Ecological niche model comparison under different climate scenarios: a case study of<i>Olea</i>spp. in Asia

Ashraf, Uzma; Peterson, A. Townsend; Chaudhry, Muhammad Nawaz; Ashraf, Irfan; Saqib, Zafeer; Ahmad, Sajid Rashid; Ali, Hassan

doi:10.1002/ecs2.1825

Cited by 77 publications

(58 citation statements)

References 38 publications

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“…Methods Thomas et al, 2003Lu et al, 2004Ma et al, 2017Maulik and Chakraborty, 2017Guisan and Thuiller, 2005Elith and Leathwick, 2009Franklin, 2010Li and Wang, 2013Morris et al, 2016Ashraf et al, 2017de Rivera and López-Quílez, 2017 can be approached from two perspectives: the data collection methods and the map production methods. Developments in data collection techniques in the last few decades have increased the types, amount and quality of data that can be collected for marine environmental characterization, particularly in terms of remotely sensed data (Brown et al, 2011;Kachelriess et al, 2014;Lecours et al, 2016b).…”

Section: Marine Habitat Mappingmentioning

confidence: 99%

On the Use of Maps and Models in Conservation and Resource Management (Warning: Results May Vary)

Lecours

2017

Front. Mar. Sci.

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Conservation planning and management typically require accurate and spatially explicit data at scales that are relevant for conservation objectives. In marine conservation, these data are often combined with spatial analytical techniques to produce marine habitat maps. While marine habitat mapping is increasingly used to inform conservation efforts, this field is still relatively young and its methods are rapidly evolving. Because conservation efforts do not always specify standards or guidelines for the production of habitat maps, results can vary dramatically. As representations of real environmental characteristics, habitat maps are highly sensitive to how they are produced. In this review paper, I present four concepts that are known to cause variation in spatial representation and prediction of habitats: the methodology used, the quality and scale of the data, and the choice of variables in regards to fitness for use. I then discuss the potential antinomy associated with the use of habitat maps in conservation: while habitat maps have become an invaluable tool to inform and assist decision-making, maps of the same area built using different methods and data may provide dissimilar representations, thus providing different information and possibly leading to different decisions. Exploring the theories and methods that have proved effective in terrestrial conservation and the spatial sciences, and how they can be integrated in marine habitat mapping practices, could help improve maps used to support marine conservation efforts and result in more reliable products to inform conservation decisions. Having a strong, consistent, transparent, repeatable, and science-based protocol for data collection and mapping is essential for effectively supporting decision-makers in developing conservation and management plans. The development of user-friendly tools to assist in the application of such protocol is crucial to a widespread improvement in practices. I discuss the potential to use interactive and collaborative Geographic Information Systems (GIS) to encourage the conservation and management community, from data collectors and mapmakers to decision-makers, to move toward a digital resilience and to develop such science-based protocol. Until standards and protocols are developed, habitat maps should always be interpreted with care, and the methods and metadata associated with their production should always be explicitly stated.

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Section: Marine Habitat Mappingmentioning

confidence: 99%

On the Use of Maps and Models in Conservation and Resource Management (Warning: Results May Vary)

Lecours

2017

Front. Mar. Sci.

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“…Through modelling we can summarize the multidimensional environmental space that limits the distribution of species, habitats or communities and project it against different spatial and temporal scenarios (Araújo & Peterson, 2012). Nonetheless, this methodology implies some degree of uncertainty mainly caused by: (a) input data biases or gaps (e.g., species occurrences), (b) modelling features (e.g., types of algorithms, threshold values), and (c) the inherent complexity of natural systems dynamics (e.g., species dispersal ability, biotic interactions) (Ashraf et al, 2017;Barry & Elith, 2006;Beale & Lennon, 2012;Elith & Leathwick, 2009;Graham & Hijmans, 2006;Luoto, Pöyry, Heikkinen, & Saarinen, 2005;Rocchini et al, 2011;Shabani, Kumar, & Ahmadi, 2016;Wisz et al, 2013). Inconsistencies in climatic input data are also an important source of uncertainty that affects the results of ecological hypotheses (Beaumont, Pitman, Poulsen, & Hughes, 2007;Soria-Auza et al, 2010;Varela, Lima-Ribeiro, & Terribile, 2015).…”

Section: Introductionmentioning

confidence: 99%

Input matters matter: Bioclimatic consistency to map more reliable species distribution models

Morales‐Barbero

Vega‐Álvarez

2018

Methods Ecol Evol

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Accuracy of global bioclimatic databases is essential to understand biodiversity–environment relationships. Many studies have explored biases and uncertainties related to species distribution models (SDMs) but the effect of choosing a specific database among the different alternatives has not been previously assessed. The lack of bioclimatic congruence (degree of agreement) between different databases is a main concern in distribution modelling and it is critical in single‐source models, for which the database choice is decisive. In order to prevent unreliable predictions derived from distorted input data, SDMs accuracy can be assessed by mapping model predictions according to a bioclimatic congruence measure derived from the comparison of multiple databases, which can be achieved with the bioclimatic consistency maps that we propose in this study. Here, (a) we present the first global‐scale bioclimatic congruence map to analyse environmental mismatches between recently updated bioclimatic databases. We also test the importance of input matters on the reliability of distribution models of sixteen mammals, by addressing (b) inconsistencies among species response curves (temperature and precipitation), and (c) discrepancies among SDMs predictions depending on the chosen bioclimatic database. Finally, (d) we propose a strategy to assess bioclimatic consistency of model predictions, showing its application to the specific case of Litocranius walleri. Our results confirm that the single‐source modelling approach greatly influences the estimation of species–environment relationship and consequently, bias spatial predictions derived from SDMs. This is especially true for studies conducted in polar and mountainous regions which showed the smallest bioclimatic congruence. We show that by adding bioclimatic congruence to SDMs projections, we can build a bioclimatic consistency map that enables the detection of both risky and consistent areas, as revealed for the case of L. walleri. Assessing uncertainty in bioclimatic input data is key to avoid erroneous conclusions in macroecological and biogeographical studies. The spatial characterization of bioclimatic consistency provides an adequate empirical framework which effectively illustrates bioclimatic data limitations. We strongly recommend that this new strategy should be formally and systematically incorporated into distribution modelling to build more reliable SDMs, which are essential to develop successful biodiversity conservation programmes.

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“…Ecological niche models for the nine species were constructed using six algorithms (in three categories) in order to develop a more robust analysis (Carvalho et al ., ; Ashraf et al ., ; Moo‐Llanes et al ., ): presence only (BIOCLIM, envelope score and environmental distances); presence/pseudopresence [GARP and a support vector machine (SVM)], and presence/background (MaxEnt) (Peterson et al ., ; Carvalho et al ., ). The algorithms used were: (a) BIOCLIM; (b) envelope score; (c) environmental distance; (d) GARP ( g enetic a lgorithm for r ule‐set p rediction); (e) maximum entropy (MaxEnt), and (f) the SVM.…”

Section: Methodsmentioning

confidence: 99%

Inferring distributional shifts of epidemiologically important North and Central American sandflies from Pleistocene to future scenarios

Moo‐Llanes

Pech-May

Ibarra-Cerdeña

et al. 2018

Medical Vet Entomology

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Nine sandfly species (Diptera: Psychodidae) are suspected or proven vectors of Leishmania spp. in the North and Central America region. The ecological niches for these nine species were modelled in three time periods and the overlaps for all time periods of the geographic predictions (G space), and of ecological dimensions using pairwise comparisons of equivalent niches (E space), were calculated. Two Nearctic, six Neotropical and one species in both bioregions occupied a reduced number of distribution areas. The ecological niche projections for most sandfly species other than Lutzomyia shannoni and Lutzomyia ovallesi have not expanded significantly since the Pleistocene. Only three species increase significantly to 2050, whereas all others remain stable. Lutzomyia longipalpis shared a similar ecological niche with more species than any other, although both L. longipalpis and Lutzomyia olmeca olmeca had conserved distributions over time. Climate change, at both regional and local levels, will play a significant role in the temporal and spatial distributions of sandfly species.

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Ecological niche model comparison under different climate scenarios: a case study ofOleaspp. in Asia

Cited by 77 publications

References 38 publications

On the Use of Maps and Models in Conservation and Resource Management (Warning: Results May Vary)

On the Use of Maps and Models in Conservation and Resource Management (Warning: Results May Vary)

Input matters matter: Bioclimatic consistency to map more reliable species distribution models

Inferring distributional shifts of epidemiologically important North and Central American sandflies from Pleistocene to future scenarios

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