Bioclimatic variables derived from remote sensing: assessment and application for species distribution modelling

Waltari, Eric; Schroeder, R.; McDonald, K. C.; Anderson, Robert P.; Carnaval, Ana Carolina

doi:10.1111/2041-210x.12264

Cited by 45 publications

(25 citation statements)

References 49 publications

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“…; Waltari et al. ). When uncertainty in spatial climate variables is not accounted for, coefficient estimates tend to be biased which lead to poor performances of SDMs as shown with recent simulations (Stoklosa et al.…”

Section: Remote Sensing Of Environmental Conditions: the Predictor Vamentioning

confidence: 97%

Will remote sensing shape the next generation of species distribution models?

Bradley

Cord

et al. 2015

Remote Sens Ecol Conserv

283

239

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Two prominent limitations of species distribution models (SDMs) are spatial biases in existing occurrence data and a lack of spatially explicit predictor variables to fully capture habitat characteristics of species. Can existing and emerging remote sensing technologies meet these challenges and improve future SDMs? We believe so. Novel products derived from multispectral and hyperspectral sensors, as well as future Light Detection and Ranging (LiDAR) and RADAR missions, may play a key role in improving model performance. In this perspective piece, we demonstrate how modern sensors onboard satellites, planes and unmanned aerial vehicles are revolutionizing the way we can detect and monitor both plant and animal species in terrestrial and aquatic ecosystems as well as allowing the emergence of novel predictor variables appropriate for species distribution modeling. We hope this interdisciplinary perspective will motivate ecologists, remote sensing experts and modelers to work together for developing a more refined SDM framework in the near future.

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Section: Remote Sensing Of Environmental Conditions: the Predictor Vamentioning

confidence: 97%

Will remote sensing shape the next generation of species distribution models?

Bradley

Cord

et al. 2015

Remote Sens Ecol Conserv

283

239

View full text Add to dashboard Cite

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“…Climatic layers based on satellites or statistical downscaling have been considered better‐performing than interpolated methods, as registered from in situ observations (Waltari, Schroeder, Mcdonald, Anderson, & Carnaval, ; cf. Deblauwe et al., ).…”

Section: Introductionmentioning

confidence: 99%

“…Satellite climatic layers have some pitfalls as well, showing a general bias towards warmer and drier estimations that has been observed particularly in low and mid‐latitude regions. The issues at high latitudes are related to seasonal dynamics of inconsistent soil moisture, compared to in situ observations (Ashouri, Sorooshian, Hsu, Bosilovich, & Wehner, ; Bosilovich, ; Waltari et al., ; Yi et al., ). Vega et al.…”

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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“…In contrast, the University of Montana (UMT) global Land Parameter Data Record version 1 (LPDR v1) was developed to exploit AMSR-E multifrequency T b observations for global daily mapping of multiple synergistic land parameters related to the status and storage of water in the atmosphere, vegetation, and soil . The LPDR v1 database has been applied for a variety of environmental studies, including quantifying surface water inundation impacts on tundra methane emissions (Watts et al, 2014), boreal wildfire disturbance and recovery assessments (Jones et al, 2013), evaluating hydroclimatic controls on vegetation phenology (Alemu and Henebry, 2013;Guan et al, 2014), biodiversity modeling and prediction (Waltari et al, 2014), and vector-borne disease risk assessments (Chuang et al, 2012). The LPDR v1 has also served as a baseline for evaluating other AMSR-E algorithm retrievals (Mladenova et al, 2014) and refinements (Jang et al, 2014;Du et al, 2014).…”

mentioning

confidence: 99%

A global satellite environmental data record derived from AMSR-E and AMSR2 microwave Earth observations

Kimball

Jones

et al. 2017

Earth Syst. Sci. Data

142

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Abstract. Spaceborne microwave remote sensing is widely used to monitor global environmental changes for understanding hydrological, ecological, and climate processes. A new global land parameter data record (LPDR) was generated using similar calibrated, multifrequency brightness temperature (T b ) retrievals from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2). The resulting LPDR provides a long-term (June 2002-December 2015 global record of key environmental observations at a 25 km grid cell resolution, including surface fractional open water (FW) cover, atmosphere precipitable water vapor (PWV), daily maximum and minimum surface air temperatures (T mx and T mn ), vegetation optical depth (VOD), and surface volumetric soil moisture (VSM). Global mapping of the land parameter climatology means and seasonal variability over the full-year records from AMSR-E (2003) and AMSR2 (2013 observation periods is consistent with characteristic global climate and vegetation patterns. Quantitative comparisons with independent observations indicated favorable LPDR performance for FW (R ≥ 0.75; RMSE ≤ 0.06), PWV (R ≥ 0.91; RMSE ≤ 4.94 mm), T mx and T mn (R ≥ 0.90; RMSE ≤ 3.48 • C), and VSM (0.63 ≤ R ≤ 0.84; bias-corrected RMSE ≤ 0.06 cm 3 cm −3 ). The LPDR-derived global VOD record is also proportional to satellite-observed NDVI (GIMMS3g) seasonality (R ≥ 0.88) due to the synergy between canopy biomass structure and photosynthetic greenness. Statistical analysis shows overall LPDR consistency but with small biases between AMSR-E and AMSR2 retrievals that should be considered when evaluating long-term environmental trends. The resulting LPDR and potential updates from continuing AMSR2 operations provide for effective global monitoring of environmental parameters related to vegetation activity, terrestrial water storage, and mobility and are suitable for climate and ecosystem studies. The LPDR dataset is publicly available at

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Bioclimatic variables derived from remote sensing: assessment and application for species distribution modelling

Cited by 45 publications

References 49 publications

Will remote sensing shape the next generation of species distribution models?

Will remote sensing shape the next generation of species distribution models?

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

A global satellite environmental data record derived from AMSR-E and AMSR2 microwave Earth observations

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