Machine learning highlights the importance of primary and secondary production in determining habitat for marine fish and macroinvertebrates

1. Species distribution models for marine organisms are increasingly used for a range of applications, including spatial planning, conservation, and fisheries management. These models have been constructed using a variety of mathematical forms and drawing on both physical and biological independent variables; however, what might be called first-generation models have mainly followed the form of linear models, or smoothing splines, informed by data collected in the context of fish surveys.
2. The performance of different classes of variables were tested in a series of species occurrence models built with machine learning methods, specifically evaluating the potential contribution of lower trophic level data. Random forest models were fitted based on the classification of the absence/presence for fish and macroinvertebrates surveyed on the US Northeast Continental Shelf.
3. The potential variables included physical, primary production, secondary production, and terrain variables. For accepted model fits, six variable importance measures were computed, which collectively showed that physical and secondary production variables make the greatest contribution across all models. In contrast, terrain variables made the least contribution to these models.
4. Multivariable analyses that account for all performance measures reinforce the role of water depth and temperature in defining species presence and absence; however, chlorophyll concentration and some specific zooplankton taxa, such as Metridia lucens and Paracalanus parvus, also make important contributions with strong seasonal variations.
5. Our results suggest that lower trophic level variables, if available, are valuable in the creation of species distribution models for marine organisms.

     

Assessing common bottlenose dolphin (Tursiops truncatus) population structure in Mississippi Sound and coastal waters of the north central Gulf of Mexico

1. Population structure of highly mobile marine organisms can be complex and difficult to study, but it is important to understand how populations partition themselves within their environment for accurate assessment of both natural and anthropogenic impacts and successful management. The 2010 Deepwater Horizon oil spill negatively impacted common bottlenose dolphins (Tursiops truncatus) within Mississippi Sound and the surrounding north central Gulf of Mexico (GOMx); however, little was known about their underlying population structure in these waters. Thus, it was unclear how many demographically independent populations were affected by the spill.
2. Common bottlenose dolphin samples were collected throughout inshore waters of Mississippi Sound and coastal waters of the north-central GOMx. Mitochondrial DNA control region sequence data and 19 nuclear microsatellite loci were analysed to determine how many populations are present and characterize their range throughout these waters.
3. Bayesian clustering and migration analyses identified two genetically distinct and demographically independent populations: one predominantly inhabiting Mississippi Sound and adjacent coastal waters, and a second population extending generally from offshore of Mobile Bay, Alabama, east along the Florida Panhandle. Neither of these populations align with the currently delineated management stocks previously used to estimate impacts from the oil spill on common bottlenose dolphins in this portion of the GOMx.
4. These results suggest that revisions may be necessary so that management stocks accurately represent the demographically independent populations present in these waters. Furthermore, better comprehension of underlying population structure will enhance impact assessments on common bottlenose dolphins and provide more appropriate baseline data to support future restoration and conservation objectives.

     

Big impacts from small abstractions: The effects of surface water abstraction on freshwater fish assemblages

1. Abstractions and diversions are prevalent in river networks worldwide; however, specific mechanisms and measures reflecting changes in functional characteristics of aquatic assemblages in response to flow abstraction have not been well established. In particular, the influence of small takes on fish assemblages is poorly understood.
2. Field surveys and stable‐isotope analyses were used to evaluate the impact of differing levels of flow abstraction on fish assemblage structure, and native–non‐native patterns of coexistence, associated with small surface water abstractions in four streams in New Zealand. Study design accounted for longitudinal processes (spatial autocorrelation) to isolate the effects of abstractions on fish assemblages.
3. Reaches with reduced flows downstream of abstraction points had significantly lower fish abundances per metre of stream length, probably because of decreased habitat size, altered interspecific interactions and barriers to movement. The loss of larger fish in reaches with high abstraction resulted in shallower mass–abundance slopes and shorter stable isotope‐derived food‐chain lengths, likely to have been caused by fewer trophic links in the food web. The large fish absent from these reaches were flow‐sensitive introduced salmonids, resulting in higher relative abundances of small‐bodied native fish, probably as a result of predatory and competitive release.
4. Quantification of metrics designed to characterize ecosystem functioning as well as abundance and species composition indicated that small water abstractions can alter both the structure and composition of stream fish assemblages and modify the outcomes of native–non‐native species interactions. Thus, a better understanding of the effects of small abstractions could be used to improve the strategic management of fish in invaded riverscapes.

     

Cryptic genetic diversity in “widespread” Southeast Asian bird species suggests that Philippine avian endemism is gravely underestimated

Mistakenly classifying morphologically cryptic endemic species as populations of widespread species potentially interferes with the conservation of biodiversity because undetected endemics that are imperilled may lack appropriate protection. It also impedes the reconstruction of the evolutionary history of a taxon by obscuring the number and distributional limits of species. Here, we present genetic and phylogenetic evidence corroborated by morphology that Philippine populations of seven widespread, non-migratory passerine birds might represent unrecognized, distinct species. An extrapolation based on this finding suggests that the proportion of endemic bird species in the Philippines could be much higher than currently estimated. This high degree of cryptic diversity in a well-studied, volant taxon implies that large numbers of unrecognized species can be expected in less thoroughly studied groups. We predict that genetic investigations of insular populations of widespread species will frequently reveal unrecognized island endemics, and because of the vulnerability of island habitats and their biota, these taxa may be particularly susceptible to extinction.     

Arthropods as surrogates of diversity at different spatial scales

This study evaluates the effectiveness of taxonomic, colonization and trophic groups of arthropods from native forests of the Azores archipelago as surrogates of the diversity of other arthropod groups and of the remaining arthropods. Consistency in the performance of surrogates was tested across three spatial scales and using two measures of diversity. Pitfall and beating samples from 109 transects, 18 forest fragments and seven islands were analysed. The results showed that Araneae, Hemiptera and small orders taxonomic groups; native, endemic and introduced colonization groups; and the herbivores trophic group were consistent surrogates of the remaining diversity across the three spatial scales analysed, for both alpha and dissimilarity diversities. However, none of the subsets considered was significantly related with all of the other subsets at any of the three spatial scales. The effectiveness of surrogacy was dependent on the spatial level considered, and groups behaved inconsistently depending on the measure of diversity used. The value of a group as a diversity surrogate should be evaluated for a study area for a given spatial scale and diversity measure, in accordance with the scale and measure that will be used for biodiversity assessments and monitoring programs in that area.     

Extirpation, colonization, and habitat dynamics of a keystone species along an urban gradient

Using 5 years of patch occupancy data for 384 habitat fragments, we evaluated population and habitat dynamics of the black-tailed prairie dog in urban habitat remnants in the rapidly developing landscape of Denver, CO, USA. Specifically, we evaluated the landscape factors, including fragment area, age, and connectivity, that characterize the habitat fragments most likely to be colonized by prairie dogs, as well as those experiencing local extinctions. In addition, we determined which patch types were most often removed by land development. Sites in proximity to colonies were more likely to be colonized by prairie dogs. Local extinctions were most common on isolated colonies, and older and more isolated colonies were more likely to be extirpated by human activity. In general, smaller and older habitat patches were at the highest risk of being lost to land development. Our results provide observations of dynamic changes to the distribution of a potential keystone species in an urban area, which can be used to inform island biogeographic and metapopulation models for wildlife persistence in developing landscapes. Although populations are currently in decline, most local extinctions are the direct result of human activity, and we suggest that prairie dogs in this area can persist with appropriate management.     

Chemical profiling with modeling differentiates wild and farm-raised salmon

Classifications of fish production methods, wild or farm-raised salmon, by elemental profiles or C and N stable isotope ratios combined with various modeling approaches were determined. Elemental analysis (As, Ba, Be, Ca, Co, Cd, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, P, Pb, Sr, Ti, and Zn) of wild and farm-raised salmon samples was performed using an inductively coupled plasma atomic emission spectroscopy. Isotopic and compositional analyses of carbon and nitrogen were performed using mass spectrometry as an alternative fingerprinting technique. Each salmon (king salmon, Oncorhynchus tshawytscha ; coho salmon, Oncorhynchus kisutch ; Atlantic salmon, Salmo salar ) was analyzed from two food production practices, wild and farm raised. Principal component analysis (PCA) and canonical discriminant analysis (CDA) were used for data exploration and visualization. Five classification modeling approaches were investigated: linear discriminate function, quadratic discriminant function, neural network, probabilistic neural network, and neural network bagging. Methods for evaluating model reliability included four strategies: resubstitution, cross-validation, and two very different test set scenarios. Generally speaking, the models performed well, with the percentage of samples classified correctly depending on the particular choice of model and evaluation method used.