scholarly journals Classification of unlabeled observations in Species Distribution Modelling using Point Process Models

2019 ◽  
Author(s):  
Emy Guilbault ◽  
Ian Renner ◽  
Michael Mahony ◽  
Eric Beh
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Species Distributions ◽  
Species Distribution Modelling ◽  
Process Models ◽  
List Type ◽  
Distribution Models ◽  
Species Identity ◽  
Distribution Modelling ◽  
Unknown Species

1AbstractSpecies distribution modelling, which allows users to predict the spatial distribution of species with the use of environmental covariates, has become increasingly popular, with many software platforms providing tools to fit species distribution models. However, the species observations used in species distribution models can have varying levels of quality and can have incomplete information, such as uncertain species identity.In this paper, we develop two algorithms to reclassify observations with unknown species identities which simultaneously predict different species distributions using spatial point processes. We compare the performance of the different algorithms using different initializations and parameters with models fitted using only the observations with known species identity through simulations.We show that performance varies with differences in correlation among species distributions, species abundance, and the proportion of observations with unknown species identities. Additionally, some of the methods developed here outperformed the models that didn’t use the misspecified data.These models represent an helpful and promising tool for opportunistic surveys where misidentification happens or for the distribution of species newly separated in their taxonomy.

scholarly journals blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models

2018 ◽  
Author(s):  
Roozbeh Valavi ◽  
Jane Elith ◽  
José J. Lahoz-Monfort ◽  
Gurutzeta Guillera-Arroita
Keyword(s):  
Species Distribution ◽  
Cross Validation ◽  
Species Distribution Models ◽  
Predictive Performance ◽  
R Package ◽  
Species Distribution Modelling ◽  
List Type ◽  
Distribution Models ◽  
Distribution Modelling ◽  
Evaluation Approaches

SummaryWhen applied to structured data, conventional random cross-validation techniques can lead to underestimation of prediction error, and may result in inappropriate model selection.We present the R package blockCV, a new toolbox for cross-validation of species distribution modelling.The package can generate spatially or environmentally separated folds. It includes tools to measure spatial autocorrelation ranges in candidate covariates, providing the user with insights into the spatial structure in these data. It also offers interactive graphical capabilities for creating spatial blocks and exploring data folds.Package blockCV enables modellers to more easily implement a range of evaluation approaches. It will help the modelling community learn more about the impacts of evaluation approaches on our understanding of predictive performance of species distribution models.

scholarly journals Making species distribution models available on the web for reuse in biodiversity experiments: Euterpe edulis species case study

2009 ◽  
Vol 21 (1) ◽  
pp. 39-49
Author(s):  
Karla Donato Fook ◽  
Silvana Amaral ◽  
Antônio Miguel Vieira Monteiro ◽  
Gilberto Câmara ◽  
Arimatéa de Carvalho Ximenes ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Species Distribution Modelling ◽  
Distribution Models ◽  
Distribution Modelling ◽  
Urban And Regional Planning ◽  
Community Needs ◽  
Computational Environment ◽  
Decision Making Processes ◽  
The Web

Currently, biodiversity conservation is one of the most urgent and important themes. Biodiversity researchers use species distribution models to make inferences about species occurrences and locations. These models are fundamental for fauna and flora preservation, as well as for decision making processes for urban and regional planning and development. Species distribution modelling tools use large biodiversity datasets which are globally distributed, can be in different computational platforms, and are hard to access and manipulate. The scientific community needs infrastructures in which biodiversity researchers can collaborate and share knowledge. In this context, we present a computational environment that supports the collaboration in species distribution modelling network on the Web. This environment is based on a modelling experiment catalogue and on a set of geoweb services, the Web Biodiversity Collaborative Modelling Services - WBCMS.

scholarly journals Habitat loss is information loss: Species distribution models are compromised in anthropogenic landscapes

2018 ◽  
Author(s):  
Russell Dinnage ◽  
Marcel Cardillo
Keyword(s):  
Habitat Loss ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Species Distributions ◽  
Process Models ◽  
Distribution Models ◽  
Ecological Processes ◽  
Anthropogenic Landscapes ◽  
Point Process Models ◽  
Species Mapping

AbstractSpecies distribution models (SDMs) are valuable tools to estimate species’ distributions, but are vulnerable to biases in the probability of a species being observed. One such bias is habitat loss, which has affected a substantial and increasing proportion of the Earth. In regions of severe habitat loss, data on a species’ occurrence may represent a small, non-random subset of sites it once occupied. This could cause distorted reconstructions of species distributions, and misleading inferences of evolutionary and ecological processes. We present a statistical approach for quantifying the influence on SDMs of habitat loss, and generating distribution predictions that are robust to these biases. We explored some of the effects of accounting for habitat loss on inferences from common downstream biogeographic and ecological analysis methods.We used herbarium record data to model the distribution of 325 plant species in the genera Banksia and Hakea across Australia, using point process models. We accounted for biases in the models by including a proxy variable representing habitat loss, and compared the fit of models without this variable to those with it. We explored the influence of habitat loss by mapping biodiversity patterns predicted with and without accounting for it.Generally, accounting for habitat loss in SDMs led to increases in the mean area of modelled species distributions of ~10% for Banksia and ~12% for Hakea across Australia (in some cases, up to several 100,000 km2 increases in predicted range), with somewhat greater average increases (11% and 15%) for species in the southwest Australian biodiversity hotspot. Accounting for habitat loss leads to an increase in predicted species richness (Alpha and Gamma diversity), but a decrease in compositional turnover (Beta diversity), across most of Australia. Accounting for habitat loss in SDMs had minimal influence on a downstream macroevolutionary analysis (Age-Range Correlation) that utilizes species distributions, seemingly because exposure to habitat loss did not show a phylogenetic pattern in this taxonomic group.The influence of habitat loss on species distributions estimated with SDM is likely to be context-dependent and difficult to generalize, but will tend to cause underestimates of range sizes. This may have consequences for mapping spatial patterns of diversity and for some downstream analyses of biogeographic, evolutionary, or ecological processes, based on species distributions, as well as conservation measures that rely on accurate species mapping.

scholarly journals Challenges and opportunities of species distribution modelling of terrestrial arthropod predators

2020 ◽  
Author(s):  
Stefano Mammola ◽  
Julien Pétillon ◽  
Axel Hacala ◽  
Sapho-Lou Marti ◽  
Jérémy Monsimet ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distributions ◽  
Species Distribution Modelling ◽  
Generalist Predators ◽  
Distribution Models ◽  
Distribution Modelling ◽  
Arthropod Predators ◽  
Data Limitations ◽  
Challenges And Opportunities ◽  
Small Models

Species distribution models (SDMs) are emerging as essential tools in the equipment of many ecologists; they are useful in exploring species distributions in space and time and in answering an assortment of questions related to historical biogeography, climate change biology and conservation biology. Given that arthropod distributions are strongly influenced by microclimatic conditions and microhabitat structure, they should be an ideal candidate group for SDM research, especially generalist predators because they are not directly dependent on vegetation or prey types. However, most SDM studies of animals to date have focused either on broad samples of vertebrates or on arthropod species that are charismatic (e.g. butterflies) or economically important (e.g. vectors of disease, crop pests and pollinators). By means of a systematic bibliometric approach, we targeted the literature published on key terrestrial arthropod predators (ants, ground beetles and spiders), chosen as a model to explore challenges and opportunities of species distribution modelling in mega-diverse arthropod groups. We show that the use of SDMs to map the geography of terrestrial arthropod predators has been a recent phenomenon, with a near-exponential growth in the number of studies over the past 10 years and still limited collaborative networks among researchers. There is a bias in studies towards charismatic species and geographical areas that hold lower levels of diversity but greater availability of data, such as Europe and North America. To overcome some of these data limitations, we illustrate the potential of modern data sources (citizen science programmes, online databases) and new modelling approaches (ensemble of small models, modelling above the species level). Finally, we discuss areas of research where SDMs may be combined with dispersal models and increasingly available phylogenetic and functional data to obtain mechanistic descriptions of species distributions and their spatio-temporal shifts within a global change perspective.

scholarly journals Using species distribution modelling to determine opportunities for trophic rewilding under future scenarios of climate change

2018 ◽  
Vol 373 (1761) ◽  
pp. 20170446 ◽  
Author(s):  
Scott Jarvie ◽  
Jens-Christian Svenning
Keyword(s):  
Climate Change ◽  
Species Distribution ◽  
Species Distribution Modelling ◽  
Climatic Conditions ◽  
First Century ◽  
Distribution Models ◽  
Distribution Modelling ◽  
Major Barrier ◽  
Methodological Choices ◽  
Biodiversity And Ecosystem Function

Trophic rewilding, the (re)introduction of species to promote self-regulating biodiverse ecosystems, is a future-oriented approach to ecological restoration. In the twenty-first century and beyond, human-mediated climate change looms as a major threat to global biodiversity and ecosystem function. A critical aspect in planning trophic rewilding projects is the selection of suitable sites that match the needs of the focal species under both current and future climates. Species distribution models (SDMs) are currently the main tools to derive spatially explicit predictions of environmental suitability for species, but the extent of their adoption for trophic rewilding projects has been limited. Here, we provide an overview of applications of SDMs to trophic rewilding projects, outline methodological choices and issues, and provide a synthesis and outlook. We then predict the potential distribution of 17 large-bodied taxa proposed as trophic rewilding candidates and which represent different continents and habitats. We identified widespread climatic suitability for these species in the discussed (re)introduction regions under current climates. Climatic conditions generally remain suitable in the future, although some species will experience reduced suitability in parts of these regions. We conclude that climate change is not a major barrier to trophic rewilding as currently discussed in the literature.This article is part of the theme issue ‘Trophic rewilding: consequences for ecosystems under global change’.

scholarly journals Evaluating ecological uniqueness over broad spatial extents using species distribution modelling

2021 ◽  
Author(s):  
Gabriel Dansereau ◽  
Pierre Legendre ◽  
Timothée Poisot
Keyword(s):  
North America ◽  
Species Distribution ◽  
Rare Species ◽  
Beta Diversity ◽  
Species Distribution Modelling ◽  
Distribution Models ◽  
Science Data ◽  
Distribution Modelling ◽  
Ecological Uniqueness ◽  
The Relationship

Aim: Local contributions to beta diversity (LCBD) can be used to identify sites with high ecological uniqueness and exceptional species composition within a region of interest. Yet, these indices are typically used on local or regional scales with relatively few sites, as they require information on complete community compositions difficult to acquire on larger scales. Here, we investigate how LCBD indices can be used to predict ecological uniqueness over broad spatial extents using species distribution modelling and citizen science data. Location: North America. Time period: 2000s. Major taxa studied: Parulidae. Methods: We used Bayesian additive regression trees (BARTs) to predict warbler species distributions in North America based on observations recorded in the eBird database. We then calculated LCBD indices for observed and predicted data and examined the site-wise difference using direct comparison, a spatial autocorrelation test, and generalized linear regression. We also investigated the relationship between LCBD values and species richness in different regions and at various spatial extents and the effect of the proportion of rare species on the relationship. Results: Our results showed that the relationship between richness and LCBD values varies according to the region and the spatial extent at which it is applied. It is also affected by the proportion of rare species in the community. Species distribution models provided highly correlated estimates with observed data, although spatially autocorrelated. Main conclusions: Sites identified as unique over broad spatial extents may vary according to the regional richness, total extent size, and the proportion of rare species. Species distribution modelling can be used to predict ecological uniqueness over broad spatial extents, which could help identify beta diversity hotspots and important targets for conservation purposes in unsampled locations.

scholarly journals The effects of data adequacy and calibration size on the accuracy of presence-only species distribution models

2019 ◽  
Author(s):  
Truly Santika ◽  
Michael F. Hutchinson ◽  
Kerrie A. Wilson
Keyword(s):  
Performance Measures ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Simulated Data ◽  
List Type ◽  
Model Accuracy ◽  
Distribution Models ◽  
Accuracy Measure ◽  
Data Coverage ◽  
True Distribution

ABSTRACTPresence-only data used to develop species distribution models are often biased towards areas that are frequently surveyed. Furthermore, the size of calibration area with respect to the area covered by the species occurrences has been shown to affect model accuracy. However, existing assessments of the effect of data inadequacy and calibration size on model accuracy have predominately been conducted using empirical studies. These studies can give ambiguous results, since the data used to train and test the model can both be biased.These limitations were addressed by applying simulated data to assess how inadequate data coverage and the size of calibration area affect the accuracy of species distribution models generated by MaxEnt and BIOCLIM. The validity of four presence-only performance measures, Contrast Validation Index (CVI), Boyce index, AUC and AUCratio, was also assessed.CVI, AUC and AUCratio ranked the accuracy of univariate models correctly according to the true importance of their defining environmental variable, a desirable property of an accuracy measure. Contrastingly, Boyce index failed to rank the accuracy of univariate models correctly and a high percentage of irrelevant variables produced models with a high Boyce index.Inadequate data coverage and increased calibration area reduced model accuracy by reducing the correct identification of the dominant environmental determinant. BIOCLIM outperformed MaxEnt models in predicting the true distribution of simulated species with a symmetric dominant response. However, MaxEnt outperformed BIOCLIM in predicting the true distribution of simulated species with skew and linear dominant responses. Despite this, the standard performance measures consistently overestimated the performance of MaxEnt models and showed them as always having higher model accuracy than the BIOCLIM models.It has been acknowledged that research should be directed towards testing and improving species distribution modelling tools, particularly how to handle the inevitable bias and scarcity of species occurrence data. Simulated data, as demonstrated here, provides a powerful approach to comprehensively test the performance of modelling tools and to disentangle the effects of data properties and modelling options on model accuracy. This may be impossible to achieve using real-world data.

scholarly journals Joint species distribution modelling with HMSC-R

2019 ◽  
Author(s):  
Gleb Tikhonov ◽  
Øystein Opedal ◽  
Nerea Abrego ◽  
Aleksi Lehikoinen ◽  
Otso Ovaskainen
Keyword(s):  
Community Ecology ◽  
Species Distribution ◽  
Phylogenetic Relationships ◽  
Real Data ◽  
Species Traits ◽  
Species Distribution Modelling ◽  
List Type ◽  
Large Species ◽  
Distribution Modelling ◽  
Environmental Covariates

AbstractJoint Species Distribution Modelling (JSDM) is becoming an increasingly popular statistical method for analyzing data in community ecology. JSDM allow the integration of community ecology data with data on environmental covariates, species traits, phylogenetic relationships, and the spatio-temporal context of the study, providing predictive insights into community assembly processes from non-manipulative observational data of species communities. Hierarchical Modelling of Species Communities (HMSC) is a general and flexible framework for fitting JSDMs, yet its full range of functionality has remained restricted to Matlab users only.To make HMSC accessible to the wider community of ecologists, we introduce HMSC-R 3.0, a user-friendly R implementation of the framework described in Ovaskainen et al (Ecology Letters, 20 (5), 561-576, 2017) and further extended in several later publications.We illustrate the use of the package by providing a series of five vignettes that apply HMSC-R 3.0 to simulated and real data. HMSC-R applications to simulated data involve single-species models, models of small communities, and models of large species communities. They demonstrate the estimation of species responses to environmental covariates and how these depend on species traits, as well as the estimation of residual species associations. They further demonstrate how HMSC-R can be applied to normally distributed data, count data, and presence-absence data. The real data consist of bird counts in a spatio-temporally structured dataset, environmental covariates, species traits and phylogenetic relationships. The vignettes demonstrate how to construct and fit many kinds of models, how to examine MCMC convergence, how to examine the explanatory and predictive powers of the models, how to assess parameter estimates, and how to make predictions.The package, along with the extended vignettes, makes JSDM fitting and post-processing easily accessible to ecologists familiar with R.

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