 7485 Trondheim, Norway
| | - Mikkel A J Kvasnes
- Department of Terrestrial Biodiversity, Norwegian Institute for Nature Research, P.O. 5685 Torgarden, 7485 Trondheim, Norway
| | - Hans C Pedersen
- Department of Terrestrial Biodiversity, Norwegian Institute for Nature Research, P.O. 5685 Torgarden, 7485 Trondheim, Norway
| | - Brett K Sandercock
- Department of Terrestrial Biodiversity, Norwegian Institute for Nature Research, P.O. 5685 Torgarden, 7485 Trondheim, Norway
| | - Erlend B Nilsen
- Department of Terrestrial Biodiversity, Norwegian Institute for Nature Research, P.O. 5685 Torgarden, 7485 Trondheim, Norway.,Nord University, Faculty of Biosciences and Aquaculture, Steinkjer, Norway
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Henden J, Ehrich D, Soininen EM, Ims RA. Accounting for food web dynamics when assessing the impact of mesopredator control on declining prey populations. J Appl Ecol 2020. [DOI: 10.1111/1365-2664.13793] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- John‐André Henden
- Department of Arctic and Marine Biology UiT The Arctic University of Norway Tromsø Norway
| | - Dorothee Ehrich
- Department of Arctic and Marine Biology UiT The Arctic University of Norway Tromsø Norway
| | - Eeva M. Soininen
- Department of Arctic and Marine Biology UiT The Arctic University of Norway Tromsø Norway
| | - Rolf A. Ims
- Department of Arctic and Marine Biology UiT The Arctic University of Norway Tromsø Norway
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