Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions - PubMed (original) (raw)

Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions

Adam M Wilson et al. PLoS Biol. 2016.

Abstract

Cloud cover can influence numerous important ecological processes, including reproduction, growth, survival, and behavior, yet our assessment of its importance at the appropriate spatial scales has remained remarkably limited. If captured over a large extent yet at sufficiently fine spatial grain, cloud cover dynamics may provide key information for delineating a variety of habitat types and predicting species distributions. Here, we develop new near-global, fine-grain (≈1 km) monthly cloud frequencies from 15 y of twice-daily Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images that expose spatiotemporal cloud cover dynamics of previously undocumented global complexity. We demonstrate that cloud cover varies strongly in its geographic heterogeneity and that the direct, observation-based nature of cloud-derived metrics can improve predictions of habitats, ecosystem, and species distributions with reduced spatial autocorrelation compared to commonly used interpolated climate data. These findings support the fundamental role of remote sensing as an effective lens through which to understand and globally monitor the fine-grain spatial variability of key biodiversity and ecosystem properties.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1

Fig 1. Global 1 km cloud metrics.

A. Mean annual cloud frequency (%) over 2000–2014. B Inter-annual variability in cloud frequency (mean of 12 monthly standard deviations). C. Spatial variability (standard deviation of mean annual cloud frequency within a one-degree, ≈110 km, circular moving window). D Intra-annual variability in cloud frequency (standard deviation of 12 monthly mean cloud frequencies). Grey indicates the (A) median global cloud frequency (51%) and (B,D) median inter-annual variability (11%), blues indicate areas with below-median values, and reds indicate areas with higher-than-median values. Data are available only for MODIS land tiles, resulting in missing data in black tiles over oceans. For further exploration see

http://earthenv.org/cloud

and for download see

http://doi.org/10.6084/m9.figshare.1531955

.

Fig 2

Fig 2. Seasonal cloud concentration.

A. Color key illustrating the distribution of global cloud seasonality and concentration. The hue indicates the month of peak cloudiness, while the saturation and value indicate the magnitude of the concentration ranging from 0 (black, all months are equally cloudy) to 100 (all clouds are observed in a single month). B. Global distribution of seasonal cloud concentration with two red boxes indicating the locations of panels C and D. Coastlines shown in white, areas with no data are dark grey. C. Regional plot of northern South America illustrating the transition from June–July–August to December–January–February cloudiness with little seasonality (dark colors) at high elevations. D. Regional plot of southern Africa illustrating the transition from the Mediterranean climate in the southwest to the summer rainfall region in the northeast. Note the incursions of summer clouds and associated rainfall (red colors) along the southern coast. In C and D, red lines indicate ecoregion boundaries [35]. For further exploration see

http://www.earthenv.org/cloud

and for download see

http://doi.org/10.6084/m9.figshare.1531955

.

Fig 3

Fig 3. Global hotspots of temporal cloud cover constancy.

A. Geographic locations of minima in cloud dynamics using colors defined in C–E. B. Inset showing detail (red square in A) over East African Biodiversity Hotspots. C–E. Scatterplot of pixels in A and B, which serves as a color key to the map. Colored pixels indicate locations in the top 10% quantile of mean annual cloud frequency (see D and E) and bottom 10% quantile of intra-annual cloud variability (blues), inter-annual cloud variability (reds), or both (greens). Lines in scatterplot indicate the 10th (and 90th for mean annual) percentiles. For further exploration see

http://www.earthenv.org/cloud

and for download see

http://doi.org/10.6084/m9.figshare.1531955

.

Fig 4

Fig 4. Tropical montane cloud forest distribution.

A–C: Distribution (relative occurrence rate) of tropical montane cloud forests estimated using an inhomogeneous point process model [47] of 529 cloud forest locations (black points) [48] with the new cloud metrics and elevation [7] (see Materials and Methods and S4 Table for modeling details). Panels show predictions for (A) South and Central America, (B) Africa, and (C) Southeast Asia/Australia. All panels share the color bar shown in panel C. For further exploration see

http://www.earthenv.org/cloud

and for download see

http://doi.org/10.6084/m9.figshare.1531955

.

Fig 5

Fig 5. Evaluation of cloud data in species distribution models.

A–F. Evaluation of predictions from species distribution models of (A,B,E,F) montane woodcreeper (Lepidocolaptes lacrymiger, blue symbols) and (C,D,E,F) king protea (Protea cynaroides, red symbols). A,C are estimated probability of presence from species distribution models fit using cloud frequency, while B,D use interpolated precipitation. Insets in A–D show detail from boxed region. Gray points indicate locations with non-detections, while red/blue “+” marks indicate observed presences. E. correlograms of the spatial autocorrelation of the data in A–D, in which solid lines indicate models built with cloud data (A,C) and dashed lines indicate predictions from a model built using interpolated precipitation data (B,D). F. Estimated probability of presence, in which the species has been undetected at locations with at least five trials or observed (colors/lines as in E), box widths proportional to the number of observations. Data available at

http://doi.org/10.6084/m9.figshare.1531955

.

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Grants and funding

National Science Foundation (NSF.gov) to WJ: DBI 1262600, DEB 1026764, and DEB 1441737. National Aeronautics and Space Administration (nasa.gov) to WJ: NASA NNX11AP72G. Yale Climate and Energy Institute postdoctoral fellowship to AW. Publication costs were covered in part by the Julian Park Fund at the University at Buffalo College of Arts of Sciences. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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