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Johnson‐Bice SM, Ferguson JM, Erb JD, Gable TD, Windels SK. Ecological forecasts reveal limitations of common model selection methods: predicting changes in beaver colony densities. Ecol Appl 2021; 31:e02198. [PMID: 32583507 PMCID: PMC7816246 DOI: 10.1002/eap.2198] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2019] [Revised: 03/13/2020] [Accepted: 03/30/2020] [Indexed: 05/20/2023]
Abstract
Over the past two decades, there have been numerous calls to make ecology a more predictive science through direct empirical assessments of ecological models and predictions. While the widespread use of model selection using information criteria has pushed ecology toward placing a higher emphasis on prediction, few attempts have been made to validate the ability of information criteria to correctly identify the most parsimonious model with the greatest predictive accuracy. Here, we used an ecological forecasting framework to test the ability of information criteria to accurately predict the relative contribution of density dependence and density-independent factors (forage availability, harvest, weather, wolf [Canis lupus] density) on inter-annual fluctuations in beaver (Castor canadensis) colony densities. We modeled changes in colony densities using a discrete-time Gompertz model, and assessed the performance of four models using information criteria values: density-independent models with (1) and without (2) environmental covariates; and density-dependent models with (3) and without (4) environmental covariates. We then evaluated the forecasting accuracy of each model by withholding the final one-third of observations from each population and compared observed vs. predicted densities. Information criteria and our forecasting accuracy metrics both provided strong evidence of compensatory density dependence in the annual dynamics of beaver colony densities. However, despite strong within-sample performance by the most complex model (density-dependent with covariates) as determined using information criteria, hindcasts of colony densities revealed that the much simpler density-dependent model without covariates performed nearly as well predicting out-of-sample colony densities. The hindcast results indicated that the complex model over-fit our data, suggesting that parameters identified by information criteria as important predictor variables are only marginally valuable for predicting landscape-scale beaver colony dynamics. Our study demonstrates the importance of evaluating ecological models and predictions with long-term data and revealed how a known limitation of information criteria (over-fitting of complex models) can affect our interpretation of ecological dynamics. While incorporating knowledge of the factors that influence animal population dynamics can improve population forecasts, we suggest that comparing forecast performance metrics can likewise improve our knowledge of the factors driving population dynamics.
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Affiliation(s)
- Sean M. Johnson‐Bice
- Department of Biological SciencesUniversity of Manitoba50 Sifton RoadWinnipegManitobaR3T 2N2Canada
- Natural Resources Research InstituteUniversity of Minnesota Duluth5013 Miller Trunk HighwayDuluthMinnesota55812USA
| | - Jake M. Ferguson
- Department of BiologyUniversity of Hawai`i at Mānoa2538 McCarthy MallHonoluluHawaii96822USA
| | - John D. Erb
- Forest Wildlife Populations and Research GroupMinnesota Department of Natural Resources1201 E. highway 2Grand RapidsMinnesota55744USA
| | - Thomas D. Gable
- Department of Fisheries, Wildlife and Conservation BiologyUniversity of Minnesota Twin Cities2003 Upper Buford CircleSt. PaulMinnesota55108USA
| | - Steve K. Windels
- Natural Resources Research InstituteUniversity of Minnesota Duluth5013 Miller Trunk HighwayDuluthMinnesota55812USA
- Department of Fisheries, Wildlife and Conservation BiologyUniversity of Minnesota Twin Cities2003 Upper Buford CircleSt. PaulMinnesota55108USA
- Voyageurs National Park360 Highway 11 E.International FallsMinnesota56649USA
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2
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Abstract
Variability in the environment defines the structure and dynamics of all living systems, from organisms to ecosystems. Species have evolved traits and strategies that allow them to detect, exploit and predict the changing environment. These traits allow organisms to maintain steady internal conditions required for physiological functioning through feedback mechanisms that allow internal conditions to remain at or near a set-point despite a fluctuating environment. In addition to feedback, many organisms have evolved feedforward processes, which allow them to adjust in anticipation of an expected future state of the environment. Here we provide a framework describing how feedback and feedforward mechanisms operating within organisms can generate effects across scales of organization, and how they allow living systems to persist in fluctuating environments. Daily, seasonal and multi-year cycles provide cues that organisms use to anticipate changes in physiologically relevant environmental conditions. Using feedforward mechanisms, organisms can exploit correlations in environmental variables to prepare for anticipated future changes. Strategies to obtain, store and act on information about the conditional nature of future events are advantageous and are evidenced in widespread phenotypes such as circadian clocks, social behaviour, diapause and migrations. Humans are altering the ways in which the environment fluctuates, causing correlations between environmental variables to become decoupled, decreasing the reliability of cues. Human-induced environmental change is also altering sensory environments and the ability of organisms to detect cues. Recognizing that living systems combine feedback and feedforward processes is essential to understanding their responses to current and future regimes of environmental fluctuations. This article is part of the theme issue ‘Integrative research perspectives on marine conservation’.
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Affiliation(s)
- Joey R Bernhardt
- Department of Aquatic Ecology, Eawag: Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland.,Department of Biology, Quebec Centre for Biodiversity Science, McGill University, Montreal, Canada H3A 1B1
| | - Mary I O'Connor
- Department of Zoology and Biodiversity Research Centre, University of British Columbia, 6270 University Boulevard, Vancouver, Canada V6T 1Z4
| | - Jennifer M Sunday
- Department of Biology, Quebec Centre for Biodiversity Science, McGill University, Montreal, Canada H3A 1B1
| | - Andrew Gonzalez
- Department of Biology, Quebec Centre for Biodiversity Science, McGill University, Montreal, Canada H3A 1B1
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3
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Rescan M, Grulois D, Ortega-Aboud E, Chevin LM. Phenotypic memory drives population growth and extinction risk in a noisy environment. Nat Ecol Evol 2020; 4:193-201. [PMID: 31988445 DOI: 10.1038/s41559-019-1089-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Accepted: 12/19/2019] [Indexed: 11/24/2022]
Abstract
Random environmental fluctuations pose major threats to wild populations. As patterns of environmental noise are themselves altered by global change, there is growing need to identify general mechanisms underlying their effects on population dynamics. This notably requires understanding and predicting population responses to the color of environmental noise, i.e. its temporal autocorrelation pattern. Here, we show experimentally that environmental autocorrelation has a large influence on population dynamics and extinction rates, which can be predicted accurately provided that a memory of past environment is accounted for. We exposed near to 1000 lines of the microalgae Dunaliella salina to randomly fluctuating salinity, with autocorrelation ranging from negative to highly positive. We found lower population growth, and twice as many extinctions, under lower autocorrelation. These responses closely matched predictions based on a tolerance curve with environmental memory, showing that non-genetic inheritance can be a major driver of population dynamics in randomly fluctuating environments.
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Pilowsky JA, Dahlgren JP. Incorporating the temporal autocorrelation of demographic rates into structured population models. OIKOS 2019. [DOI: 10.1111/oik.06438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Julia A. Pilowsky
- Dept of Ecology and Evolutionary Biology, Univ. of Adelaide, Benham Laboratories North Terrace Campus AU‐5005 Adelaide South Australia Australia
- Center for Macroecology, Evolution and Climate, Univ. of Copenhagen Universitetsparken 15 DK‐2100 7 Copenhagen Denmark
| | - Johan P. Dahlgren
- Dept of Biology, SDU Interdisciplinary Centre on Population Dynamics, Univ. of Southern Denmark Odense Denmark
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Ferguson JM, Taper ML, Zenil-Ferguson R, Jasieniuk M, Maxwell BD. Incorporating Parameter Estimability Into Model Selection. Front Ecol Evol 2019. [DOI: 10.3389/fevo.2019.00427] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Kilduff DP, Botsford LW, Thompson LC. Frequency content of environmental variability and extinction risk of age-structured populations: Chinook salmon (Oncorhynchus tschawytscha) as an example. THEOR ECOL-NETH 2019; 12:145-54. [DOI: 10.1007/s12080-018-0401-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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7
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Ho EKH, Agrawal AF. Mutation accumulation in selfing populations under fluctuating selection. Evolution 2018; 72:1759-1772. [DOI: 10.1111/evo.13553] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Revised: 06/26/2018] [Accepted: 07/01/2018] [Indexed: 12/13/2022]
Affiliation(s)
- Eddie K. H. Ho
- Department of Ecology and Evolutionary Biology University of Toronto 25 Willcocks Street Toronto ON M5S 3B2 Canada
| | - Aneil F. Agrawal
- Department of Ecology and Evolutionary Biology University of Toronto 25 Willcocks Street Toronto ON M5S 3B2 Canada
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Lee AM, Saether BE, Markussen SS, Engen S. Modelling time to population extinction when individual reproduction is autocorrelated. Ecol Lett 2017; 20:1385-1394. [PMID: 28925038 DOI: 10.1111/ele.12834] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2017] [Revised: 04/02/2017] [Accepted: 08/08/2017] [Indexed: 11/30/2022]
Abstract
In nature, individual reproductive success is seldom independent from year to year, due to factors such as reproductive costs and individual heterogeneity. However, population projection models that incorporate temporal autocorrelations in individual reproduction can be difficult to parameterise, particularly when data are sparse. We therefore examine whether such models are necessary to avoid biased estimates of stochastic population growth and extinction risk, by comparing output from a matrix population model that incorporates reproductive autocorrelations to output from a standard age-structured matrix model that does not. We use a range of parameterisations, including a case study using moose data, treating probabilities of switching reproductive class as either fixed or fluctuating. Expected time to extinction from the two models is found to differ by only small amounts (under 10%) for most parameterisations, indicating that explicitly accounting for individual reproductive autocorrelations is in most cases not necessary to avoid bias in extinction estimates.
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Affiliation(s)
- Aline Magdalena Lee
- Centre for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Bernt-Erik Saether
- Centre for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Stine Svalheim Markussen
- Centre for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Steinar Engen
- Centre for Biodiversity Dynamics, Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway
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Ferguson JM, Reichert BE, Fletcher RJ, Jager HI. Detecting population-environmental interactions with mismatched time series data. Ecology 2017; 98:2813-2822. [PMID: 28759123 DOI: 10.1002/ecy.1966] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Revised: 06/14/2017] [Accepted: 07/10/2017] [Indexed: 11/08/2022]
Abstract
Time series analysis is an essential method for decomposing the influences of density and exogenous factors such as weather and climate on population regulation. However, there has been little work focused on understanding how well commonly collected data can reconstruct the effects of environmental factors on population dynamics. We show that, analogous to similar scale issues in spatial data analysis, coarsely sampled temporal data can fail to detect covariate effects when interactions occur on timescales that are fast relative to the survey period. We propose a method for modeling mismatched time series data that couples high-resolution environmental data to low-resolution abundance data. We illustrate our approach with simulations and by applying it to Florida's southern Snail kite population. Our simulation results show that our method can reliably detect linear environmental effects and that detecting nonlinear effects requires high-resolution covariate data even when the population turnover rate is slow. In the Snail kite analysis, our approach performed among the best in a suite of previously used environmental covariates explaining Snail kite dynamics and was able to detect a potential phenological shift in the environmental dependence of Snail kites. Our work provides a statistical framework for reliably detecting population-environment interactions from coarsely surveyed time series. An important implication of this work is that the low predictability of animal population growth by weather variables found in previous studies may be due, in part, to how these data are utilized as covariates.
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Affiliation(s)
- Jake M Ferguson
- National Institute of Mathematical and Biology Synthesis, University of Tennessee, Knoxville, Tennessee, 37996, USA.,Center for Modeling Complex Interactions, University of Idaho, 875 Perimeter Drive, Moscow, Idaho, 83844, USA
| | - Brian E Reichert
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, 32611, USA
| | - Robert J Fletcher
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, 32611, USA
| | - Henriëtte I Jager
- Environmental Sciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, Tennessee, 37830, USA
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Pardo D, Jenouvrier S, Weimerskirch H, Barbraud C. Effect of extreme sea surface temperature events on the demography of an age-structured albatross population. Philos Trans R Soc Lond B Biol Sci 2017; 372:20160143. [PMID: 28483873 PMCID: PMC5434094 DOI: 10.1098/rstb.2016.0143] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/07/2017] [Indexed: 01/08/2023] Open
Abstract
Climate changes include concurrent changes in environmental mean, variance and extremes, and it is challenging to understand their respective impact on wild populations, especially when contrasted age-dependent responses to climate occur. We assessed how changes in mean and standard deviation of sea surface temperature (SST), frequency and magnitude of warm SST extreme climatic events (ECE) influenced the stochastic population growth rate log(λs) and age structure of a black-browed albatross population. For changes in SST around historical levels observed since 1982, changes in standard deviation had a larger (threefold) and negative impact on log(λs) compared to changes in mean. By contrast, the mean had a positive impact on log(λs). The historical SST mean was lower than the optimal SST value for which log(λs) was maximized. Thus, a larger environmental mean increased the occurrence of SST close to this optimum that buffered the negative effect of ECE. This 'climate safety margin' (i.e. difference between optimal and historical climatic conditions) and the specific shape of the population growth rate response to climate for a species determine how ECE affect the population. For a wider range in SST, both the mean and standard deviation had negative impact on log(λs), with changes in the mean having a greater effect than the standard deviation. Furthermore, around SST historical levels increases in either mean or standard deviation of the SST distribution led to a younger population, with potentially important conservation implications for black-browed albatrosses.This article is part of the themed issue 'Behavioural, ecological and evolutionary responses to extreme climatic events'.
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Affiliation(s)
- Deborah Pardo
- Centre d'Etudes Biologiques de Chizé, UMR7372 CNRS, 79360 Villiers-en-Bois, France
- British Antarctic Survey, Madingley Road High Cross, Cambridge CB3 0ET, UK
| | - Stéphanie Jenouvrier
- Centre d'Etudes Biologiques de Chizé, UMR7372 CNRS, 79360 Villiers-en-Bois, France
- Woods Hole Oceanographic Institution, Mailstop 50, Woods Hole, MA 02543, USA
| | - Henri Weimerskirch
- Centre d'Etudes Biologiques de Chizé, UMR7372 CNRS, 79360 Villiers-en-Bois, France
| | - Christophe Barbraud
- Centre d'Etudes Biologiques de Chizé, UMR7372 CNRS, 79360 Villiers-en-Bois, France
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