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Lachish S, Brandell EE, Craft ME, Dobson AP, Hudson PJ, MacNulty DR, Coulson T. Investigating the Dynamics of Elk Population Size and Body Mass in a Seasonal Environment Using a Mechanistic Integral Projection Model. Am Nat 2020; 196:E23-E45. [PMID: 32673097 DOI: 10.1086/708723] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Environmentally mediated changes in body size often underlie population responses to environmental change, yet this is not a universal phenomenon. Understanding when phenotypic change underlies population responses to environmental change is important for obtaining insights and robust predictions of population dynamics in a changing world. We develop a dynamic integral projection model that mechanistically links environmental conditions to demographic rates and phenotypic traits (body size) via changes in resource availability and individual energetics. We apply the model to the northern Yellowstone elk population and explore population responses to changing patterns of seasonality, incorporating the interdependence of growth, demography, and density-dependent processes operating through population feedback on available resources. We found that small changes in body size distributions can have large impacts on population dynamics but need not cause population responses to environmental change. Environmental changes that altered demographic rates directly, via increasing or decreasing resource availability, led to large population impacts in the absence of substantial changes to body size distributions. In contrast, environmentally driven shifts in body size distributions could occur with little consequence for population dynamics when the effect of environmental change on resource availability was small and seasonally restricted and when strong density-dependent processes counteracted expected population responses. These findings highlight that a robust understanding of how associations between body size and demography influence population responses to environmental change will require knowledge of the shape of the relationship between phenotypic distributions and vital rates, the population status with regard to its carrying capacity, and importantly the nature of the environmentally driven change in body size and carrying capacity.
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Cotterill GG, Cross PC, Middleton AD, Rogerson JD, Scurlock BM, du Toit JT. Hidden cost of disease in a free-ranging ungulate: brucellosis reduces mid-winter pregnancy in elk. Ecol Evol 2018; 8:10733-10742. [PMID: 30519402 PMCID: PMC6262735 DOI: 10.1002/ece3.4521] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 08/06/2018] [Accepted: 08/19/2018] [Indexed: 11/08/2022] Open
Abstract
Demonstrating disease impacts on the vital rates of free-ranging mammalian hosts typically requires intensive, long-term study. Evidence for chronic pathogens affecting reproduction but not survival is rare, but has the potential for wide-ranging effects. Accurately quantifying disease-associated reductions in fecundity is important for advancing theory, generating accurate predictive models, and achieving effective management. We investigated the impacts of brucellosis (Brucella abortus) on elk (Cervus canadensis) productivity using serological data from over 6,000 captures since 1990 in the Greater Yellowstone Ecosystem, USA. Over 1,000 of these records included known age and pregnancy status. Using Bayesian multilevel models, we estimated the age-specific pregnancy probabilities of exposed and naïve elk. We then used repeat-capture data to investigate the full effects of the disease on life history. Brucellosis exposure reduced pregnancy rates of elk captured in mid- and late-winter. In an average year, we found 60% of exposed 2-year-old elk were pregnant compared to 91% of their naïve counterparts (a 31 percentage point reduction, 89% HPDI = 20%-42%), whereas exposed 3- to 9-year-olds were 7 percentage points less likely to be pregnant than naïve elk of their same age (89% HPDI = 2%-11%). We found these reduced rates of pregnancy to be independent from disease-induced abortions, which afflict a portion of exposed elk. We estimate that the combination of reduced pregnancy by mid-winter and the abortions following mid-winter reduces the reproductive output of exposed female elk by 24%, which affects population dynamics to a similar extent as severe winters or droughts. Exposing hidden reproductive costs of disease is essential to avoid conflating them with the effects of climate and predation. Such reproductive costs cause complex population dynamics, and the magnitude of the effect we found should drive a strong selection gradient if there is heritable resistance.
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Affiliation(s)
| | - Paul C. Cross
- U.S. Geological SurveyNorthern Rocky Mountain Science CenterBozemanMontana
| | - Arthur D. Middleton
- Department of Environmental Science, Policy and ManagementUniversity of CaliforniaBerkeleyCalifornia
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Ossi F, Gaillard JM, Hebblewhite M, Morellet N, Ranc N, Sandfort R, Kroeschel M, Kjellander P, Mysterud A, Linnell JDC, Heurich M, Soennichsen L, Sustr P, Berger A, Rocca M, Urbano F, Cagnacci F. Plastic response by a small cervid to supplemental feeding in winter across a wide environmental gradient. Ecosphere 2017. [DOI: 10.1002/ecs2.1629] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Affiliation(s)
- Federico Ossi
- Biodiversity and Molecular Ecology Department; IASMA Research and Innovation Centre; Fondazione Edmund Mach; Via Mach 1 38010 San Michele all'Adige Trentino Italy
- UMR CNRS 5558 “Biometrie et Biologie Evolutive”; Université Claude Bernard Lyon1; Bat G. Mendel 43 Bd du 11 Novembre 1918 69622 Villeurbanne Cedex France
| | - Jean-Michel Gaillard
- UMR CNRS 5558 “Biometrie et Biologie Evolutive”; Université Claude Bernard Lyon1; Bat G. Mendel 43 Bd du 11 Novembre 1918 69622 Villeurbanne Cedex France
| | - Mark Hebblewhite
- Biodiversity and Molecular Ecology Department; IASMA Research and Innovation Centre; Fondazione Edmund Mach; Via Mach 1 38010 San Michele all'Adige Trentino Italy
- Wildlife Biology Program; Department of Ecosystem and Conservation Sciences; University of Montana; Missoula Montana 59812 USA
| | - Nicolas Morellet
- UR35 Comportement et Écologie de la Faune Sauvage; Institut National de la Recherche Agronomique (INRA); B.P. 52627 F-31326 Castanet-Tolosan France
| | - Nathan Ranc
- Biodiversity and Molecular Ecology Department; IASMA Research and Innovation Centre; Fondazione Edmund Mach; Via Mach 1 38010 San Michele all'Adige Trentino Italy
- Department of Organismic and Evolutionary Biology; Harvard University; 26 Oxford Street Cambridge Massachusetts 02138 USA
| | - Robin Sandfort
- Department of Integrative Biology and Biodiversity Research; Institute of Wildlife Biology and Game Management; University of Natural Resources and Life Sciences Vienna; Gregor-Mendel Straße 33 A-1180 Vienna Austria
| | - Max Kroeschel
- Chair of Wildlife Ecology and Wildlife Management; University of Freiburg; Fahnenbergplatz 79085 Freiburg Germany
- Forest Research Institute of Baden-Wuerttemberg; Wonnhaldestraße 4 79100 Freiburg Germany
| | - Petter Kjellander
- Grimsö Wildlife Research Station; Department of Ecology; Swedish University of Agricultural Science (SLU); SE-73091 Riddarhyttan Sweden
| | - Atle Mysterud
- Department of Bioscience; Centre for Ecological and Evolutionary Synthesis; University of Oslo; P.O. Box 1066, Blindern NO-0316 Oslo Norway
| | - John D. C. Linnell
- Norwegian Institute for Nature Research (NINA); P.O. Box 5685, Sluppen NO-7485 Trondheim Norway
| | - Marco Heurich
- Department of Conservation and Research; Bavarian Forest National Park; Freyunger Straße 2 94481 Grafenau Germany
- Chair of Wildlife Biology Ecology and Wildlife Management; University of Freiburg; Fahnenbergplatz 79085 Freiburg Germany
| | - Leif Soennichsen
- Mammal Research Institute; Waszkiewicza 1 17-230 Bialowieza Poland
| | - Pavel Sustr
- Department of Biodiversity Research; Global Change Research Centre; Belidla 986/4a Brno 60300 Czech Republic
| | - Anne Berger
- Leibniz-Institute for Zoo and Wildlife Research (IZW); Alfred-Kowalke-Straße 17 10315 Berlin Germany
| | - Michele Rocca
- Trentino Hunting Association; Via Guardini 41 38121 Trento Italy
| | | | - Francesca Cagnacci
- Biodiversity and Molecular Ecology Department; IASMA Research and Innovation Centre; Fondazione Edmund Mach; Via Mach 1 38010 San Michele all'Adige Trentino Italy
- Department of Organismic and Evolutionary Biology; Harvard University; 26 Oxford Street Cambridge Massachusetts 02138 USA
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Ebinger MR, Haroldson MA, van Manen FT, Costello CM, Bjornlie DD, Thompson DJ, Gunther KA, Fortin JK, Teisberg JE, Pils SR, White PJ, Cain SL, Cross PC. Detecting grizzly bear use of ungulate carcasses using global positioning system telemetry and activity data. Oecologia 2016; 181:695-708. [PMID: 26971522 DOI: 10.1007/s00442-016-3594-5] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2015] [Accepted: 02/22/2016] [Indexed: 11/24/2022]
Abstract
Global positioning system (GPS) wildlife collars have revolutionized wildlife research. Studies of predation by free-ranging carnivores have particularly benefited from the application of location clustering algorithms to determine when and where predation events occur. These studies have changed our understanding of large carnivore behavior, but the gains have concentrated on obligate carnivores. Facultative carnivores, such as grizzly/brown bears (Ursus arctos), exhibit a variety of behaviors that can lead to the formation of GPS clusters. We combined clustering techniques with field site investigations of grizzly bear GPS locations (n = 732 site investigations; 2004-2011) to produce 174 GPS clusters where documented behavior was partitioned into five classes (large-biomass carcass, small-biomass carcass, old carcass, non-carcass activity, and resting). We used multinomial logistic regression to predict the probability of clusters belonging to each class. Two cross-validation methods-leaving out individual clusters, or leaving out individual bears-showed that correct prediction of bear visitation to large-biomass carcasses was 78-88 %, whereas the false-positive rate was 18-24 %. As a case study, we applied our predictive model to a GPS data set of 266 bear-years in the Greater Yellowstone Ecosystem (2002-2011) and examined trends in carcass visitation during fall hyperphagia (September-October). We identified 1997 spatial GPS clusters, of which 347 were predicted to be large-biomass carcasses. We used the clustered data to develop a carcass visitation index, which varied annually, but more than doubled during the study period. Our study demonstrates the effectiveness and utility of identifying GPS clusters associated with carcass visitation by a facultative carnivore.
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Affiliation(s)
- Michael R Ebinger
- College of Forestry and Conservation, University of Montana, University Hall, Room 309, Missoula, MT, 59812, USA. .,Interagency Grizzly Bear Study Team, Northern Rocky Mountain Science Center, US Geological Survey, 2327 University Way, Suite 2, Bozeman, MT, 59715, USA. .,Department of Ecology, Montana State University, P.O. Box 173460, Bozeman, MT, 59717, USA.
| | - Mark A Haroldson
- Interagency Grizzly Bear Study Team, Northern Rocky Mountain Science Center, US Geological Survey, 2327 University Way, Suite 2, Bozeman, MT, 59715, USA
| | - Frank T van Manen
- Interagency Grizzly Bear Study Team, Northern Rocky Mountain Science Center, US Geological Survey, 2327 University Way, Suite 2, Bozeman, MT, 59715, USA
| | - Cecily M Costello
- College of Forestry and Conservation, University of Montana, University Hall, Room 309, Missoula, MT, 59812, USA.,Montana Fish, Wildlife and Parks, 490 N. Meridian Road, Kalispell, MT, 59901, USA
| | - Daniel D Bjornlie
- Large Carnivore Section, Wyoming Game and Fish Department, 260 Buena Vista, Lander, WY, 82520, USA
| | - Daniel J Thompson
- Large Carnivore Section, Wyoming Game and Fish Department, 260 Buena Vista, Lander, WY, 82520, USA
| | - Kerry A Gunther
- Bear Management Office, Yellowstone Center for Resources, Yellowstone National Park, P.O. Box 168, Yellowstone National Park, WY, 82190, USA
| | - Jennifer K Fortin
- College of Forestry and Conservation, University of Montana, University Hall, Room 309, Missoula, MT, 59812, USA.,School of Biological Sciences, Washington State University, P.O. Box 644236, Pullman, WA, 99164-4236, USA
| | - Justin E Teisberg
- School of Biological Sciences, Washington State University, P.O. Box 644236, Pullman, WA, 99164-4236, USA.,Grizzly Bear Recovery Program, US Fish and Wildlife Service, 385 Fish Hatchery Road, Libby, MT, 59923, USA
| | - Shannon R Pils
- Interagency Grizzly Bear Study Team, Northern Rocky Mountain Science Center, US Geological Survey, 2327 University Way, Suite 2, Bozeman, MT, 59715, USA.,Shoshone National Forest, US Forest Service, Wapiti Ranger District, 203A Yellowstone Avenue, Cody, WY, 82414, USA
| | - P J White
- National Park Service, Yellowstone Center for Resources, Yellowstone National Park, P.O. Box 168, Yellowstone National Park, WY, 82190, USA
| | - Steven L Cain
- Grand Teton National Park, P.O. Box 170, Moose, WY, 83012, USA.,Grand Teton National Park Foundation, P.O. Box 249, Moose, WY, 83012, USA
| | - Paul C Cross
- Interagency Grizzly Bear Study Team, Northern Rocky Mountain Science Center, US Geological Survey, 2327 University Way, Suite 2, Bozeman, MT, 59715, USA
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