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Journal Article•DOI•

Aligning population models with data: Adaptive management for big game harvests

Mariana B. Nagy-Reis1, Jody R. Reimer2, Mark A. Lewis1, William F. Jensen, Mark S. Boyce1 •
University of Alberta1, University of Utah2
01 Jan 2021-Global Ecology and Conservation (Elsevier BV)-Vol. 26
TL;DR: In this paper, the authors present a density-dependent population dynamics model that can be used in conjunction with adaptive management to optimize big game management, designed to use data commonly collected by state and provincial wildlife agencies.
About: This article is published in Global Ecology and Conservation.The article was published on 2021-01-01 and is currently open access. It has received 1 citations till now. The article focuses on the topics: Maximum sustainable yield & Population.
Citations
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Posted Content•DOI•
Bayesian species distribution models integrate presence-only and presence-absence data to predict deer distribution and relative abundance

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Virginia Morera-Pujol, Philip S. Mostert, Kilian J Murphy, Tim Burkitt, Barry Coad, Barry J. McMahon, Maarten Nieuwenhuis, Kevin Morelle, Alastair Ward, Simone Ciuti 
23 May 2022-bioRxiv
TL;DR: In this article , the authors applied integrated species distribution models (ISDMs) to empirical data, using presence-absence and presence-only data for the three main deer species in Ireland: red, fallow and sika deer.
Abstract: The use of georeferenced information on the presence of a species to predict its distribution across a geographic area is one of the most common tools in management and conservation. The collection of high-quality presence-absence data through structured surveys is, however, expensive, and managers usually have more abundant low-quality presence-only data collected by citizen scientists, opportunistic observations, and culling returns for game species. Integrated Species Distribution Models (ISDMs) have been developed to make the most of the data available by combining the higher-quality, but usually less abundant and more spatially restricted presence-absence data, with the lower quality, unstructured, but usually more extensive and abundant presence-only data. Joint-likelihood ISDMs can be run in a Bayesian context using INLA (Integrated Nested Laplace Approximation) methods that allow the addition of a spatially structured random effect to account for data spatial autocorrelation. These models, however, have only been applied to simulated data so far. Here, for the first time, we apply this approach to empirical data, using presence-absence and presence-only data for the three main deer species in Ireland: red, fallow and sika deer. We collated all deer data available for the past 15 years and fitted models predicting distribution and relative abundance at a 25 km2 resolution across the island. Models’ predictions were associated to spatial estimate of uncertainty, allowing us to assess the quality of the model and the effect that data scarcity has on the certainty of predictions. Furthermore, we validated the three species-specific models using independent deer hunting returns. Our work clearly demonstrates the applicability of spatially-explicit ISDMs to empirical data in a Bayesian context, providing a blueprint for managers to exploit unused and seemingly unusable data that can, when modelled with the proper tools, serve to inform management and conservation policies.

5 citations

References
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Journal Article•
R: A language and environment for statistical computing.

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R Core Team
01 Jan 2014-MSOR connections
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
Abstract: Copyright (©) 1999–2012 R Foundation for Statistical Computing. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this manual into another language, under the above conditions for modified versions, except that this permission notice may be stated in a translation approved by the R Core Team.

272,030 citations

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Adaptive Environmental Assessment and Management

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C. S. Holling
01 Sep 2005
TL;DR: In this article, various methods of environmental impact assessment as a guide to design of new environmental development and management projects are discussed. But the authors do not reject the concept of the environmental impact analysis but rather stress the need for fundamental understanding of the structure and dynamics of ecosystems.
Abstract: This book is on the various methods of environmental impact assessment as a guide to design of new environmental development and management projects. This approach surveys the features of the environment likely to be affected by the developments under consideration, analyses the information collected, tries to predict the impact of these developments and lays down guidelines or rules for their management. This book is concerned with practical problems, e.g. development in Canada, the management of fisheries, pest control, etc. It is devoted to a general understanding of environmental systems through methods that have worked in the real world with its many uncertainties. It does not reject the concept of environmental impact analysis but rather stresses the need for fundamental understanding of the structure and dynamics of ecosystems.

3,437 citations

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Matrix Population Models

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Hal Caswell1•
Woods Hole Oceanographic Institution1
01 Jan 1989
TL;DR: Matrix population models are discrete-time structured population models in which individuals are classified into discrete stages (age classes, size classes, developmental stages, spatial locations, etc.) as discussed by the authors.
Abstract: Matrix population models are discrete-time structured population models in which individuals are classified into discrete stages (age classes, size classes, developmental stages, spatial locations, etc.).

2,881 citations

Journal Article•DOI•
Population dynamics of large herbivores: variable recruitment with constant adult survival.

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Jean-Michel Gaillard1, Marco Festa-Bianchet1, Nigel G. Yoccoz1•
University of Lyon1
01 Feb 1998-Trends in Ecology and Evolution
TL;DR: High yearly variability in juvenile survival may play a predominant role in population dynamics and the pattern of high and stable adult survival and variable juvenile survival is observed in contrasting environments.
Abstract: Recent long-term studies of population ecology of large herbivorous mammals suggest that survival of prime-aged females varies little from year to year and across populations. Juvenile survival, on the other hand, varies considerably from year to year. The pattern of high and stable adult survival and variable juvenile survival is observed in contrasting environments, independently of the main proximal sources of mortality and regardless of whether mortality is stochastic or density-dependent. High yearly variability in juvenile survival may play a predominant role in population dynamics.

1,234 citations

"Aligning population models with dat..." refers background in this paper

  • ..., fecundity), and lastly adult survival (Gaillard et al., 1998; Eberhardt 2002)....

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  • ...Limitations of data precluded finding a nonlinear term for density dependence but wewould expect that a larger dataset is likely to reveal more detail in the structure of density dependence (Fowler 1987; Gaillard et al., 1998)....

    [...]

  • ...(3) and (4)), because density dependence most strongly affects juvenile survival and adult fecundity (Gaillard et al., 1998; Eberhardt 2002)....

    [...]

Journal Article•DOI•
Adaptive management of natural resources--framework and issues.

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Byron K. Williams1•
United States Geological Survey1
01 May 2011-Journal of Environmental Management
TL;DR: Key elements, processes, and issues in adaptive decision making are highlighted in terms of this framework, and special emphasis is given to the question of geographic scale, the difficulties presented by non-stationarity, and organizational challenges in implementing adaptive management.

556 citations

"Aligning population models with dat..." refers background or methods in this paper

  • ...Another essential step in active adaptive management is monitoring the population of interest, which brings key information for adjusting policies as part of the interactive learning process (White 2001; Williams 2011)....

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  • ...…license (http://creativecommons.org/ advantages of adaptive management and its applications in fisheries, forestry, and waterfowl management (Walters 1986; Holling 1978; Nichols et al. 1995, 2007; Williams 2011), this approach seldom has been applied to big game populations (Varley and Boyce 2006)....

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  • ...Uncertainty typically is associated with limited ecological understanding of population responses to alternative management actions (Williams 2011)....

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  • ...advantages of adaptive management and its applications in fisheries, forestry, and waterfowl management (Walters 1986; Holling 1978; Nichols et al. 1995, 2007; Williams 2011), this approach seldom has been applied to big game populations (Varley and Boyce 2006)....

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  • ...Using the iterative process of manipulating populations, monitoring their response, and comparing it to model predictions, managers would be able to reduce uncertainties associated with parameter estimation, a major component of adaptive management (Williams 2011)....

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