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

ABSTRACT: 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.

SUBMITTER: Naujokaitis-Lewis I

PROVIDER: S-EPMC4958004 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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

Naujokaitis-Lewis Ilona I Curtis Janelle M R JM

PeerJ 20160719

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 associ ...[more]

PMID: 27547529

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Project description:IntroductionTo reliably solve the EEG inverse problem, accurate EEG forward solutions based on a detailed, individual volume conductor model of the head are essential. A crucial-but often neglected-aspect in generating a volume conductor model is the choice of the tissue conductivities, as these may vary from subject to subject. In this study, we investigate the sensitivity of EEG forward and inverse solutions to tissue conductivity uncertainties for sources distributed over the whole cortex surface.MethodsWe employ a detailed five-compartment head model distinguishing skin, skull, cerebrospinal fluid, gray matter, and white matter, where we consider uncertainties of skin, skull, gray matter, and white matter conductivities. We use the finite element method (FEM) to calculate EEG forward solutions and goal function scans (GFS) as inverse approach. To be able to generate the large number of EEG forward solutions, we employ generalized polynomial chaos (gPC) expansions.ResultsFor sources up to a depth of 4 cm, we find the strongest influence on the signal topography of EEG forward solutions for the skull conductivity and a notable effect for the skin conductivity. For even deeper sources, e.g., located deep in the longitudinal fissure, we find an increasing influence of the white matter conductivity. The conductivity variations translate to varying source localizations particularly for quasi-tangential sources on sulcal walls, whereas source localizations of quasi-radial sources on the top of gyri are less affected. We find a strong correlation between skull conductivity and the variation of source localizations and especially the depth of the reconstructed source for quasi-tangential sources. We furthermore find a clear but weaker correlation between depth of the reconstructed source and the skin conductivity.DiscussionOur results clearly show the influence of tissue conductivity uncertainties on EEG source analysis. We find a particularly strong influence of skull and skin conductivity uncertainties.

Project description:Providing reliable environmental quality standards (EQSs) is a challenging issue in environmental risk assessment (ERA). These EQSs are derived from toxicity endpoints estimated from dose-response models to identify and characterize the environmental hazard of chemical compounds released by human activities. These toxicity endpoints include the classical x% effect/lethal concentrations at a specific time t (EC/LC(x, t)) and the new multiplication factors applied to environmental exposure profiles leading to x% effect reduction at a specific time t (MF(x, t), or denoted LP(x, t) by the EFSA). However, classical dose-response models used to estimate toxicity endpoints have some weaknesses, such as their dependency on observation time points, which are likely to differ between species (e.g., experiment duration). Furthermore, real-world exposure profiles are rarely constant over time, which makes the use of classical dose-response models difficult and may prevent the derivation of MF(x, t). When dealing with survival or immobility toxicity test data, these issues can be overcome with the use of the general unified threshold model of survival (GUTS), a toxicokinetic-toxicodynamic (TKTD) model that provides an explicit framework to analyse both time- and concentration-dependent data sets as well as obtain a mechanistic derivation of EC/LC(x, t) and MF(x, t) regardless of x and at any time t of interest. In ERA, the assessment of a risk is inherently built upon probability distributions, such that the next critical step is to characterize the uncertainties of toxicity endpoints and, consequently, those of EQSs. With this perspective, we investigated the use of a Bayesian framework to obtain the uncertainties from the calibration process and to propagate them to model predictions, including LC(x, t) and MF(x, t) derivations. We also explored the mathematical properties of LC(x, t) and MF(x, t) as well as the impact of different experimental designs to provide some recommendations for a robust derivation of toxicity endpoints leading to reliable EQSs: avoid computing LC(x, t) and MF(x, t) for extreme x values (0 or 100%), where uncertainty is maximal; compute MF(x, t) after a long period of time to take depuration time into account and test survival under pulses with different periods of time between them.

Project description:BackgroundSpecies-specific information on distribution and demographic patterns provides important implications for conservation and fisheries management. However, such information is often lacking for morphologically-similar species, which may lead to biases in the assessments of these species and even decrease effort towards sustainable management. Here, we aimed to uncover the distribution range, population structure and demographic history for four exploited Trichiurus cutlassfishes using genetics. These cutlassfishes contribute substantial global fisheries catch, with a high proportion of catch harvested from the NW Pacific.MethodsWe chose the widely available mitochondrial 16S ribosomal RNA (16S) as the genetic marker for cutlassfishes. We compiled the 16S sequence data from both the GenBank and a survey of trawler catch samples along the NW Pacific coasts 22-39°N. Genealogical relationships within each species was visualized with haplotype networks and potential population differentiations were further evaluated with AMOVA. Demographic histories were estimated using neutrality test, mismatch analysis, and the Bayesian skyline plot. The reconstructed phylogenetic trees were used to delimit and estimate the divergence time of species and included populations.ResultsIn each of two cosmopolitan species, T. lepturus and T. nanhaiensis, we observed distinct populations along the coasts of warm oceans; such population differentiation might result from historical geographic barriers in the Pleistocene. In the NW Pacific, four Trichiurus species vary in their distribution habitats, which reflect differential ecological niches among these species. The small-sized T. brevis are primarily found in nearshore habitats; the warm-affiliated T. nanhaiensis are present along the path of the Kuroshio Current; the cold-affiliated T. japonicus spatially diverged from the widely-distributed T. lepturus, with the latter mainly occupy in warmer regions. Despite these differences, a single well-mixing fish stock, thus one management unit, was identified in each of the four species, presumably due to expansion of their population sizes predated the Last Glacial Maximum and a lack of distribution barrier. The most dominant T. japonicus, which have at least one magnitude higher effective population size than the others, show a unique abrupt size expansion event at 75 to 50-kilo years ago when the low sea level occurred during the ice age.Main conclusionsThe demographic history revealed by our genetic analyses advances understanding of the current distribution and population structure for these congeneric species. Moreover, the uncovered population structure provides insight into the assessment and management of these species. Such information complements contemporary knowledge about these species and enables us to forecast their ability to resist future environmental and anthropogenic disturbances.

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

Publications

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

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