A5032805182- Species Distribution and Climate Change
- Ecology and Vegetation Dynamics Studies
- Wildlife Ecology and Conservation
- Fish Ecology and Management Studies
- Data Analysis with R
- Genetic diversity and population structure
- Plant and animal studies
- Land Use and Ecosystem Services
- Forest Insect Ecology and Management
- Environmental DNA in Biodiversity Studies
- Economic and Environmental Valuation
- Biocrusts and Microbial Ecology
- Aquatic Ecosystems and Phytoplankton Dynamics
- Forest ecology and management
- Marine and fisheries research
- Plant Pathogens and Fungal Diseases
- Mycorrhizal Fungi and Plant Interactions
- Soil Geostatistics and Mapping
- Remote Sensing in Agriculture
- Animal Ecology and Behavior Studies
- Amphibian and Reptile Biology
- Rangeland and Wildlife Management
- Animal Behavior and Reproduction
- Marine Bivalve and Aquaculture Studies
- Ecosystem dynamics and resilience
The University of Melbourne
2015-2024
John Wiley & Sons (United States)
2019-2021
Hudson Institute
2021
Ecological Society of America
2019
Ecosystem Sciences
2019
ARC Centre of Excellence for Environmental Decisions
2018
Botho University
2012
Prediction of species’ distributions is central to diverse applications in ecology, evolution and conservation science. There increasing electronic access vast sets occurrence records museums herbaria, yet little effective guidance on how best use this information the context numerous approaches for modelling distributions. To meet need, we compared 16 methods over 226 species from 6 regions world, creating most comprehensive set model comparisons date. We used presence‐only data fit models,...
Species distribution models (SDMs) are numerical tools that combine observations of species occurrence or abundance with environmental estimates. They used to gain ecological and evolutionary insights predict distributions across landscapes, sometimes requiring extrapolation in space time. SDMs now widely terrestrial, freshwater, marine realms. Differences methods between disciplines reflect both differences mobility “established use.” Model realism robustness is influenced by selection...
MaxEnt is a program for modelling species distributions from presence-only records. This paper written ecologists and describes the model statistical perspective, making explicit links between structure of model, decisions required in producing modelled distribution, knowledge about data that might affect those decisions. To begin we discuss characteristics data, highlighting implications distributions. We particularly focus on problems sample bias lack information prevalence. The keystone...
1 Ecologists use statistical models for both explanation and prediction, need techniques that are flexible enough to express typical features of their data, such as nonlinearities interactions. 2 This study provides a working guide boosted regression trees (BRT), an ensemble method fitting differs fundamentally from conventional aim fit single parsimonious model. Boosted combine the strengths two algorithms: (models relate response predictors by recursive binary splits) boosting (an adaptive...
Most methods for modeling species distributions from occurrence records require additional data representing the range of environmental conditions in modeled region. These data, called background or pseudo‐absence are usually drawn at random entire region, whereas collection is often spatially biased toward easily accessed areas. Since spatial bias generally results bias, difference between and sampling may lead to inaccurate models. To correct estimation, we propose choosing with same as...
1. Species are shifting their ranges at an unprecedented rate through human transportation and environmental change. Correlative species distribution models (SDMs) frequently applied for predicting potential future distributions of range-shifting species, despite these models' assumptions that equilibrium with the environments used to train (fit) models, training data representative conditions which predicted. Here we explore modelling approaches aim minimize extrapolation errors assess...
Species distribution models (SDMs) are increasingly proposed to support conservation decision making. However, evidence of SDMs supporting solutions for on-ground problems is still scarce in the scientific literature. Here, we show that successful examples exist but largely hidden grey literature, and thus less accessible analysis learning. Furthermore, framework within which used rarely made explicit. Using case studies from biological invasions, identification critical habitats, reserve...
Ecological data often show temporal, spatial, hierarchical (random effects), or phylogenetic structure. Modern statistical approaches are increasingly accounting for such dependencies. However, when performing cross‐validation, these structures regularly ignored, resulting in serious underestimation of predictive error. One cause the poor performance uncorrected (random) noted by modellers, dependence that persist as model residuals, violating assumption independence. Even more concerning,...
ABSTRACT Generalized dissimilarity modelling (GDM) is a statistical technique for analysing and predicting spatial patterns of turnover in community composition (beta diversity) across large regions. The approach an extension matrix regression, designed specifically to accommodate two types nonlinearity commonly encountered large‐scaled ecological data sets: (1) the curvilinear relationship between increasing distance, observed compositional dissimilarity, sites; (2) variation rate at...
Abstract Species distribution models ( SDM s) are used to inform a range of ecological, biogeographical and conservation applications. However, users often underestimate the strong links between data type, model output suitability for end‐use. We synthesize current knowledge provide simple framework that summarizes how interactions type sampling process (i.e. imperfect detection bias) determine quantity is estimated by . then draw upon published literature simulations illustrate evaluate...
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...
Summary Species distribution models (habitat models) relate the occurrence or abundance of a species to environmental and/or geographical predictors that then allow predictions be mapped across an entire region. These are used in range policy settings such as managing greenhouse gases, biosecurity threats and conservation planning. Prediction errors almost ubiquitous habitat models. An understanding source, magnitude pattern these is essential if transparently decision making. This study...
Species distribution models (SDMs) are widely used to explain and predict species ranges environmental niches. They most commonly constructed by inferring species' occurrence–environment relationships using statistical machine‐learning methods. The variety of methods that can be construct SDMs (e.g. generalized linear/additive models, tree‐based maximum entropy, etc.), the ways such implemented, permits substantial flexibility in SDM complexity. Building with an appropriate amount complexity...
MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout JournalEditorsTheme Sections 321:267-281 (2006) - doi:10.3354/meps321267 Variation in demersal fish species richness oceans surrounding New Zealand: an analysis using boosted regression trees J. R. Leathwick1,*, Elith2, M. P. Francis3, T. Hastie4, Taylor1 1National Institute of Water and Atmospheric Research, PO Box 11115, Hamilton,...
ABSTRACT Predictive species distribution modelling (SDM) has become an essential tool in biodiversity conservation and management. The choice of grain size (resolution) environmental layers used is one important factor that may affect predictions. We applied 10 distinct techniques to presence‐only data for 50 five different regions, test whether: (1) a 10‐fold coarsening resolution affects predictive performance SDMs, (2) any observed effects are dependent on the type region, technique, or...
Summary Species distribution modelling is used increasingly in both applied and theoretical research to predict how species are distributed understand attributes of species’ environmental requirements. In modelling, various statistical methods that combine occurrence data with spatial layers the suitability any site for species. While number sharing initiatives involving occurrences scientific community has increased dramatically over past few years, quality methodological concerns related...
Abstract When applied to structured data, conventional random cross‐validation techniques can lead underestimation of prediction error, and may result in inappropriate model selection. We present the r package block CV , a new toolbox for species distribution modelling. Although it has been developed with modelling mind, be used any spatial The generate spatially or environmentally separated folds. It includes tools measure autocorrelation ranges candidate covariates, providing user insights...
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...
Aim Species distribution models (SDMs) have been used to address a wide range of theoretical and applied questions in the terrestrial realm, but marine-based applications remain relatively scarce. In this review, we consider how conceptual practical issues associated with SDMs apply marine organisms highlight challenges relevant improving SDMs. Location We include studies from both systems that encompass many geographic locations around globe. Methods first performed literature search...
Abstract A large array of species distribution model ( SDM ) approaches has been developed for explaining and predicting the occurrences individual or assemblages. Given wealth existing models, it is unclear which models perform best interpolation extrapolation data sets, particularly when one concerned with We compared predictive performance 33 variants 15 widely applied recently emerged s in context multispecies data, including both joint that multiple together, stacked each individually...