Locality Uncertainty and the Differential Performance of Four Common Niche-Based Modeling Techniques

Fernández, Miguel; Blum, Stanley; Reichle, Steffen; Holzman, Barbara A.; Hamilton, Healy

doi:10.17161/bi.v6i1.3314

Cited by 33 publications

(42 citation statements)

References 73 publications

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“…niche breadth) characteristics of the studied species (Luoto et al 2005, Bulluck et al 2006, McPherson and Jetz 2007, Evangelista et al 2008, Chefaoui et al 2011, Connor et al 2018. Similar effects can be caused by a low positional accuracy of the occurrences (Johnson and Gillingham 2008, Fernandez et al 2009, Osborne and Leitão 2009. the range of environments that the species can inhabit), in other words that species able to tolerate a wider range of conditions are typically more widespread (Brown 1984, Gaston et al 1997, Arribas et al 2012, Boulangeat et al 2012).…”

Section: Introductionmentioning

confidence: 87%

The effect of positional error on fine scale species distribution models increases for specialist species

et al. 2019

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Species occurrences inherently include positional error. Such error can be problematic for species distribution models (SDMs), especially those based on fine-resolution environmental data. It has been suggested that there could be a link between the influence of positional error and the width of the species ecological niche. Although positional errors in species occurrence data may imply serious limitations, especially for modelling species with narrow ecological niche, it has never been thoroughly explored. We used a virtual species approach to assess the effects of the positional error on fine-scale SDMs for species with environmental niches of different widths. We simulated three virtual species with varying niche breadth, from specialist to generalist. The true distribution of these virtual species was then altered by introducing different levels of positional error (from 5 to 500 m). We built generalized linear models and MaxEnt models using the distribution of the three virtual species (unaltered and altered) and a combination of environmental data at 5 m resolution. The models' performance and niche overlap were compared to assess the effect of positional error with varying niche breadth in the geographical and environmental space. The positional error negatively impacted performance and niche overlap metrics. The amplitude of the influence of positional error depended on the species niche, with models for specialist species being more affected than those for generalist species. The positional error had the same effect on both modelling techniques. Finally, increasing sample size did not mitigate the negative influence of positional error. We showed that fine-scale SDMs are considerably affected by positional error, even when such error is low. Therefore, where new surveys are undertaken, we recommend paying attention to data collection techniques to minimize the positional error in occurrence data and thus to avoid its negative effect on SDMs, especially when studying specialist species.

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Section: Introductionmentioning

confidence: 87%

The effect of positional error on fine scale species distribution models increases for specialist species

et al. 2019

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“…A different source of error, which should be addressed before modeling, is the imprecision of GIS coordinates in the occurrence data; this error is accentuated when the number of occurrences available is small. Previous studies have shown that boosted regression trees and MaxEnt are less influenced by these types of error, [42]; however a recent study demonstrated that for other species, GARP was less influenced, supporting the use of a variety of modeling techniques as opposed to a single one [43]. Another important factor was to calibrate the models for regions with good data availability (i.e., using a restricted extent of the region modeled, which in our study meant excluding north-western Russia from input in the models and then projecting to it) instead of using large extents of suitable conditions for which poor occurrence data exists [8], [23].…”

Section: Discussionmentioning

confidence: 96%

Predicting the Fate of Biodiversity Using Species’ Distribution Models: Enhancing Model Comparability and Repeatability

et al. 2012

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Species distribution modeling (SDM) is an increasingly important tool to predict the geographic distribution of species. Even though many problems associated with this method have been highlighted and solutions have been proposed, little has been done to increase comparability among studies. We reviewed recent publications applying SDMs and found that seventy nine percent failed to report methods that ensure comparability among studies, such as disclosing the maximum probability range produced by the models and reporting on the number of species occurrences used. We modeled six species of Falco from northern Europe and demonstrate that model results are altered by (1) spatial bias in species’ occurrence data, (2) differences in the geographic extent of the environmental data, and (3) the effects of transformation of model output to presence/absence data when applying thresholds. Depending on the modeling decisions, forecasts of the future geographic distribution of Falco ranged from range contraction in 80% of the species to no net loss in any species, with the best model predicting no net loss of habitat in Northern Europe. The fact that predictions of range changes in response to climate change in published studies may be influenced by decisions in the modeling process seriously hampers the possibility of making sound management recommendations. Thus, each of the decisions made in generating SDMs should be reported and evaluated to ensure conclusions and policies are based on the biology and ecology of the species being modeled.

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“…In this study, we decided to focus on the most common uses for GBIF-mobilised data reported in the scientific literature: ecological niche modelling (ENM) (Grinnell, 1917;Fernández et al, 2009;Peterson and Vieglais, 2001) and related analyses. The compilation of scientific literature using or citing GBIF is available since 2011 on-line at: http://www.mendeley.com/groups/1068301/gbifpublic-library/.…”

Section: C-'fitness-for-use' Assessmentmentioning

confidence: 99%

Content assessment of the primary biodiversity data published through GBIF network: Status, challenges and potentials

et al. 2013

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-With the establishment of the Global Biodiversity Information Facility (GBIF) in 2001 as an inter-governmental coordinating body, concerted efforts have been made during the past decade to establish a global research infrastructure to facilitate the publishing, discovery, and access to primary biodiversity data. The participants in GBIF have enabled the access to over 377 million records of such data as of August 2012. This is a remarkable achievement involving efforts at national, regional and global levels in multiple areas such as data digitization, standardization and exchange protocols. However concerns about the quality and 'fitness for use' of the data mobilized in particular for the scientific communities have grown over the years and must now be carefully considered in future developments. This paper is the first comprehensive assessment of the content mobilised so far through GBIF, as well as a reflexion on possible strategies to improve its 'fitness for use'. The methodology builds on complementary approaches adopted by the GBIF Secretariat and the University of Navarra for the development of comprehensive content assessment methodologies. The outcome of this collaborative research demonstrates the immense value of the GBIF mobilized data and its potential for the scientific communities. Recommendations are provided to the GBIF community to improve the quality of the data published as well as priorities for future data mobilization.

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Locality Uncertainty and the Differential Performance of Four Common Niche-Based Modeling Techniques

Cited by 33 publications

References 73 publications

The effect of positional error on fine scale species distribution models increases for specialist species

The effect of positional error on fine scale species distribution models increases for specialist species

Predicting the Fate of Biodiversity Using Species’ Distribution Models: Enhancing Model Comparability and Repeatability

Content assessment of the primary biodiversity data published through GBIF network: Status, challenges and potentials

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