Browsing by Subject "Southern Hemisphere"
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- ItemOpen AccessAssessments of the Southern Hemisphere humpback whale Breeding Stock B: Results for the models and sensitivities proposed at and following the 62nd meeting of the Scientific Committee of the IWC(2011) Müller, A; Butterworth, Doug S; Johnston Susan JThis document reports on the results of several models and sensitivities proposed at the 62nd meeting of the Scientific Committee (SC) of the IWC for the assessment of the Southern Hemisphere humpback whale Breeding Stock B, as well as some additional models and sensitivities that were proposed intersessionally. These models and sensitivities explore various stock structure hypotheses and alternative input data. Prior incoherence resulting from the implementation of genetic Nmin constraints was found to be a problem throughout the analyses, and the preferred way of including these constraints was the use of a global Nmin constraint for the Gabon and WSA regions combined. The estimated intrinsic growth rate for B1 ranges from 0.039 to 0.052 for the main models and 0.043 to 0.069 for B2. The estimated 2010 abundance relative to pristine level ranges from 0.508 to 0.750 for B1, and 0.045 to 0.124 for B2.
- ItemOpen AccessConcerning demographic limitations on the population growth rate of West Australian (breeding stock D) humpback whales(International Whaling Commission, 2011) Brandão, Anabela; Butterworth, Doug SThe upper bound of 0.126 on the maximum demographically possible annual growth rate for humpback whales that has standardly been imposed on recent applications of age-aggregated assessment models for this species in the IWC Scientific Committee, is based on an analysis that assumes steady age structure. It is conceivable that transient age-structure effects could admit greater population growth rates for short periods than suggested by such a bound. This possibility is addressed by developing an age-structured population model in which possible density dependent changes in pregnancy rate, age at first parturition and natural mortality are modelled explicitly, and allowance is made for the possibility of natural mortality increasing at older ages. The model is applied to the case of the west Australian humpback whale population (Breeding Stock D), for which breeding ground surveys over the 1982–1994 period provide a point estimate of 0.10 for the annual population growth rate. Results based upon the breeding population survey estimate of abundance of 10,032 in 1999 suggest that 0.12 is the maximum demographically feasible annual rate of increase for this stock over 1982–1994 if it is a closed population. This result is based on essentially the same parameter choices as led to the earlier r = 0.126 bound, i.e. that in the limit of low population size the age at first parturition approaches five years from above, the annual pregnancy rate 0.5 from below, and the annual natural mortality rate 0.01 from above. Transient effects do not appear able to reconcile the observed rate of increase with less extreme values of demographic parameters than led to the previously imposed upper bound of 0.126 on the maximum possible annual growth rate. Although use of extreme values reported for demographic parameters for Northern Hemisphere humpback whale populations, rather than those considered here, would reduce this suggested maximum rate of 0.12, the conclusion that transient effects have a very limited impact on observed population growth rates would be unlikely to change.
- ItemOpen AccessPhenological changes in the Southern Hemisphere(Public Library of Science, 2013) Chambers, Lynda E; Altwegg, Res; Barbraud, Christophe; Barnard, Phoebe; Beaumont, Linda J; Crawford, Robert J M; Durant, Joel M; Hughes, Lesley; Keatley, Marie R; Low, MattCurrent evidence of phenological responses to recent climate change is substantially biased towards northern hemisphere temperate regions. Given regional differences in climate change, shifts in phenology will not be uniform across the globe, and conclusions drawn from temperate systems in the northern hemisphere might not be applicable to other regions on the planet. We conduct the largest meta-analysis to date of phenological drivers and trends among southern hemisphere species, assessing 1208 long-term datasets from 89 studies on 347 species. Data were mostly from Australasia (Australia and New Zealand), South America and the Antarctic/subantarctic, and focused primarily on plants and birds. This meta-analysis shows an advance in the timing of spring events (with a strong Australian data bias), although substantial differences in trends were apparent among taxonomic groups and regions. When only statistically significant trends were considered, 82% of terrestrial datasets and 42% of marine datasets demonstrated an advance in phenology. Temperature was most frequently identified as the primary driver of phenological changes; however, in many studies it was the only climate variable considered. When precipitation was examined, it often played a key role but, in contrast with temperature, the direction of phenological shifts in response to precipitation variation was difficult to predict a priori . We discuss how phenological information can inform the adaptive capacity of species, their resilience, and constraints on autonomous adaptation. We also highlight serious weaknesses in past and current data collection and analyses at large regional scales (with very few studies in the tropics or from Africa) and dramatic taxonomic biases. If accurate predictions regarding the general effects of climate change on the biology of organisms are to be made, data collection policies focussing on targeting data-deficient regions and taxa need to be financially and logistically supported.
- ItemOpen AccessPlastic pollution in the world's oceans: more than 5 trillion plastic pieces weighing over 250,000 tons afloat at sea(Public Library of Science, 2014) Eriksen, Marcus; Lebreton, Laurent C M; Carson, Henry S; Thiel, Martin; Moore, Charles J; Borerro, Jose C; Galgani, Francois; Ryan, Peter G; Reisser, JuliaPlastic pollution is ubiquitous throughout the marine environment, yet estimates of the global abundance and weight of floating plastics have lacked data, particularly from the Southern Hemisphere and remote regions. Here we report an estimate of the total number of plastic particles and their weight floating in the world's oceans from 24 expeditions (2007-2013) across all five sub-tropical gyres, costal Australia, Bay of Bengal and the Mediterranean Sea conducting surface net tows (N = 680) and visual survey transects of large plastic debris (N = 891). Using an oceanographic model of floating debris dispersal calibrated by our data, and correcting for wind-driven vertical mixing, we estimate a minimum of 5.25 trillion particles weighing 268,940 tons. When comparing between four size classes, two microplastic <4.75 mm and meso- and macroplastic >4.75 mm, a tremendous loss of microplastics is observed from the sea surface compared to expected rates of fragmentation, suggesting there are mechanisms at play that remove <4.75 mm plastic particles from the ocean surface.
- ItemOpen AccessPoor transferability of species distribution models for a pelagic predator, the grey petrel, Indicates contrasting habitat preferences across ocean basins(Public Library of Science, 2015) Torres, Leigh G; Sutton, Philip J H; Thompson, David R; Delord, Karine; Weimerskirch, Henri; Sagar, Paul M; Sommer, Erica; Dilley, Ben J; Ryan, Peter G; Phillips, Richard ASpecies distribution models (SDMs) are increasingly applied in conservation management to predict suitable habitat for poorly known populations. High predictive performance of SDMs is evident in validations performed within the model calibration area (interpolation), but few studies have assessed SDM transferability to novel areas (extrapolation), particularly across large spatial scales or pelagic ecosystems. We performed rigorous SDM validation tests on distribution data from three populations of a long-ranging marine predator, the grey petrel Procellaria cinerea , to assess model transferability across the Southern Hemisphere (25-65°S). Oceanographic data were combined with tracks of grey petrels from two remote sub-Antarctic islands (Antipodes and Kerguelen) using boosted regression trees to generate three SDMs: one for each island population, and a combined model. The predictive performance of these models was assessed using withheld tracking data from within the model calibration areas (interpolation), and from a third population, Marion Island (extrapolation). Predictive performance was assessed using k-fold cross validation and point biserial correlation. The two population-specific SDMs included the same predictor variables and suggested birds responded to the same broad-scale oceanographic influences. However, all model validation tests, including of the combined model, determined strong interpolation but weak extrapolation capabilities. These results indicate that habitat use reflects both its availability and bird preferences, such that the realized distribution patterns differ for each population. The spatial predictions by the three SDMs were compared with tracking data and fishing effort to demonstrate the conservation pitfalls of extrapolating SDMs outside calibration regions. This exercise revealed that SDM predictions would have led to an underestimate of overlap with fishing effort and potentially misinformed bycatch mitigation efforts. Although SDMs can elucidate potential distribution patterns relative to large-scale climatic and oceanographic conditions, knowledge of local habitat availability and preferences is necessary to understand and successfully predict region-specific realized distribution patterns.
- ItemOpen AccessPrior incoherence within a Bayesian assessment of the Southern Hemisphere humpback whale breeding stock B population(2010) Müller, Andrea; Butterworth, Doug SIn very simple terms, a Bayesian analysis involves drawing estimatable parameter values from some prior distribution, computing population dynamics and assigning a likelihood value to each combination based on comparisons to data containing information on population size and/or trend. A posterior distribution may then be constructed and conclusions drawn about the parameter estimates. In Model Ia (see Appendix) r B1 , r B2 , ( ) 1 arg ~ ln B Nt , ( ) 2 arg ~ ln B Nt are the parameter values drawn from priors for the intrinsic growth rate and the log of the recent abundance for the two populations under consideration.
- ItemOpen AccessReport on discussions on modelling studies of possible interchange between the C1 and C3 breeding Substocks of Southern Hemisphere humpback whales, Cape Town(2009) Butterworth, Doug S; Johnston, Susan JOver the week of 8-12 December, 2008, an International Stock Assessment Workshop was held under MARAM’s auspices at the University of Cape Town. This Workshop reviewed and discussed further lines of research for assessment analyses of five Southern African marine populations. One of these was Breeding Stock C of the Southern Hemisphere humpback whales, with a focus on modelling of possible interchange between breeding sub-stocks C1 and C3. The specific intent in the humpback case was to identify work usefully carried out prior to the IWC Intersessional Meeting (on Assessment Methodology to take account of Mixing/Interchange between Southern Hemisphere Humpback Populations) scheduled for Seattle in February 2009, so as to facilitate progress at that meeting.
- ItemOpen AccessSimulation exercise to ascertain the relative utility of collection of various data types for informing future Southern Hemisphere humpback whale assessments and Breeding Stocks D, E1 and the Oceania stocks(2015) Ross-Gillespie, Andrea; Butterworth, Doug SThe existing three-stock model for Breeding Stock D (West Australia or BSD), Breeding Stock E1 (East Australia or BSE1) and the collection of Oceania breeding sub-stocks (referred to as Breeding Stock O or BSO for convenience) is used to simulate future data which might be collected for these stocks, to ascertain which have the best potential to improve estimates of precision of key quantities associated with the population dynamics. For BSD, a future estimate of absolute abundance in 2017 and a series of relative abundance estimates each year from 2016 to 2020, where all estimates have CVs of 0.25, are considered. For BSE1, a similar further relative abundance series is considered, as well as new mark-recapture data collected over 2016 to 2020 where sighting probabilities are taken to be half the average achieved previously. Sighting probabilities are dealt with in a similar manner for future mark-recapture data considered for BSO. Both the further absolute abundance estimate for BSD and especially more mark-recapture data for BSO show some potential for providing improved precision in parameter estimates. However, there seems to be little increase in precision to be gained from further relative abundance data, bearing in mind that these results presume the current three-stock model to be correct. The three-stock model is further used to estimate the range of future observations that would remain compatible with that model’s assumptions. Actual data collected in future could be compared to such ranges to check on the model’s ability to continue to reflect reality.
- ItemOpen AccessSummary of the data and information available for the assessment of breeding stock B of the Southern Hemisphere humpback whales(2008) Johnston, Susan JFair amount of evidence to suggest two sub-stocks B1 (Gabon) and B2 (west South Africa, Namibia, Angola) • Would make sense to thus try and assess B1 and B2 separately.
- ItemOpen AccessSummary of the data and information available for the assessment of breeding stock C of the Southern Hemisphere humpback whales(2008) Johnston, Susan J• Fair amount of evidence to suggest three possible substocks C1 (Mozambique, and South African East Coast migration corridor), C2 (Mayote, Comores and other Islands of the Mozambique Channel) and C3 (Madagascan waters including Antongil Bay) • SC/A06/HW38 – suggests separation of C1 from the other, and lumping C2 and C3 • C2 considered small