MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling

Vega, Greta C.; Pertierra, Luis R.; Olalla‐Tárraga, Miguel Á.

doi:10.1038/sdata.2017.78

Cited by 132 publications

(111 citation statements)

References 45 publications

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“…We compiled temperature and precipitation layers from the following recently updated databases: WorldClim v. 2 (http://www.worldclim.org/version2), MERRAclim (Vega et al., , ), CHELSA (http://www.chelsa-climate.org), and ocean databases MARSPEC (http://www.marspec.org) and Bio‐ORACLE v. 2.0 (http://www.bio-oracle.org). To assess bioclimatic congruence (degree of agreement in environmental variables) between the considered databases, we chose straightforward measures of water and energy resources that can be used to model multiple taxa because water–energy relationship shapes distribution patterns of a wide range of plant and animal species (Hawkins et al., ).…”

Section: Methodsmentioning

confidence: 99%

“…Vega et al. () even found differences in up to c .10°C in mean temperatures of the more extreme months when comparing two different databases. Given all these drawbacks, the selection of appropriate bioclimatic data becomes a complex task in distribution modelling that should be carefully accomplished, considering its limitations, and not simply assuming that the use of a single database is optimal for all regions and scales (Ashouri et al., ; Blacutt et al., ; Yi et al., ).…”

Section: Introductionmentioning

confidence: 97%

“…WorldClim provides interpolated data layers, consisting of long‐term average monthly temperature and precipitation values, based on observations from weather stations world‐wide. Recently, other open‐access bioclimatic databases have been created, based not merely on statistic interpolation from weather stations, but also on quasi‐mechanistic statistical downscaling (CHELSA: Karger et al., ), satellite information (MERRAclim: Vega, Pertierra, & Olalla‐Tárraga, , ), or a combination of these (Bio‐ORACLE: Assis et al., ; Tyberghein et al., ; MARSPEC: Sbrocco & Barber, ).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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

Morales‐Barbero

Vega‐Álvarez

2018

Methods Ecol Evol

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“…We analyze the ecological niche and geographic potential of this species in the context of bioclimatic variables derived from the WorldClim climate data archive, at a spatial resolution of 2.5′ (∼4.5 km). Although problems with this climate data archive have been signaled, presently it is the climate dataset that is best synchronized with future climate datasets, such that it is generally the dataset used in such studies. All data and R scripts that we have developed, derived, and used in elaborating the case study examples are available at https://goo.gl/c7Tx4o.…”

Section: Case Studymentioning

confidence: 99%

Major challenges for correlational ecological niche model projections to future climate conditions

Peterson

Cobos

Jiménez‐García

2018

Annals of the New York Academy of Sciences

115

117

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Species-level forecasts of distributional potential and likely distributional shifts, in the face of changing climates, have become popular in the literature in the past 20 years. Many refinements have been made to the methodology over the years, and the result has been an approach that considers multiple sources of variation in geographic predictions, and how that variation translates into both specific predictions and uncertainty in those predictions. Although numerous previous reviews and overviews of this field have pointed out a series of assumptions and caveats associated with the methodology, three aspects of the methodology have important impacts but have not been treated previously in detail. Here, we assess those three aspects: (1) effects of niche truncation on model transfers to future climate conditions, (2) effects of model selection procedures on future-climate transfers of ecological niche models, and (3) relative contributions of several factors (replicate samples of point data, general circulation models, representative concentration pathways, and alternative model parameterizations) to overall variance in model outcomes. Overall, the view is one of caution: although resulting predictions are fascinating and attractive, this paradigm has pitfalls that may bias and limit confidence in niche model outputs as regards the implications of climate change for species' geographic distributions.

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“…Here, the biotic component of the parasite, B, is denoted by the fundamental niche of the dengue vector, the mosquito Aedes aegypti (red polyhedron) [8]. A (gray points) summarizes global bioclimatic variables [89] condensed in three principal components. Finally, the dispersal potential, M, was restricted to Guatemala as the area of interest (black polyhedron).…”

Section: Niches In Host-spacementioning

confidence: 99%

An Ecological Framework for Modeling the Geography of Disease Transmission

Johnson

Escobar

Zambrana‐Torrelio

2019

Trends in Ecology & Evolution

111

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Ecological niche modeling (ENM) is widely employed in ecology to predict species' potential geographic distributions in relation to their environmental constraints and is rapidly becoming the gold-standard method for disease risk mapping. However, given the biological complexity of disease systems, the traditional ENM framework requires reevaluation. We provide an overview of the application of ENM to disease systems and propose a theoretical framework based on the biological properties of both hosts and parasites to produce reliable outputs resembling disease system distributions. Additionally, we discuss the differences between biological considerations when implementing ENM for distributional ecology and epidemiology. This new framework will help the field of disease ecology and applications of biogeography in the epidemiology of infectious diseases. Challenges and Opportunities to Map Disease RiskThe recent rise of emerging infectious diseases (EIDs) (see Glossary) [1] has increased the burden of infectious diseases and negatively impacted the global economy [2][3][4][5]. Approximately 60% of emerging human diseases are caused by pathogenic parasites of animal origin (zoonoses), particularly wildlife [6]. As human activities intensify, contact with wildlife and exposure to novel parasites increase, potentially driving zoonotic disease emergence [1,7]. Given the threat that EIDs pose to human populations, understanding the underlying drivers of parasite geographic distribution and their spillover to humans is particularly relevant for epidemiologists, public-health practitioners, and policy makers [9].Ecological niche modeling (ENM) has proven useful to forecast the distribution of a vast number of organisms [10-13] and is increasingly employed to predict parasite distributions locally and globally [14][15][16][17]. Despite great strides made in the implementation of ENM to forecast complex biological phenomena such as disease systems [18], traditional frameworks may render biologically unrealistic predictions and thus must be revised, as we show in this review. We provide an overview of the current state of disease ENM and propose a framework based on the biological properties of both parasites and hosts to produce reliable outputs resembling disease systems distributions. Specifically, our theoretical framework: (i) addresses the selection of an appropriate modeling approach and highlights the importance of including biologically sound predictor variables; (ii) proposes the concept of a microscale parasitic niche defined by host traits to identify relevant parasite-host associations; and (iii) integrates traditional parasite ENM with the proposed microscale niche to better understand geographic distributions and improve fine-scale predictions of disease transmission risk. ENM and Biotic InteractionsENM estimates the distributions of species by linking their geographic occurrence with their environmental constraints, often utilizing correlative approaches (detailed explanations in [18]). Highlights

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MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling

Cited by 132 publications

References 45 publications

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

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

Major challenges for correlational ecological niche model projections to future climate conditions

An Ecological Framework for Modeling the Geography of Disease Transmission

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