A standard protocol for reporting species distribution models

Ecography, Journal Year: 2020, Volume and Issue: 43(9), P. 1261 - 1277

Published: June 1, 2020

Species distribution models (SDMs) constitute the most common class of across ecology, evolution and conservation. The advent ready‐to‐use software packages increasing availability digital geoinformation have considerably assisted application SDMs in past decade, greatly enabling their broader use for informing conservation management, quantifying impacts from global change. However, must be fit purpose, with all important aspects development applications properly considered. Despite widespread SDMs, standardisation documentation modelling protocols remain limited, which makes it hard to assess whether steps are appropriate end use. To address these issues, we propose a standard protocol reporting an emphasis on describing how study's objective is achieved through series modeling decisions. We call this ODMAP (Overview, Data, Model, Assessment Prediction) protocol, as its components reflect main involved building other empirically‐based biodiversity models. serves two purposes. First, provides checklist authors, detailing key model analyses, thus represents quick guide generic workflow modern SDMs. Second, introduces structured format documenting communicating models, ensuring transparency reproducibility, facilitating peer review expert evaluation quality, well meta‐analyses. detail elements ODMAP, explain can used different objectives applications, complements efforts store associated metadata define standards. illustrate utility by revisiting nine previously published case studies, provide interactive web‐based facilitate plan advance encouraging further refinement adoption scientific community.

Language: Английский

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Predictive performance of presence‐only species distribution models: a benchmark study with reproducible code

Ecological Monographs, Journal Year: 2021, Volume and Issue: 92(1)

Published: Oct. 8, 2021

Abstract Species distribution modeling (SDM) is widely used in ecology and conservation. Currently, the most available data for SDM are species presence‐only records (available through digital databases). There have been many studies comparing performance of alternative algorithms data. Among these, a 2006 paper from Elith colleagues has particularly influential field, partly because they several novel methods (at time) on global set that included independent presence–absence model evaluation. Since its publication, some further developed new ones emerged. In this paper, we explore patterns predictive across methods, by reanalyzing same (225 six different regions) using updated knowledge practices. We apply well‐established such as generalized additive models MaxEnt, alongside others received attention more recently, including regularized regressions, point‐process weighted random forests, XGBoost, support vector machines, ensemble framework biomod. All use include background samples (a sample environments landscape) fitting. impacts weights presence points introduce ways evaluating fitted to these data, area under precision‐recall gain curve, focusing rank results. find way matters. The top method was an tuned individual models. contrast, ensembles built biomod with default parameters performed no better than single moderate performing Similarly, second forest parameterized deal (contrasted relatively few records), which substantially outperformed other implementations. that, general, nonparametric techniques capability controlling complexity traditional regression MaxEnt boosted trees still among code working examples provided make study fully reproducible.

Language: Английский

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Joint species distribution modelling with ther‐package Hmsc

Methods in Ecology and Evolution, Journal Year: 2019, Volume and Issue: 11(3), P. 442 - 447

Published: Dec. 26, 2019

Abstract Joint Species Distribution Modelling (JSDM) is becoming an increasingly popular statistical method for analysing data in community ecology. Hierarchical of Communities (HMSC) a general and flexible framework fitting JSDMs. HMSC allows the integration ecology with on environmental covariates, species traits, phylogenetic relationships spatio‐temporal context study, providing predictive insights into assembly processes from non‐manipulative observational communities. The full range functionality has remained restricted to Matlab users only. To make accessible wider ecologists, we introduce H msc 3.0, user‐friendly r implementation. We illustrate use package by applying 3.0 case studies real simulated data. consist bird counts spatio‐temporally structured dataset, traits relationships. Vignettes involve single‐species models, models small communities, large communities spatial demonstrate estimation responses covariates how these depend as well residual associations. construct fit different types random effects, examine MCMC convergence, explanatory powers assess parameter estimates predictions. further can be applied normally distributed data, count presence–absence package, along extended vignettes, makes JSDM post‐processing easily ecologists familiar .

Language: Английский

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Collinearity in ecological niche modeling: Confusions and challenges

Ecology and Evolution, Journal Year: 2019, Volume and Issue: 9(18), P. 10365 - 10376

Published: Aug. 20, 2019

Ecological niche models are widely used in ecology and biogeography. Maxent is one of the most frequently modeling tools, many studies have aimed to optimize its performance. However, scholars conflicting views on treatment predictor collinearity modeling. Despite this lack consensus, quantitative examinations effects modeling, especially model transfer scenarios, lacking. To address knowledge gap, here we quantify under different scenarios training projection. We separately examine collinearity, shifts between testing data, environmental novelty demonstrate that excluding highly correlated variables does not significantly influence find shift significant negative performance transfer. thus conclude (a) robust training; (b) strategy has little impact because accounts for redundant variables; (c) can negatively affect transferability. therefore recommend report better infer accuracy when spatially and/or temporally transferred.

Language: Английский

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A comparison between Ensemble and MaxEnt species distribution modelling approaches for conservation: A case study with Egyptian medicinal plants

Ecological Informatics, Journal Year: 2020, Volume and Issue: 60, P. 101150 - 101150

Published: Sept. 3, 2020

Language: Английский

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Joint Species Distribution Modelling

Published: May 18, 2020

Joint species distribution modelling (JSDM) is a fast-developing field and promises to revolutionise how data on ecological communities are analysed interpreted. Written for both readers with limited statistical background, those expertise, this book provides comprehensive account of JSDM. It enables integrate abundances, environmental covariates, traits, phylogenetic relationships, the spatio-temporal context in which have been acquired. Step-by-step coverage full technical detail methods provided, as well advice interpreting results analyses broader modern community ecology theory. With advantage numerous example R-scripts, an ideal guide help graduate students researchers learn conduct interpret practice R-package Hmsc, providing fast starting point applying joint their own data.

Language: Английский

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Machine learning and deep learning—A review for ecologists

Methods in Ecology and Evolution, Journal Year: 2023, Volume and Issue: 14(4), P. 994 - 1016

Published: Feb. 13, 2023

Abstract The popularity of machine learning (ML), deep (DL) and artificial intelligence (AI) has risen sharply in recent years. Despite this spike popularity, the inner workings ML DL algorithms are often perceived as opaque, their relationship to classical data analysis tools remains debated. Although it is assumed that excel primarily at making predictions, can also be used for analytical tasks traditionally addressed with statistical models. Moreover, most discussions reviews on focus mainly DL, failing synthesise wealth different advantages general principles. Here, we provide a comprehensive overview field starting by summarizing its historical developments, existing algorithm families, differences traditional tools, universal We then discuss why when models prediction where they could offer alternatives methods inference, highlighting current emerging applications ecological problems. Finally, summarize trends such scientific causal ML, explainable AI, responsible AI may significantly impact future. conclude powerful new predictive modelling analysis. superior performance compared explained higher flexibility automatic data‐dependent complexity optimization. However, use inference still disputed predictions creates challenges interpretation these Nevertheless, expect become an indispensable tool ecology evolution, comparable other tools.

Language: Английский

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Assessing the reliability of species distribution projections in climate change research

Diversity and Distributions, Journal Year: 2021, Volume and Issue: 27(6), P. 1035 - 1050

Published: Feb. 19, 2021

Abstract Aim Forecasting changes in species distribution under future scenarios is one of the most prolific areas application for models (SDMs). However, no consensus yet exists on reliability such drawing conclusions species’ response to changing climate. In this study, we provide an overview common modelling practices field and assess model predictions using a virtual approach. Location Global. Methods We first review papers published between 2015 2019. Then, use approach three commonly applied SDM algorithms (GLM, MaxEnt random forest) estimated actual predictive performance parameterized with different settings violations assumptions. Results Most relied single (65%) small samples ( N < 50, 62%), used presence‐only data (85%), binarized models' output (74%) split‐sample validation (94%). Our simulation reveals that tends be over‐optimistic compared real performance, whereas spatial block provides more honest estimate, except when datasets are environmentally biased. The binarization predicted probabilities presence reduces models’ ability considerably. Sample size main predictors accuracy, but has little influence accuracy. Finally, inclusion ecologically irrelevant violation assumptions increases accuracy decreases projections, leading biased estimates range contraction expansion. Main predict low average, particularly binarized. A robust by spatially independent required, does not rule out inflation assumption violation. findings call caution interpretation projections climates.

Language: Английский

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Contributions of Quaternary botany to modern ecology and biogeography

Plant Ecology & Diversity, Journal Year: 2019, Volume and Issue: 12(3-4), P. 189 - 385

Published: May 4, 2019

Quaternary (last 2.6 million years) botany involves studying plant megafossils (e.g. tree stumps), macrofossils seeds, leaves), and microfossils pollen, spores) preserved in peat bogs lake sediments. Although have been studied since the late eighteenth century, today is largely dominated by pollen analysis.Quaternary analysis just over 100 years old. It started primarily as a geological tool for correlation, relative dating, climate reconstruction. In 1950 major advance occurred with publication Knut Fægri Johs Iversen of their Text-book Modern Pollen Analysis which provided foundations botanical ecological past dynamics biota biotic systems. The development radiocarbon dating 1950s freed from being dating. As result these developments, became valuable implement long-term ecology biogeography.Selected contributions that has made to biogeography are reviewed. They fall into four general parts: (1) aspects interglacial glacial stages such location nature glacial-stage refugia soil glaciated unglaciated areas; (2) responses environmental change (spreading, extinction, persistence, adaptation); (3) topics potential niches, vegetation, forest dynamics; (4) its application human impact tropical systems, conservation changing world, island palaeoecology, plant–animal interactions, biodiversity patterns time.The future briefly discussed 10 suggestions presented help strengthen it links biogeography. much contribute when used conjunction new approaches ancient-DNA, molecular biomarkers, multi-proxy palaeoecology.

Language: Английский

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On the Interpretations of Joint Modeling in Community Ecology

Trends in Ecology & Evolution, Journal Year: 2021, Volume and Issue: 36(5), P. 391 - 401

Published: Feb. 21, 2021

Language: Английский

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