Dataset Information

Data quantity is more important than its spatial bias for predictive species distribution modelling.

ABSTRACT: Biological records are often the data of choice for training predictive species distribution models (SDMs), but spatial sampling bias is pervasive in biological records data at multiple spatial scales and is thought to impair the performance of SDMs. We simulated presences and absences of virtual species as well as the process of recording these species to evaluate the effect on species distribution model prediction performance of (1) spatial bias in training data, (2) sample size (the average number of observations per species), and (3) the choice of species distribution modelling method. Our approach is novel in quantifying and applying real-world spatial sampling biases to simulated data. Spatial bias in training data decreased species distribution model prediction performance, but sample size and the choice of modelling method were more important than spatial bias in determining the prediction performance of species distribution models.

SUBMITTER: Gaul W

PROVIDER: S-EPMC7703440 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Data quantity is more important than its spatial bias for predictive species distribution modelling.

Gaul Willson W Sadykova Dinara D White Hannah J HJ Leon-Sanchez Lupe L Caplat Paul P Emmerson Mark C MC Yearsley Jon M JM

PeerJ 20201127

Biological records are often the data of choice for training predictive species distribution models (SDMs), but spatial sampling bias is pervasive in biological records data at multiple spatial scales and is thought to impair the performance of SDMs. We simulated presences and absences of virtual species as well as the process of recording these species to evaluate the effect on species distribution model prediction performance of (1) spatial bias in training data, (2) sample size (the average n ...[more]

PMID: 33312769

Similar Datasets

Project description:Global environmental changes are rapidly affecting species' distributions and habitat suitability worldwide, requiring a continuous update of biodiversity status to support effective decisions on conservation policy and management. In this regard, satellite-derived Ecosystem Functional Attributes (EFAs) offer a more integrative and quicker evaluation of ecosystem responses to environmental drivers and changes than climate and structural or compositional landscape attributes. Thus, EFAs may hold advantages as predictors in Species Distribution Models (SDMs) and for implementing multi-scale species monitoring programs. Here we describe a modelling framework to assess the predictive ability of EFAs as Essential Biodiversity Variables (EBVs) against traditional datasets (climate, land-cover) at several scales. We test the framework with a multi-scale assessment of habitat suitability for two plant species of conservation concern, both protected under the EU Habitats Directive, differing in terms of life history, range and distribution pattern (Iris boissieri and Taxus baccata). We fitted four sets of SDMs for the two test species, calibrated with: interpolated climate variables; landscape variables; EFAs; and a combination of climate and landscape variables. EFA-based models performed very well at the several scales (AUCmedian from 0.881±0.072 to 0.983±0.125), and similarly to traditional climate-based models, individually or in combination with land-cover predictors (AUCmedian from 0.882±0.059 to 0.995±0.083). Moreover, EFA-based models identified additional suitable areas and provided valuable information on functional features of habitat suitability for both test species (narrowly vs. widely distributed), for both coarse and fine scales. Our results suggest a relatively small scale-dependence of the predictive ability of satellite-derived EFAs, supporting their use as meaningful EBVs in SDMs from regional and broader scales to more local and finer scales. Since the evaluation of species' conservation status and habitat quality should as far as possible be performed based on scalable indicators linking to meaningful processes, our framework may guide conservation managers in decision-making related to biodiversity monitoring and reporting schemes.

Project description:BackgroundHigh glycemic index (GI) diets have been linked to elevated risk of cardiometabolic diseases. One possible underlying mechanism comes from high GI diet's potential to promote lipid peroxidation.ObjectivesWe aim to evaluate whether and to what extent dietary carbohydrate quality and quantity are associated with systemic levels of lipid peroxidation in females.MethodsIn this cross-sectional analysis of 2163 middle-aged women, a subset of the Shanghai Women's Health Study, we measured lipid peroxidation biomarkers F2-isoprostanes (F2-IsoPs) and its metabolite, 2,3-dinor-5,6-dihydro-15-F2t-IsoP (F2-IsoP-M), in urine. The quality of carbohydrate was defined by dietary GI, assessed using a validated FFQ via in-person interviews. A multivariable linear regression model with restricted cubic spline functions was used to evaluate the association of measured biomarkers with carbohydrate intake and dietary GI.ResultsAfter adjustment for potential confounding factors such as cigarette smoking, BMI, and comorbidities, among others, we found that F2-IsoP-M concentrations were positively associated with both carbohydrate intake and dietary GI. Carbohydrate intake and dietary GI were weakly correlated (r = 0.12). When further mutually adjusted for the 2 factors, the positive association with F2-IsoP-M remained statistically significant for GI (P = 0.004) but not for carbohydrate intake (P = 0.50). Compared with those in the 10th percentile of dietary GI, fold increases (95% CI) in F2-IsoP-M concentrations for those in the 30th, 50th, 70th, and 90th percentiles were 1.03 (1.00, 1.07), 1.06 (1.01, 1.10), 1.09 (1.03, 1.14), and 1.13 (1.05, 1.21), respectively. Moreover, there appeared a threshold regarding the association between dietary GI and F2-IsoP-M concentrations, with the dose-effect slope of GI being 2.3 times greater when GI was ≥75 relative to GI <75.ConclusionsThis study provides evidence that the quality of dietary carbohydrate may be more important than the quantity of the intake with regard to systemic lipid peroxidation.

Project description:Species distribution models (SDMs) trained on presence-only data are frequently used in ecological research and conservation planning. However, users of SDM software are faced with a variety of options, and it is not always obvious how selecting one option over another will affect model performance. Working with MaxEnt software and with tree fern presence data from New Zealand, we assessed whether (a) choosing to correct for geographical sampling bias and (b) using complex environmental response curves have strong effects on goodness of fit. SDMs were trained on tree fern data, obtained from an online biodiversity data portal, with two sources that differed in size and geographical sampling bias: a small, widely-distributed set of herbarium specimens and a large, spatially clustered set of ecological survey records. We attempted to correct for geographical sampling bias by incorporating sampling bias grids in the SDMs, created from all georeferenced vascular plants in the datasets, and explored model complexity issues by fitting a wide variety of environmental response curves (known as "feature types" in MaxEnt). In each case, goodness of fit was assessed by comparing predicted range maps with tree fern presences and absences using an independent national dataset to validate the SDMs. We found that correcting for geographical sampling bias led to major improvements in goodness of fit, but did not entirely resolve the problem: predictions made with clustered ecological data were inferior to those made with the herbarium dataset, even after sampling bias correction. We also found that the choice of feature type had negligible effects on predictive performance, indicating that simple feature types may be sufficient once sampling bias is accounted for. Our study emphasizes the importance of reducing geographical sampling bias, where possible, in datasets used to train SDMs, and the effectiveness and essentialness of sampling bias correction within MaxEnt.

Dataset Information

Data quantity is more important than its spatial bias for predictive species distribution modelling.

Publications

Data quantity is more important than its spatial bias for predictive species distribution modelling.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets