Species’ Distribution Modeling for Conservation Educators and Practitioners

By Richard G. Pearson. Lessons in Conservation, 2010

656 citations recorded by the source corpus.

Models that predict distributions of species by combining known occurrence records with digital layers of environmental variables have much potential for application in conservation. Through using this module, teachers will enable students to develop species distribution models, to apply the models across a series of analyses, and to interpret predictions accurately. In addition to its original components, this module features an updated and condensed synthesis document ("A Brief Introduction to Species Distribution Modeling for Conservation Educators and Practitioners," which provides theoretical and practical guidance for the expanding field of species distribution modeling. The synthesis is supplemented by a new exercise where learners create and optimize species distribution models using Wallace, an R-based GUI (Graphical User Interface) application for ecological modeling that currently focuses on building, evaluating, and visualizing models of species niches and distributions. Additionally, there are four new PowerPoint presentations on species distribution models (the history and theory, data and algorithms, and evaluating SDMs), as well as a presentation on how to use Wallace. The original Synthesis, "Species' Distribution Modeling for Conservation Educators and Practitioners," introduces learners to the modeling approach, outlines key concepts and terminology, and describes questions that may be addressed using the approach. A theoretical framework that is fundamental to ensuring that students understand the uses and limitations of the models is then described. Additionally, it details the main steps in building and testing a distribution model, and describes three case studies that illustrate applications of the models. This module is targeted at a level suitable for teaching graduate students and conservation professionals.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/c8ccf4e7187ee37454ac2a350d9a8e22bef6ba85

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Citations in the corpus

Selecting thresholds for the prediction of species occurrence with presence‐only data (2013, methodology): “If both presence and absence data are available, there are many approaches for selecting a threshold or cut-off value to transform non-binary into binary predictions (Fielding & Bell, 1997; Liu et al., 2005; Jim enez-Valverde & Lobo, 2007; Pearson, 2007; Freeman & Moisen, 2008; Nenz en & Ara ujo, 20”
Can species distribution modelling provide estimates of population densities? A case study with jaguars in the Neotropics (2012, methodology): “We implemented two approaches to derive binary presence– absence maps from continuous maps of suitability (and to obtain the matrices of confusion): the ‘ROC plot-based approach’ threshold (it corresponds to the point on the ROC curve, which has the shortest distance to the top-left corner in ROC pl”
Predicting distributions of invasive species (2013, background): “ Richardson, D. M. and Thuiller, W. (2007).”
Maxent modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia (2009, background): “Since Maxent is mapping the fundamental niche (different from occupied niche) of the species using bioclimatic variables the suitable habitat for C. monticola may be overpredicted in some areas (Pearson 2007; Murienne et al., 2009).”
Use of Maximum Entropy Modeling in Wildlife Research (2009, methodology): “Kappa [3,26,27] and jackknife statistics [16,20,27] are also used to test models.”
Assessment of impact of climate change on Rhododendrons in Sikkim Himalayas using Maxent modelling: limitations and challenges (2012, methodology): “The ability of the model to predict the independent data can be assessed using a suitable test statistic (Pearson 2007).”
Accounting for spatially biased sampling effort in presence‐only species distribution modelling (2015, background): “This was indeed the effect of biased sampling effort on model performance, as indicated by the significant decrease in sensitivity across all species, which was indicative of underprediction of presences (Pearson, 2007).”
The challenge of modeling niches and distributions for data‐poor species: a comprehensive approach to model complexity (2018, methodology): “The jackknife (a method of withholding data) consists of n iterations; in each iteration n – 1 localities are used for calibration, and the model is evaluated on the withheld locality (Pearson 2007, Shcheglovitova and Anderson 2013).”
Species Distribution Models: Ecological Explanation and Prediction Across Space and Time (2009, neutral): “Instructional texts and training opportunities in species modeling are now available, including online texts (Pearson 2007) and university courses and workshops.”
Modelling potential distribution of the threatened tree species Juniperus oxycedrus: how to evaluate the predictions of different modelling approaches? (2011, background): “A high prediction success and at the same time a low rate of commission errors (Type-I errors) are attributed to models that successfully predict presence in a high proportion of known species occurrences while not predicting a large proportion of the study area as suitable (Hernandez et al. 2006; P”