15 research outputs found
Population Structure of Humpback Whales from Their Breeding Grounds in the South Atlantic and Indian Oceans
Although humpback whales are among the best-studied of the large whales, population boundaries in the Southern Hemisphere (SH) have remained largely untested. We assess population structure of SH humpback whales using 1,527 samples collected from whales at fourteen sampling sites within the Southwestern and Southeastern Atlantic, the Southwestern Indian Ocean, and Northern Indian Ocean (Breeding Stocks A, B, C and X, respectively). Evaluation of mtDNA population structure and migration rates was carried out under different statistical frameworks. Using all genetic evidence, the results suggest significant degrees of population structure between all ocean basins, with the Southwestern and Northern Indian Ocean most differentiated from each other. Effective migration rates were highest between the Southeastern Atlantic and the Southwestern Indian Ocean, followed by rates within the Southeastern Atlantic, and the lowest between the Southwestern and Northern Indian Ocean. At finer scales, very low gene flow was detected between the two neighbouring sub-regions in the Southeastern Atlantic, compared to high gene flow for whales within the Southwestern Indian Ocean. Our genetic results support the current management designations proposed by the International Whaling Commission of Breeding Stocks A, B, C, and X as four strongly structured populations. The population structure patterns found in this study are likely to have been influenced by a combination of long-term maternally directed fidelity of migratory destinations, along with other ecological and oceanographic features in the region
Supplementary Table S1
Supplementary data for: Blair, M.E., Rose, R.A., Ersts, P., Sanderson, E.W., Redford, K.H., Didier, K., Sterling, E.J., and R.G. Pearson. (2012) Incorporating climate change into conservation planning: Identifying priority areas across a species’ range. Frontiers in Biogeography, v.4(4). Permalink: http://escholarship.org/uc/item/5bx4919
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research letter: Incorporating climate change into conservation planning: Identifying priority areas across a species’ range
Theoretical and practical approaches associated with conservation biogeography, including ecological niche modeling, have been applied to the difficult task of determining how to incorporate climate change into conservation prioritization methodologies. Most studies have focused on identifying species that are most at risk from climate change, but here we asked, which areas within a species’ range does climate change threaten most? We explored methods for incorporating climate change within a range-wide conservation planning framework, using a case study of jaguars (Panthera onca). We used ecological niche models to estimate exposure to climate change across the range of the jaguar and incorporated these estimates into habitat quality scores for re-prioritization of high-priority areas for jaguar conservation. Methods such as these are needed to guide prioritization of geographically-specific actions for conservation across a species’ range
Recommended from our members
research letter: Incorporating climate change into conservation planning: Identifying priority areas across a species’ range
Theoretical and practical approaches associated with conservation biogeography, including ecological niche modeling, have been applied to the difficult task of determining how to incorporate climate change into conservation prioritization methodologies. Most studies have focused on identifying species that are most at risk from climate change, but here we asked, which areas within a species’ range does climate change threaten most? We explored methods for incorporating climate change within a range-wide conservation planning framework, using a case study of jaguars (Panthera onca). We used ecological niche models to estimate exposure to climate change across the range of the jaguar and incorporated these estimates into habitat quality scores for re-prioritization of high-priority areas for jaguar conservation. Methods such as these are needed to guide prioritization of geographically-specific actions for conservation across a species’ range
Pairwise measures of genetic divergence in various populations of Southern Hemisphere humpback whales, using all samples (Table 4), males + females (Table 5), females only (Table 6) and males only (Table 7).
<p>Pairwise Φst and Fst values are above and below the diagonal, respectively. Significant values are highlighted in bold.</p
Sample location, size, mtDNA control region variability for breeding grounds and migratory corridors of Southern Hemisphere humpback whales.
<p>Region C1 groups samples from Mozambique (<i>M</i>) and Eastern South Africa (<i>ESA</i>), and Region C3 groups samples from Antongil Bay (<i>AB</i>) and Southern Madagascar (<i>SM</i>). Haplotype (h) and nucleotide (Ï€) diversities, as well as their standard deviations are provided. Numbers of males and females do not always add up to the sample size, given that the dataset contains individuals sex. Duplicate samples were removed from the analysis.</p
Pairwise measures of genetic divergence in various populations of Southern Hemisphere humpback whales, using all samples (Table 4), males + females (Table 5), females only (Table 6) and males only (Table 7).
<p>Pairwise Φst and Fst values are above and below the diagonal, respectively. Significant values are highlighted in bold.</p
IWC boundaries for humpback whale breeding grounds and feeding areas in the South Atlantic and Indian Oceans.
<p>Sampling locations are indicated in parentheses and referred to in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007318#pone-0007318-t001" target="_blank">Table 1</a>.</p
Chi-Square test for differences in haplotype frequencies for four breeding Regions (A, B, C and X) of Southern Hemisphere humpback whales.
<p>All strata based on sex of animals are shown. The P-value is the probability of a more extreme variance component or F-value than that observed, in comparison to a null distribution of these values on 1,000 random permutations of the data matrix. Significant values (p<0.05) are highlighted in bold.</p