Accounting for the temporal variation of spatial effect improves inference and projection of population dynamics models

被引:5
|
作者
Zhao, Qing [1 ]
Boomer, G. Scott [2 ]
Silverman, Emily [2 ]
Fleming, Kathy [2 ]
机构
[1] Colorado State Univ, Dept Fish Wildlife & Conservat Biol, Ft Collins, CO 80523 USA
[2] US Fish & Wildlife Serv, Div Migratory Bird Management, 11510 Amer Holly Dr, Laurel, MD 20708 USA
关键词
Abundance; Conservation planning; Ecological forecast; Hierarchical bayesian framework; Spatial model; System shift; CLIMATE-CHANGE; DENSITY-DEPENDENCE; AUTOCORRELATION; REGRESSION; ECOLOGY; TIME; UNCERTAINTY; PREDICTION; MANAGEMENT; WINBUGS;
D O I
10.1016/j.ecolmodel.2017.07.019
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Population dynamics models incorporating density dependence and habitat heterogeneity are useful tools to explain and project the spatiotemporal variation of wildlife abundance. Despite their wide application in ecology and conservation biology, the inference and projection of these models may be problematic when residual spatial auto correlation (SAC) is found. We aimed to improve the inference and projection of population dynamics models by accounting for residual SAC. We considered three Gompertz models that incorporated density dependence and the effect of wetland habitat to explain and project the abundance of Mallard (Anas platyrhynchos). We compared a conventional model that did not account for residual SAC (ENV) with two novel models accounting for residual SAC, one incorporating a spatial effect (a spatially autocorrelated process error) that did not vary over time (STA) and the other incorporating a spatial effect that varied over time (DYN). We evaluated model inference using data from 1974 to 1998 and projection using data from 1999 to 2010. We then forecasted Mallard abundance from 2011 to 2100 under different levels of wetland habitat loss. The DYN model eliminated residual SAC and had better model fit than the ENV and STA models (Delta(D) over bar = 2498.3 and 1988.8, respectively). The projection coverage rate of the DYN model was the closest to the nominal value among the three models. The DYN model forecasted smaller areas with decrease in Mallard abundance under future wetland habitat loss than the ENV and STA models. The novel and conventional population dynamics models we considered in this study, combined with the practical model evaluation approach, can provide reliable inference and projection of wildlife abundance, and thus have wide application in ecological studies and conservation practices that aim to understand and project the spatiotemporal variation of wildlife abundance under environmental changes. In particular, when conservation decision-making is based on model projections, the DYN may be used to minimize the risk of reducing conservation effort in areas that still have high conservation value, due to its favorable projection performance. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:252 / 259
页数:8
相关论文
共 50 条
  • [1] Statistical modelling of annual variation for inference on stochastic population dynamics using Integral Projection Models
    Metcalf, C. Jessica E.
    Ellner, Stephen P.
    Childs, Dylan Z.
    Salguero-Gomez, Roberto
    Merow, Cory
    McMahon, Sean M.
    Jongejans, Eelke
    Rees, Mark
    [J]. METHODS IN ECOLOGY AND EVOLUTION, 2015, 6 (09): : 1007 - 1017
  • [2] REGIONAL POPULATION PROJECTION MODELS AND ACCOUNTING METHODS
    REES, PH
    [J]. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY, 1979, 142 : 223 - 255
  • [3] Embedding population dynamics models in inference
    Buckland, Stephen T.
    Newman, Ken B.
    Fernandez, Carmen
    Thomas, Len
    Harwood, John
    [J]. STATISTICAL SCIENCE, 2007, 22 (01) : 44 - 58
  • [4] An Integrated Framework of Population Change: Influential Factors, Spatial Dynamics, and Temporal Variation
    Chi, Guangqing
    Ventura, Stephen J.
    [J]. GROWTH AND CHANGE, 2011, 42 (04) : 549 - 570
  • [5] Robust inference in semiparametric spatial-temporal models
    Santos, Julius
    Barrios, Erniel
    [J]. COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2021, 50 (08) : 2266 - 2285
  • [6] Accounting for temporal change in multiple biodiversity patterns improves the inference of metacommunity processes
    Guzman, Laura Melissa
    Thompson, Patrick L.
    Viana, Duarte S.
    Vanschoenwinkel, Bram
    Horvath, Zsofia
    Ptacnik, Robert
    Jeliazkov, Alienor
    Gascon, Stephanie
    Lemmens, Pieter
    Anton-Pardo, Maria
    Langenheder, Silke
    De Meester, Luc
    Chase, Jonathan M.
    [J]. ECOLOGY, 2022, 103 (06)
  • [7] Incorporating spatial variation in density enhances the stability of simple population dynamics models
    Jaggi, S
    Joshi, A
    [J]. JOURNAL OF THEORETICAL BIOLOGY, 2001, 209 (02) : 249 - 255
  • [8] ACCOUNTING FOR SPATIAL RELATIONSHIPS IN MODELS OF INTERSTATE POPULATION MIGRATION
    CUSHING, BJ
    [J]. ANNALS OF REGIONAL SCIENCE, 1986, 20 (02): : 66 - 73
  • [9] Spatial and temporal changes in population distribution and population projection at county level in China
    Sang, Mei
    Jiang, Jing
    Huang, Xin
    Zhu, Feifei
    Wang, Qian
    [J]. HUMANITIES & SOCIAL SCIENCES COMMUNICATIONS, 2024, 11 (01):
  • [10] Spatial and temporal changes in population distribution and population projection at county level in China
    Mei Sang
    Jing Jiang
    Xin Huang
    Feifei Zhu
    Qian Wang
    [J]. Humanities and Social Sciences Communications, 11