Thermal energy and stress properties as the main drivers of regional distribution of coral species richness in the Indian Ocean

Mebrahtu Ateweberhan, Tim R. McClanahan, Joseph Maina, Charles Sheppard

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Aim: Large‐scale variation in species richness results from multiple environmental controls. We proposed to identify the main factors that influence species richness of reef corals to provide insight into natural forces and their implications for future climate impacts.

Location: Indian Ocean.

Methods: We applied quantile regression (QR) to predict coral species richness and to evaluate the influence of reef area, biogeographical connection – represented by reef area up‐current, geological provinces defined by plate tectonics, latitude, longitude and mainland‐island separation, the physico‐chemical factors of photosynthetically active radiation (PAR), salinity and primary productivity and seven properties of sea surface temperature (mean, minimum, maximum, standard deviation, range, kurtosis and skewness) on coral species richness. Predictions at three quantiles (0.50, 0.75 and 0.95) were compared against ordinary least squares regression (OLS) analysis.

Results: All four models successfully predicted species richness, with the 95% QR model showing the best fit. According to this model, reef area, number of reefs up‐current and mean‐SST had positive effects while maximum and kurtosis‐SST had negative effects on richness. The distribution of observed richness values in relation to the regression line suggested this model revealed the main limiting factors. High predicted richness sites were located in the region from Western Australia to Central Indian Ocean Islands and southern India, the Mozambique Channel and East Africa and Southern Red Sea and Gulf of Aden. Sites with low predicted richness were Arabian/Persian Gulf and Gulf of Oman, South Africa and south‐west Madagascar, Gulf of Kutch, Bay of Bengal and the Mascarenes.

Conclusions: Temperature properties played a prominent role in influencing species richness mainly as latent energy and stress rather than thermal stability. These thermal properties indicate the conditions needed to promote coral diversity and selecting climate refugia for conservation management.
LanguageEnglish
Pages1355-1366
Number of pages12
JournalJournal of Biogeography
Volume45
Issue number6
Early online date30 Apr 2018
DOIs
Publication statusPublished - Jun 2018
Externally publishedYes

Fingerprint

Indian Ocean
corals
coral
species richness
heat
species diversity
reefs
energy
reef
sea surface temperature
Indian Ocean Islands
climate
Bay of Bengal
Yemen
Persian Gulf
Mozambique
Oman
Red Sea
skewness
climate effect

Keywords

  • biodiversity
  • biogeography
  • ecological resilience
  • marine conservation
  • quantile regression
  • SST variability
  • thermal energy
  • thermal stress

Cite this

Ateweberhan, Mebrahtu ; McClanahan, Tim R. ; Maina, Joseph ; Sheppard, Charles. / Thermal energy and stress properties as the main drivers of regional distribution of coral species richness in the Indian Ocean. In: Journal of Biogeography. 2018 ; Vol. 45, No. 6. pp. 1355-1366.
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abstract = "Aim: Large‐scale variation in species richness results from multiple environmental controls. We proposed to identify the main factors that influence species richness of reef corals to provide insight into natural forces and their implications for future climate impacts.Location: Indian Ocean.Methods: We applied quantile regression (QR) to predict coral species richness and to evaluate the influence of reef area, biogeographical connection – represented by reef area up‐current, geological provinces defined by plate tectonics, latitude, longitude and mainland‐island separation, the physico‐chemical factors of photosynthetically active radiation (PAR), salinity and primary productivity and seven properties of sea surface temperature (mean, minimum, maximum, standard deviation, range, kurtosis and skewness) on coral species richness. Predictions at three quantiles (0.50, 0.75 and 0.95) were compared against ordinary least squares regression (OLS) analysis.Results: All four models successfully predicted species richness, with the 95{\%} QR model showing the best fit. According to this model, reef area, number of reefs up‐current and mean‐SST had positive effects while maximum and kurtosis‐SST had negative effects on richness. The distribution of observed richness values in relation to the regression line suggested this model revealed the main limiting factors. High predicted richness sites were located in the region from Western Australia to Central Indian Ocean Islands and southern India, the Mozambique Channel and East Africa and Southern Red Sea and Gulf of Aden. Sites with low predicted richness were Arabian/Persian Gulf and Gulf of Oman, South Africa and south‐west Madagascar, Gulf of Kutch, Bay of Bengal and the Mascarenes.Conclusions: Temperature properties played a prominent role in influencing species richness mainly as latent energy and stress rather than thermal stability. These thermal properties indicate the conditions needed to promote coral diversity and selecting climate refugia for conservation management.",
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Thermal energy and stress properties as the main drivers of regional distribution of coral species richness in the Indian Ocean. / Ateweberhan, Mebrahtu; McClanahan, Tim R.; Maina, Joseph; Sheppard, Charles.

In: Journal of Biogeography, Vol. 45, No. 6, 06.2018, p. 1355-1366.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Thermal energy and stress properties as the main drivers of regional distribution of coral species richness in the Indian Ocean

AU - Ateweberhan, Mebrahtu

AU - McClanahan, Tim R.

AU - Maina, Joseph

AU - Sheppard, Charles

PY - 2018/6

Y1 - 2018/6

N2 - Aim: Large‐scale variation in species richness results from multiple environmental controls. We proposed to identify the main factors that influence species richness of reef corals to provide insight into natural forces and their implications for future climate impacts.Location: Indian Ocean.Methods: We applied quantile regression (QR) to predict coral species richness and to evaluate the influence of reef area, biogeographical connection – represented by reef area up‐current, geological provinces defined by plate tectonics, latitude, longitude and mainland‐island separation, the physico‐chemical factors of photosynthetically active radiation (PAR), salinity and primary productivity and seven properties of sea surface temperature (mean, minimum, maximum, standard deviation, range, kurtosis and skewness) on coral species richness. Predictions at three quantiles (0.50, 0.75 and 0.95) were compared against ordinary least squares regression (OLS) analysis.Results: All four models successfully predicted species richness, with the 95% QR model showing the best fit. According to this model, reef area, number of reefs up‐current and mean‐SST had positive effects while maximum and kurtosis‐SST had negative effects on richness. The distribution of observed richness values in relation to the regression line suggested this model revealed the main limiting factors. High predicted richness sites were located in the region from Western Australia to Central Indian Ocean Islands and southern India, the Mozambique Channel and East Africa and Southern Red Sea and Gulf of Aden. Sites with low predicted richness were Arabian/Persian Gulf and Gulf of Oman, South Africa and south‐west Madagascar, Gulf of Kutch, Bay of Bengal and the Mascarenes.Conclusions: Temperature properties played a prominent role in influencing species richness mainly as latent energy and stress rather than thermal stability. These thermal properties indicate the conditions needed to promote coral diversity and selecting climate refugia for conservation management.

AB - Aim: Large‐scale variation in species richness results from multiple environmental controls. We proposed to identify the main factors that influence species richness of reef corals to provide insight into natural forces and their implications for future climate impacts.Location: Indian Ocean.Methods: We applied quantile regression (QR) to predict coral species richness and to evaluate the influence of reef area, biogeographical connection – represented by reef area up‐current, geological provinces defined by plate tectonics, latitude, longitude and mainland‐island separation, the physico‐chemical factors of photosynthetically active radiation (PAR), salinity and primary productivity and seven properties of sea surface temperature (mean, minimum, maximum, standard deviation, range, kurtosis and skewness) on coral species richness. Predictions at three quantiles (0.50, 0.75 and 0.95) were compared against ordinary least squares regression (OLS) analysis.Results: All four models successfully predicted species richness, with the 95% QR model showing the best fit. According to this model, reef area, number of reefs up‐current and mean‐SST had positive effects while maximum and kurtosis‐SST had negative effects on richness. The distribution of observed richness values in relation to the regression line suggested this model revealed the main limiting factors. High predicted richness sites were located in the region from Western Australia to Central Indian Ocean Islands and southern India, the Mozambique Channel and East Africa and Southern Red Sea and Gulf of Aden. Sites with low predicted richness were Arabian/Persian Gulf and Gulf of Oman, South Africa and south‐west Madagascar, Gulf of Kutch, Bay of Bengal and the Mascarenes.Conclusions: Temperature properties played a prominent role in influencing species richness mainly as latent energy and stress rather than thermal stability. These thermal properties indicate the conditions needed to promote coral diversity and selecting climate refugia for conservation management.

KW - biodiversity

KW - biogeography

KW - ecological resilience

KW - marine conservation

KW - quantile regression

KW - SST variability

KW - thermal energy

KW - thermal stress

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JF - Journal of Biogeography

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