Environmental niche modelling is an acclaimed method for estimating species’ present or future distributions. However, in marine environments the assembly of representative data from reliable and unbiased occurrences is challenging. Here, we aimed to model the environmental niche and distribution of marine, parasitic nematodes from the Pseudoterranova decipiens complex using the software Maxent. The distribution of these potentially zoonotic species is of interest, because they infect the muscle tissue of host species targeted by fisheries. To achieve the best possible model, we used two different approaches. The land distance (LD) model was based on abiotic data, whereas the definitive host distance (DHD) model included species-specific biotic data. To assess whether DHD is a suitable descriptor for Pseudoterranova spp., the niches of the parasites and their respective definitive hosts were analysed using ecospat. The performance of LD and DHD was compared based on the variables’ contribution to the model. The DHD-model clearly outperformed the LD-model. While the LD-model gave an estimate of the parasites’ niches, it only showed the potential distribution. The DHD-model produced an estimate of the species’ realised distribution and indicated that biotic variables can help to improve the modelling of data-poor, marine species.
Distributions of Species
Uncertainty regarding the movement and population exchange of Atlantic bluefin tuna (Thunnus thynnus) from the two primary spawning areas (Gulf of Mexico, Mediterranean Sea) is increasingly implicated as a major impediment for the conservation of this species. Here, two mixture methods were applied to natural chemical markers (δ18O and δ13C) in otoliths (ear stones) to comprehensively investigate the nature and degree of transoceanic movement and mixing of eastern and western populations in several areas of the North Atlantic Ocean that potentially represent mixing hotspots. Areas investigated occurred on both sides of the 45°W management boundary as well as waters off the coast of Africa (Morocco, Canary Islands) where both populations are known to occur. Projections of population composition (i.e., natal or nursery origin) from a multinomial logistic regression (MLR) classification method with different probability thresholds were generally in agreement with maximum likelihood estimates from the commonly used mixed-population program HISEA; however, predicted contributions for the less abundant population were occasionally higher for MLR estimates. Both MLR and HISEA clearly showed that mixing of Atlantic bluefin tuna in the Central North Atlantic Ocean was highly variable from year to year with expatriates of eastern or western origin commonly crossing into the other management area. Pronounced transoceanic movement and mixing of western migrants was also present off the coast of Africa, with the occurrence of western migrants in the Canary Islands and Morocco ranging from zero to the majority of the individuals assayed for the years examined. Results indicate highly variable rates of movement and population exchange for Atlantic bluefin tuna, highlighting the need for temporally resolved estimates of natal origin in mixing hotspots to improve population models used to evaluate the status of this threatened species.
Changes in the abundance and productivity of biological populations in the North Pacific have often been associated with large-scale modes of climate variability. The Pacific Decadal Oscillation (PDO), which describes spatio-temporal variability in North Pacific sea surface temperature (SST), correlates with much of this variability. However, since the late 1980s, the North Pacific Gyre Oscillation (NPGO) has explained an increasing proportion of variance in North Pacific climate properties. Ecological responses to this change in the proportion of variance ascribed to the two climate patterns remain poorly understood. Here, we test the hypothesis that relationships between biological time series and climate covariates (SST and the PDO) differ for nine Gulf of Alaska fish and crustacean populations before and after the late 1980s. Additionally, we evaluate whether non-stationary climate-biology relationships arose synchronously across populations as a community response. We used different formulations of Generalized Additive Models in a population and community context and compared results to the classical approach of aggregated population responses based on Principal Component Analysis (PCA). The results showed that climate-biology relationships weakened or reversed for most populations in the late 1980s, coinciding with the increase in variance of the NPGO. However, these non-stationary responses were highly species-specific and did not arise synchronously as a community response. We show that PCA does not represent community dynamics properly when only few species covary in time and exhibit long-term trends. Therefore, this approach might not be always useful to detect synchronous changes among biological time series as a community response. Novel associations among climate variables and novel climate-biology relationships are expected to become increasingly evident with future climate change, and the recognition of switches between different explanatory variable-response relationships may be critical for successful management of marine resources during transitions to these novel climate states.
Cold water coral and sponge communities (CWCS) are important indicators of vulnerable marine ecosystems (VMEs) and are used to delineate areas for marine conservation and fisheries management. Although the Northeast Pacific region of Canada (NEPC) is notable for having unique CWCS assemblages and is the location of >80% of Canadian seamounts, the extent of potential CWCS-defined VMEs in this region is unknown. Here, we used a diverse set of environmental data layers (n=30) representing a range of bathymetric derivatives, physicochemical variables, and water column properties to assess the primary factors influencing the niche separation and potential distributions of six habitat-forming groups of CWCS in the NEPC (sponge classes: Hexactinellida, Demospongiae; coral orders: Alcyonacea, Scleractinia, Antipatharia, Pennatulacea). The primary environmental gradients that influence niche separation among CWCS are driven by total alkalinity, dissolved inorganic carbon, and dissolved oxygen. Significant niche separation among groups indicates CWCS to be primarily specialists occurring in rare habitat conditions in the NEPC. Species distribution models (SDMs) developed for each CWCS group shared severely low dissolved oxygen levels ([O2] < 0.5 ml L−1) as a top predictor for habitat suitability in the NEPC. Niche separation is further emphasized by differences in the model-predicted areas of suitable habitat among CWCS groups. Although niches varied among taxa, the general areas of high habitat suitability for multiple CWCS groups in the NEPC occurred within the 500–1400 m bottom depth range which is strongly associated with the extensive oxygen minimum zone (OMZ) characterizing this region. As a result, the largest continuous area of potential CWCS habitat occurred along the continental slope with smaller, isolated patches also occurring at several offshore seamounts that have summits that extend into OMZ depths. Our results provide insight into the factors that influence the distributions of some of the most important habitat-forming taxa in the deep ocean and create an empirical foundation for supporting cold-water coral and sponge conservation in the NEPC.
Establishing protected areas (PAs) ranks among the top priority actions to mitigate the global scale of modern biodiversity declines. However, the distribution of biodiversity is spatially asymmetric among regions and lineages, and the extent to which PAs offer effective protection for species and ecosystems remains uncertain. Penguins, regarded as prime bioindicator birds of the ecological health of their terrestrial and marine habitats, represent priority targets for such quantitative assessments. Of the world’s 18 penguin species, eleven are undergoing population declines, for which ten are classified as ‘Vulnerable’ or ‘Endangered’. Here, we employ a global-scale dataset to quantify the extent to which their terrestrial breeding areas are currently protected by PAs. Using quantitative methods for spatial ecology, we compare the global distribution of penguin colonies, including range and population size analyses, with the distribution of terrestrial PAs classified by the International Union for Conservation of Nature, and generate hotspot and endemism maps worldwide. Our assessment quantitatively reveals < 40% of the terrestrial range of eleven penguin species is currently protected, and that range size is the significant factor in determining PA protection. We also show that there are seven global hotspots of penguin biodiversity where four or five penguin species breed. We suggest that future penguin conservation initiatives should be implemented based on more comprehensive, quantitative assessments of the multi-dimensional interactions between areas and species to further the effectiveness of PA networks.
The spatial prediction of species distributions from survey data is a significant component of spatial planning and the ecosystem-based management approach to marine resources. Statistical analysis of species occurrences and their relationships with associated environmental factors is used to predict how likely a species is to occur in unsampled locations as well as future conditions. However, it is known that environmental factors alone may not be sufficient to account for species distribution. Other ecological processes including species interactions (such as competition and predation), and the impact of human activities, may affect the spatial arrangement of a species. Novel techniques have been developed to take a more holistic approach to estimating species distributions, such as Bayesian Hierarchical Species Distribution model (B-HSD model) and mechanistic food-web models using the new Ecospace Habitat Foraging Capacity model (E-HFC model). Here we used both species distribution and spatial food-web models to predict the distribution of European hake (Merluccius merluccius), anglerfishes (Lophius piscatoriusand L. budegassa) and red mullets (Mullus barbatus and M. surmuletus) in an exploited marine ecosystem of the Northwestern Mediterranean Sea. We explored the complementarity of both approaches, comparing results of food-web models previously informed with species distribution modelling results, aside from their applicability as independent techniques. The study shows that both modelling results are positively and significantly correlated with observational data. Predicted spatial patterns of biomasses show positive and significant correlations between modelling approaches and are more similar when using both methodologies in a complementary way: when using the E-HFC model previously informed with the environmental envelopes obtained from the B-HSD model outputs, or directly using niche calculations from B-HSD models to drive the niche priors of E-HFC. We discuss advantages, limitations and future developments of both modelling techniques.
There are evidences of how climate change is affecting seaweeds distribution and the ecosystems services they provide. Therefore, it is necessary to consider these impacts when managing marine areas. One of the most applied tools in recent years to deal with this are species distribution models, however there are still some challenges to solve, such as the inclusion of hydrodynamic predictors and the application of effective, transferable and user-oriented methodologies.
Five species (Saccorhiza polyschides, Gelidium spinosum, Sargassum muticum, Pelvetia canaliculata and Cystoseira baccata) in Europe and 15 variables were considered. Nine of them were projected to the RCPs 4.5 and 8.5 for the mid-term (2040–2069) and the long term (2070–2099). Algorithms for each species were applied to generate models that were assessed by comparison of probabilities and observations (area under the curve, true skill statistics, Boyce index, sensitivity, correct classification rate), niches overlap (Schoener's D, Hellinger's I), geographical similarity (interquartile range) and ecological realism.
Models built demonstrated very good predictive accuracy and sensitivity, without overfitting risk. A medium overlap in the historical and RCPs environmental conditions were obtained, therefore the models can be considered transferable and results accurate because only some isolated points were detected as outliers, corresponding to low probabilities.
The areas of S. polyschides and G. spinosum have been identified to be dramatically reduced, meanwhile S. muticum and C. baccata were predicted to expand their range. P. canaliculata was expected to keep its sites of presence but with a decrease in its probability of occurrence. For all species it was remarkable the importance of hydrodynamic variables and parameters representing extreme conditions. Spatially predictions of the potential species and areas at risk are decisive for defining management strategies and resource allocation. The performance and usefulness of the approach applied in this study have been demonstrated for algae with different ecological requirements (from upper littoral to subtidal) and distributional patterns (native and invasive), therefore results can be used by marine planners with different goals: marine protected areas designation, monitoring efforts guiding, invasions risk assessment or aquaculture facilities zonation.
The structure of the phytoplankton community in surface waters is the consequence of complex interactions between the physical and chemical properties of the upper water column as well as the interaction within the general biological community. Understanding the structure of phytoplankton communities is especially challenging in highly variable and dynamic marine environments. A variety of strategies have been employed to delineate marine planktonic habitats, including both biogeochemical and water-mass-based approaches. These methods have led to fundamental improvements in our understanding of marine phytoplankton distributions, but they are often difficult to apply to systems with physical and chemical properties and forcings that vary greatly over relatively short spatial or temporal scales. In this study, we have developed a method of dynamic habitat delineation based on environmental variables that are biologically relevant, that integrate over varying time scales, and that are derived from standard oceanographic measurements. As a result, this approach is widely applicable, simple to implement, and effective in resolving the spatial distribution of phytoplankton communities. As a test of our approach, we have applied it to the Amazon River-influenced Western Tropical North Atlantic (WTNA) and to the South China Sea (SCS), which is influenced by both the Mekong River and seasonal coastal upwelling. These two systems differ substantially in their spatial and temporal scales, nutrient sources/sinks, and hydrographic complexity, providing an effective test of the applicability of our analysis. Despite their significant differences in scale and character, our approach generated statistically robust habitat classifications that were clearly relevant to surface phytoplankton communities. Additional analysis of the habitat-defining variables themselves can provide insight into the processes acting to shape phytoplankton communities in each habitat. Finally, by demonstrating the biological relevance of the generated habitats, we gain insights into the conditions promoting the growth of distinct communities and the factors that lead to mismatches between environmental conditions and phytoplankton community structure.
The Bluntnose Sixgill Shark, Hexanchus griseus, is a large predatory shark, has a worldwide distribution and is listed as near-threatened by the International Union of Conservation of Nature (IUCN). The Seattle Aquarium collected observations of free-swimming Sixgill Sharks in Elliott Bay, Washington, under the aquarium’s pier in 20 m of water from 2003 to 2005 and again from 2008 to 2015 using the same methodology. Compared to total Sixgill sightings between 2003 and 2005 (273) fewer total Sixgills were sighted at the aquarium’s research station between 2008 and 2015 (33). The reason for the observed decline in sightings in unknown but based on data from other studies on Sixgills in Puget Sound during the same timeperiod the authors hypothesize the decrease may be due to natural variability of juvenile Sixgill recruitment to Elliott Bay.
Sea turtle populations are often assessed at the regional to sub-basin scale from discrete indices of nesting abundance. While this may be practical and sometimes effective, widespread in-water surveys may enhance assessments by including additional demographics, locations, and revealing emerging population trends. Here, we describe sea turtle observations from 13 years of towed-diver surveys across 53 coral islands, atolls, and reefs in the Central, West, and South Pacific. These surveys covered more than 7,300 linear km, and observed more than 3,400 green (Chelonia mydas) and hawksbill (Eretmochelys imbricata) sea turtles. From these data, we estimated sea turtle densities, described trends across space and time, and modelled the influence of environmental and anthropogenic drivers. Both species were patchily distributed across spatial scales, and green turtles were 11 times more abundant than hawksbills. The Pacific Remote Island Areas had the highest densities of greens (3.62 turtles km-1, Jarvis Island), while American Samoa had the most hawksbills (0.12 turtles km-1, Ta’u Island). The Hawaiian Islands had the lowest turtle densities (island ave = 0.07 turtles km-1) yet the highest annual population growth (μ = 0.08, σ = 0.22), suggesting extensive management protections can yield positive conservation results. Densities peaked at 27.5°C SST, in areas of high productivity and low human impact, and were consistent with patterns of historic overexploitation. Though such intensive surveys have great value, they are logistically demanding and therefore have an uncertain budget and programmatic future. We hope the methods we described here may be applied to future comparatively low-cost surveys either with autonomous vehicles or with environmental DNA.