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Can we detect oceanic biodiversity hotspots from space?

ABSTRACT: Understanding the variability of marine biodiversity is a central issue in microbiology. Current observational programs are based on in situ studies, but their implementation at the global scale is particularly challenging, owing to the ocean extent, its temporal variability and the heterogeneity of the data sources on which compilations are built. Here, we explore the possibility of identifying phytoplanktonic biodiversity hotspots from satellite. We define a Shannon entropy index based on patchiness in ocean color bio-optical anomalies. This index provides a high resolution (1 degree) global coverage. It shows a relation to temperature and mid-latitude maxima in accordance with those previously evidenced in microbiological biodiversity model and observational studies. Regional maxima are in remarkable agreement with several known biodiversity hotspots for plankton organisms and even for higher levels of the marine trophic chain, as well as with some in situ planktonic biodiversity estimates (from Atlantic Meridional Transect cruise). These results encourage to explore marine biodiversity with a coordinated effort of the molecular, ecological and remote sensing communities.

SUBMITTER: De Monte S

PROVIDER: S-EPMC3965306 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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Can we detect oceanic biodiversity hotspots from space?

De Monte Silvia S Soccodato Alice A Alvain Séverine S d'Ovidio Francesco F

The ISME journal 20130502 10

Understanding the variability of marine biodiversity is a central issue in microbiology. Current observational programs are based on in situ studies, but their implementation at the global scale is particularly challenging, owing to the ocean extent, its temporal variability and the heterogeneity of the data sources on which compilations are built. Here, we explore the possibility of identifying phytoplanktonic biodiversity hotspots from satellite. We define a Shannon entropy index based on patc ...[more]

PMID: 23635866

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Project description:With the ongoing crisis of biodiversity loss and limited resources for conservation, the concept of biodiversity hotspots has been useful in determining conservation priority areas. However, there has been limited research into how temporal variability in biodiversity may influence conservation area prioritization. To address this information gap, we present an approach to evaluate the temporal consistency of biodiversity hotspots in large marine ecosystems. Using a large scale, public monitoring dataset collected over an eight year period off the US Pacific Coast, we developed a methodological approach for avoiding biases associated with hotspot delineation. We aggregated benthic fish species data from research trawls and calculated mean hotspot thresholds for fish species richness and Shannon's diversity indices over the eight year dataset. We used a spatial frequency distribution method to assign hotspot designations to the grid cells annually. We found no areas containing consistently high biodiversity through the entire study period based on the mean thresholds, and no grid cell was designated as a hotspot for greater than 50% of the time-series. To test if our approach was sensitive to sampling effort and the geographic extent of the survey, we followed a similar routine for the northern region of the survey area. Our finding of low consistency in benthic fish biodiversity hotspots over time was upheld, regardless of biodiversity metric used, whether thresholds were calculated per year or across all years, or the spatial extent for which we calculated thresholds and identified hotspots. Our results suggest that static measures of benthic fish biodiversity off the US West Coast are insufficient for identification of hotspots and that long-term data are required to appropriately identify patterns of high temporal variability in biodiversity for these highly mobile taxa. Given that ecological communities are responding to a changing climate and other environmental perturbations, our work highlights the need for scientists and conservation managers to consider both spatial and temporal dynamics when designating biodiversity hotspots.

Project description:India is one of the 12 mega biodiversity countries of the world, which represents 11% of world's flora in about 2.4% of global land mass. Approximately 28% of the total Indian flora and 33% of angiosperms occurring in India are endemic. Higher human population density in biodiversity hotspots in India puts undue pressure on these sensitive eco-regions. In the present study, we predict the future distribution of 637 endemic plant species from three biodiversity hotspots in India; Himalaya, Western Ghats, Indo-Burma, based on A1B scenario for year 2050 and 2080. We develop individual variable based models as well as mixed models in MaxEnt by combining ten least co-related bioclimatic variables, two disturbance variables and one physiography variable as predictor variables. The projected changes suggest that the endemic flora will be adversely impacted, even under such a moderate climate scenario. The future distribution is predicted to shift in northern and north-eastern direction in Himalaya and Indo-Burma, while in southern and south-western direction in Western Ghats, due to cooler climatic conditions in these regions. In the future distribution of endemic plants, we observe a significant shift and reduction in the distribution range compared to the present distribution. The model predicts a 23.99% range reduction and a 7.70% range expansion in future distribution by 2050, while a 41.34% range reduction and a 24.10% range expansion by 2080. Integration of disturbance and physiography variables along with bioclimatic variables in the models improved the prediction accuracy. Mixed models provide most accurate results for most of the combinations of climatic and non-climatic variables as compared to individual variable based models. We conclude that a) regions with cooler climates and higher moisture availability could serve as refugia for endemic plants in future climatic conditions; b) mixed models provide more accurate results, compared to single variable based models.

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Can we detect oceanic biodiversity hotspots from space?

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Can we detect oceanic biodiversity hotspots from space?

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