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 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 Evaluating species distribution models with discrimination accuracy is uninformative for many applications

2019 ◽  
Author(s):  
Dan L. Warren ◽  
Nicholas J. Matzke ◽  
Teresa L. Iglesias
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Current Approach ◽  
Data Partitioning ◽  
Empirical Modeling ◽  
Environmental Predictors ◽  
Discrimination Accuracy ◽  
Distribution Models ◽  
Ecological Processes ◽  
Occurrence Data

AbstractAimSpecies distribution models are used across evolution, ecology, conservation, and epidemiology to make critical decisions and study biological phenomena, often in cases where experimental approaches are intractable. Choices regarding optimal models, methods, and data are typically made based on discrimination accuracy: a model’s ability to predict subsets of species occurrence data that were withheld during model construction. However, empirical applications of these models often involve making biological inferences based on continuous estimates of relative habitat suitability as a function of environmental predictor variables. We term the reliability of these biological inferences “functional accuracy.” We explore the link between discrimination accuracy and functional accuracy.MethodsUsing a simulation approach we investigate whether models that make good predictions of species distributions correctly infer the underlying relationship between environmental predictors and the suitability of habitat.ResultsWe demonstrate that discrimination accuracy is only informative when models are simple and similar in structure to the true niche, or when data partitioning is geographically structured. However, the utility of discrimination accuracy for selecting models with high functional accuracy was low in all cases.Main conclusionsThese results suggest that many empirical studies and decisions are based on criteria that are unrelated to models’ usefulness for their intended purpose. We argue that empirical modeling studies need to place significantly more emphasis on biological insight into the plausibility of models, and that the current approach of maximizing discrimination accuracy at the expense of other considerations is detrimental to both the empirical and methodological literature in this active field. Finally, we argue that future development of the field must include an increased emphasis on simulation; methodological studies based on ability to predict withheld occurrence data may be largely uninformative about best practices for applications where interpretation of models relies on estimating ecological processes, and will unduly penalize more biologically informative modeling approaches.

Accounting for uncertainty when mapping species distributions: The need for maps of ignorance

2011 ◽  
Vol 35 (2) ◽  
pp. 211-226 ◽  
Author(s):  
Duccio Rocchini ◽  
Joaquín Hortal ◽  
Szabolcs Lengyel ◽  
Jorge M. Lobo ◽  
Alberto Jiménez-Valverde ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Species Distributions ◽  
Map Building ◽  
Dynamic Nature ◽  
Distribution Models ◽  
Good Practices ◽  
Sources Of Uncertainty ◽  
Fundamental Goal ◽  
Species Occurrences

Accurate mapping of species distributions is a fundamental goal of modern biogeography, both for basic and applied purposes. This is commonly done by plotting known species occurrences, expert-drawn range maps or geographical estimations derived from species distribution models. However, all three kinds of maps are implicitly subject to uncertainty, due to the quality and bias of raw distributional data, the process of map building, and the dynamic nature of species distributions themselves. Here we review the main sources of uncertainty suggesting a code of good practices in order to minimize their effects. Specifically, we claim that uncertainty should be always explicitly taken into account and we propose the creation of maps of ignorance to provide information on where the mapped distributions are reliable and where they are uncertain.

scholarly journals A Robust Prediction Model for Species Distribution Using Bagging Ensembles with Deep Neural Networks

2021 ◽  
Vol 13 (8) ◽  
pp. 1495
Author(s):  
Jehyeok Rew ◽  
Yongjang Cho ◽  
Eenjun Hwang
Keyword(s):  
Neural Networks ◽  
Species Distribution ◽  
Best Practice ◽  
Deep Neural Networks ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Environmental Data ◽  
Distribution Model ◽  
Distribution Models ◽  
Robust Prediction

Species distribution models have been used for various purposes, such as conserving species, discovering potential habitats, and obtaining evolutionary insights by predicting species occurrence. Many statistical and machine-learning-based approaches have been proposed to construct effective species distribution models, but with limited success due to spatial biases in presences and imbalanced presence-absences. We propose a novel species distribution model to address these problems based on bootstrap aggregating (bagging) ensembles of deep neural networks (DNNs). We first generate bootstraps considering presence-absence data on spatial balance to alleviate the bias problem. Then we construct DNNs using environmental data from presence and absence locations, and finally combine these into an ensemble model using three voting methods to improve prediction accuracy. Extensive experiments verified the proposed model’s effectiveness for species in South Korea using crowdsourced observations that have spatial biases. The proposed model achieved more accurate and robust prediction results than the current best practice models.

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