National Marine Fisheries Service

About: National Marine Fisheries Service is a government organization based out in Silver Spring, Maryland, United States. It is known for research contribution in the topics: Population & Fisheries management. The organization has 3949 authors who have published 7053 publications receiving 305073 citations. The organization is also known as: NOAA Fisheries & NOAA National Marine Fisheries Service.

Projecting Marine Mammal Distribution in a Changing Climate

Abstract: Climate-related shifts in marine mammal range and distribution have been observed in some populations; however, the nature and magnitude of future responses are uncertain in novel environments projected under climate change. This poses a challenge for agencies charged with management and conservation of these species. Specialized diets, restricted ranges, or reliance on specific substrates or sites (e.g., for pupping) make many marine mammal populations particularly vulnerable to climate change. High-latitude, predominantly ice-obligate, species have experienced some of the largest changes in habitat and distribution and these are expected to continue. Efforts to predict and project marine mammal distributions to date have emphasized data-driven statistical habitat models. These have proven successful for short time-scale (e.g., seasonal) management activities, but confidence that such relationships will hold for multi-decade projections and novel environments is limited. Recent advances in mechanistic modeling of marine mammals (i.e., models that rely on robust physiological and ecological principles expected to hold under climate change) may address this limitation. The success of such approaches rests on continued advances in marine mammal ecology, behavior, and physiology together with improved regional climate projections. The broad scope of this challenge suggests initial priorities be placed on vulnerable species or populations (those already experiencing declines or projected to undergo ecological shifts resulting from climate changes that are consistent across climate projections) and species or populations for which ample data already exist (with the hope that these may inform climate change sensitivities in less well observed species or populations elsewhere). The sustained monitoring networks, novel observations, and modeling advances required to more confidently project marine mammal distributions in a changing climate will ultimately benefit management decisions across time-scales, further promoting the resilience of marine mammal populations.

The importance of spatial models for estimating the strength of density dependence

Abstract: Identifying the existence and magnitude of density dependence is one of the oldest concerns in ecology Ecologists have aimed to estimate density dependence in population and community data by fitting a simple autoregressive (Gompertz) model for density dependence to time series of abundance for an entire population However, it is increasingly recognized that spatial heterogeneity in population densities has implications for population and community dynamics We therefore adapt the Gompertz model to approximate, local densities over continuous space instead of population-wide abundance, and allow productivity to vary spatially using Gaussian random fields We then show that the conventional (nonspatial) Gompertz model can result in biased estimates of density dependence (eg, identifying oscillatory dynamics when not present) if densities vary spatially By contrast, the spatial Gompertz model provides accurate and precise estimates of density dependence for a variety of simulation scenarios and data availabilities These results are corroborated when comparing spatial and nonspatial models for data from 10 years and -100 sampling stations for three long-lived rockfishes (Sebastes spp) off the California, USA coast In this case, the nonspatial model estimates implausible oscillatory dynamics on an annual time scale, while the spatial model estimates strong autocorrelation and is supported by model selection tools We conclude by discussing the importance of improved data archiving techniques, so that spatial models can be used to reexamine classic questions regarding the existence and magnitude of density dependence in wild populations

Mitogenomic phylogenetic analyses of the Delphinidae with an emphasis on the Globicephalinae.

Abstract: Previous DNA-based phylogenetic studies of the Delphinidae family suggest it has undergone rapid diversification, as characterised by unresolved and poorly supported taxonomic relationships (polytomies) for some of the species within this group. Using an increased amount of sequence data we test between alternative hypotheses of soft polytomies caused by rapid speciation, slow evolutionary rate and/or insufficient sequence data, and hard polytomies caused by simultaneous speciation within this family. Combining the mitogenome sequences of five new and 12 previously published species within the Delphinidae, we used Bayesian and maximum-likelihood methods to estimate the phylogeny from partitioned and unpartitioned mitogenome sequences. Further ad hoc tests were then conducted to estimate the support for alternative topologies. We found high support for all the relationships within our reconstructed phylogenies, and topologies were consistent between the Bayesian and maximum-likelihood trees inferred from partitioned and unpartitioned data. Resolved relationships included the placement of the killer whale (Orcinus orca) as sister taxon to the rest of the Globicephalinae subfamily, placement of the Risso's dolphin (Grampus griseus) within the Globicephalinae subfamily, removal of the white-beaked dolphin (Lagenorhynchus albirostris) from the Delphininae subfamily and the placement of the rough-toothed dolphin (Steno bredanensis) as sister taxon to the rest of the Delphininae subfamily rather than within the Globicephalinae subfamily. The additional testing of alternative topologies allowed us to reject all other putative relationships, with the exception that we were unable to reject the hypothesis that the relationship between L. albirostris and the Globicephalinae and Delphininae subfamilies was polytomic. Despite their rapid diversification, the increased sequence data yielded by mitogenomes enables the resolution of a strongly supported, bifurcating phylogeny, and a chronology of the divergences within the Delphinidae family. This highlights the benefits and potential application of large mitogenome datasets to resolve long-standing phylogenetic uncertainties.