Advances in global sensitivity analyses of demographic-based species distribution models to address uncertainties in dynamic landscapes

Developing a rigorous understanding of multiple global threats to species persistence requires the use of integrated modeling methods that capture processes which influence species distributions. Species distribution models (SDMs) coupled with population dynamics models can incorporate relationships...

Full description

Bibliographic Details
Main Authors: Ilona Naujokaitis-Lewis, Janelle M.R. Curtis
Format: Article
Language:English
Published: PeerJ Inc. 2016-07-01
Series:PeerJ
Subjects:
Online Access:https://peerj.com/articles/2204.pdf
id doaj-b21245b9f3a343c1b53e6ee112667f58
record_format Article
spelling doaj-b21245b9f3a343c1b53e6ee112667f582020-11-24T22:31:30ZengPeerJ Inc.PeerJ2167-83592016-07-014e220410.7717/peerj.2204Advances in global sensitivity analyses of demographic-based species distribution models to address uncertainties in dynamic landscapesIlona Naujokaitis-Lewis0Janelle M.R. Curtis1National Wildlife Research Centre, Carleton University, Environment and Climate Change Canada, Ottawa, Ontario, CanadaCentre for Applied Conservation Research, University of British Columbia, Vancouver, British Columbia, CanadaDeveloping a rigorous understanding of multiple global threats to species persistence requires the use of integrated modeling methods that capture processes which influence species distributions. Species distribution models (SDMs) coupled with population dynamics models can incorporate relationships between changing environments and demographics and are increasingly used to quantify relative extinction risks associated with climate and land-use changes. Despite their appeal, uncertainties associated with complex models can undermine their usefulness for advancing predictive ecology and informing conservation management decisions. We developed a computationally-efficient and freely available tool (GRIP 2.0) that implements and automates a global sensitivity analysis of coupled SDM-population dynamics models for comparing the relative influence of demographic parameters and habitat attributes on predicted extinction risk. Advances over previous global sensitivity analyses include the ability to vary habitat suitability across gradients, as well as habitat amount and configuration of spatially-explicit suitability maps of real and simulated landscapes. Using GRIP 2.0, we carried out a multi-model global sensitivity analysis of a coupled SDM-population dynamics model of whitebark pine (Pinus albicaulis) in Mount Rainier National Park as a case study and quantified the relative influence of input parameters and their interactions on model predictions. Our results differed from the one-at-time analyses used in the original study, and we found that the most influential parameters included the total amount of suitable habitat within the landscape, survival rates, and effects of a prevalent disease, white pine blister rust. Strong interactions between habitat amount and survival rates of older trees suggests the importance of habitat in mediating the negative influences of white pine blister rust. Our results underscore the importance of considering habitat attributes along with demographic parameters in sensitivity routines. GRIP 2.0 is an important decision-support tool that can be used to prioritize research, identify habitat-based thresholds and management intervention points to improve probability of species persistence, and evaluate trade-offs of alternative management options.https://peerj.com/articles/2204.pdfUncertaintyGlobal sensitivity analysisPicea speciesPopulation viability analysisDiseasePopulation dynamics
collection DOAJ
language English
format Article
sources DOAJ
author Ilona Naujokaitis-Lewis
Janelle M.R. Curtis
spellingShingle Ilona Naujokaitis-Lewis
Janelle M.R. Curtis
Advances in global sensitivity analyses of demographic-based species distribution models to address uncertainties in dynamic landscapes
PeerJ
Uncertainty
Global sensitivity analysis
Picea species
Population viability analysis
Disease
Population dynamics
author_facet Ilona Naujokaitis-Lewis
Janelle M.R. Curtis
author_sort Ilona Naujokaitis-Lewis
title Advances in global sensitivity analyses of demographic-based species distribution models to address uncertainties in dynamic landscapes
title_short Advances in global sensitivity analyses of demographic-based species distribution models to address uncertainties in dynamic landscapes
title_full Advances in global sensitivity analyses of demographic-based species distribution models to address uncertainties in dynamic landscapes
title_fullStr Advances in global sensitivity analyses of demographic-based species distribution models to address uncertainties in dynamic landscapes
title_full_unstemmed Advances in global sensitivity analyses of demographic-based species distribution models to address uncertainties in dynamic landscapes
title_sort advances in global sensitivity analyses of demographic-based species distribution models to address uncertainties in dynamic landscapes
publisher PeerJ Inc.
series PeerJ
issn 2167-8359
publishDate 2016-07-01
description Developing a rigorous understanding of multiple global threats to species persistence requires the use of integrated modeling methods that capture processes which influence species distributions. Species distribution models (SDMs) coupled with population dynamics models can incorporate relationships between changing environments and demographics and are increasingly used to quantify relative extinction risks associated with climate and land-use changes. Despite their appeal, uncertainties associated with complex models can undermine their usefulness for advancing predictive ecology and informing conservation management decisions. We developed a computationally-efficient and freely available tool (GRIP 2.0) that implements and automates a global sensitivity analysis of coupled SDM-population dynamics models for comparing the relative influence of demographic parameters and habitat attributes on predicted extinction risk. Advances over previous global sensitivity analyses include the ability to vary habitat suitability across gradients, as well as habitat amount and configuration of spatially-explicit suitability maps of real and simulated landscapes. Using GRIP 2.0, we carried out a multi-model global sensitivity analysis of a coupled SDM-population dynamics model of whitebark pine (Pinus albicaulis) in Mount Rainier National Park as a case study and quantified the relative influence of input parameters and their interactions on model predictions. Our results differed from the one-at-time analyses used in the original study, and we found that the most influential parameters included the total amount of suitable habitat within the landscape, survival rates, and effects of a prevalent disease, white pine blister rust. Strong interactions between habitat amount and survival rates of older trees suggests the importance of habitat in mediating the negative influences of white pine blister rust. Our results underscore the importance of considering habitat attributes along with demographic parameters in sensitivity routines. GRIP 2.0 is an important decision-support tool that can be used to prioritize research, identify habitat-based thresholds and management intervention points to improve probability of species persistence, and evaluate trade-offs of alternative management options.
topic Uncertainty
Global sensitivity analysis
Picea species
Population viability analysis
Disease
Population dynamics
url https://peerj.com/articles/2204.pdf
work_keys_str_mv AT ilonanaujokaitislewis advancesinglobalsensitivityanalysesofdemographicbasedspeciesdistributionmodelstoaddressuncertaintiesindynamiclandscapes
AT janellemrcurtis advancesinglobalsensitivityanalysesofdemographicbasedspeciesdistributionmodelstoaddressuncertaintiesindynamiclandscapes
_version_ 1725736847667101696