Search

Primary production dynamics are strongly associated with vertical density profiles, which dictate the depth of 10 stratification and mixed layers. Climate change and artificial structures (e.g. windfarms) are likely to modify the strength of stratification and vertical distribution of nutrient fluxes, especially in shelf seas where fine scale processes are important drivers, affecting the vertical distribution of phytoplankton. To understand the effect of physical changes on primary production, identifying the linkage between density and phytoplankton profiles is essential. Here, the ecological relevance of eight density layers (DLs) obtained by multiple methods that define three different portions of the pycnocline (above, 15 centre, below) was evaluated to identify a valuable proxy for subsurface Chlorophyll-a (Chl-a mg m-3) concentrations. The associations of subsurface Chl-a with surface and deep mixing were investigated by hypothesizing the occurrence at the same depth of any DL and the maximum Chl-a layer (DMC) using Spearman correlation, linear regression, and a Major Axis analysis. Out of 1237 observations of the water column exhibiting a pycnocline, 78% reported DMCs above the bottom mixed layer depth (BMLD). This suggests that the BMLD is a boundary trapping Chl-a in shallow waters (≤ 20 120 m). BMLD constantly described Chl-a vertical distribution despite surface mixing indicators, suggesting a significant contribution of deep mixing processes in supporting subsurface production under specific conditions (e.g. prolonged stratification, tidal cycle, and bathymetry). Using BMLD for defining subsurface Chl-a could be a valuable tool for understanding the spatiotemporal variability of Chl-a in shelf seas, representing a potential variable for ecological assessments. [ABSTRACT FROM AUTHOR]

Identifying and quantifying the effects of climate change that alter the habitat overlap of marine predators and their prey population distributions is of great importance for the sustainable management of populations. This study uses Bayesian joint models with integrated nested Laplace approximation (INLA) to predict future spatial density distributions in the form of common spatial trends of predator–prey overlap in 2050 under the "business‐as‐usual, worst‐case" climate change scenario. This was done for combinations of six mobile marine predator species (gray seal, harbor seal, harbor porpoise, common guillemot, black‐legged kittiwake, and northern gannet) and two of their common prey species (herring and sandeels). A range of five explanatory variables that cover both physical and biological aspects of critical marine habitat were used as follows: bottom temperature, stratification, depth‐averaged speed, net primary production, and maximum subsurface chlorophyll. Four different methods were explored to quantify relative ecological cost/benefits of climate change to the common spatial trends of predator–prey density distributions. All but one future joint model showed significant decreases in overall spatial percentage change. The most dramatic loss in predator–prey population overlap was shown by harbor seals with large declines in the common spatial trend for both prey species. On the positive side, both gannets and guillemots are projected to have localized regions with increased overlap with sandeels. Most joint predator–prey models showed large changes in centroid location, however the direction of change in centroids was not simply northwards, but mostly ranged from northwest to northeast. This approach can be very useful in informing the design of spatial management policies under climate change by using the potential differences in ecological costs to weigh up the trade‐offs in decisions involving issues of large‐scale spatial use of our oceans, such as marine protected areas, commercial fishing, and large‐scale marine renewable developments. [ABSTRACT FROM AUTHOR]

Abstract Phytoplankton are an extremely important component of the functioning of ecosystems and climate regulation. Because concentrations of phytoplankton are highly patchy in both space and time, it is proposed that more consideration concerning the potential impact from human developments and activities on the service provision afforded by phytoplankton should be accounted for in marine management processes. The multiple species of primary producers provide important provisioning and regulating ecosystem services (ES) and form the basis of marine food-webs, supporting production of higher trophic levels (a provisioning ES), and act as a sink of CO 2 (a climate regulation ES). Spatial and temporal patchiness in the production of phytoplankton can be related to patchiness in the provision of these ES. Patches of naturally high phytoplankton productivity should be afforded consideration within processes to assess environmental status, within marine spatial planning (including marine protected areas) and within sectoral licensing, with marine planning and licensing acting at scales most in harmony with scales of phytoplankton heterogeneity (meters to tens of kilometres). In this study, consideration of phytoplankton in marine management decision making has been reviewed. This paper suggests that potential impacts of maritime developments and activities on the natural patchiness of phytoplankton communities be included in management deliberations, and mitigation be considered. This affords opportunities for researchers to engage with management authorities to support ecosystems-based management. Doing so will assist in maintaining or achieving good environmental status and support further, reliant, ES. Highlights • Phytoplankton support a wide range of ecosystem services (ES). • Spatial variability results in some locales supporting increased primary production. • This has disproportionate impacts on distributions of local supported ES. • Marine management should consider areas of naturally increased primary production. • Impacts on the ability of phytoplankton to support local ES should be considered. [ABSTRACT FROM AUTHOR]

Understanding spatial physical habitat selection driven by competition and/or predator-prey interactions of mobile marine species is a fundamental goal of spatial ecology. However, spatial counts or density data for highly mobile animals often (1) include excess zeros, (2) have spatial correlation, and (3) have highly nonlinear relationships with physical habitat variables, which results in the need for complex joint spatial models. In this paper, we test the use of Bayesian hierarchical hurdle and zero-inflated joint models with integrated nested Laplace approximation (INLA), to fit complex joint models to spatial patterns of eight mobile marine species (grey seal, harbor seal, harbor porpoise, common guillemot, black-legged kittiwake, northern gannet, herring, and sandeels). For each joint model, we specified nonlinear smoothed effect of physical habitat covariates and selected either competing species or predator-prey interactions. Out of a range of six ecologically important physical and biologic variables that are predicted to change with climate change and large-scale energy extraction, we identified the most important habitat variables for each species and present the relationships between these bio/physical variables and species distributions. In particular, we found that net primary production played a significant role in determining habitat preferences of all the selected mobile marine species. We have shown that the INLA method is well-suited for modeling spatially correlated data with excessive zeros and is an efficient approach to fit complex joint spatial models with nonlinear effects of covariates. Our approach has demonstrated its ability to define joint habitat selection for both competing and prey-predator species that can be relevant to numerous issues in the management and conservation of mobile marine species. [ABSTRACT FROM AUTHOR]

• Physical and biological variables identified as strong indicators of ecosystem change. • Contrasting regions provided useful insights on ecosystem responses to disturbances. • Interactions between indicators can dramatically change over time. • Ecosystem change linked to physical pressures and primary production changes. • Hidden variable modelled ecosystem changes. Understanding ecosystem dynamics within shallow shelf seas is of great importance to support marine spatial management of natural populations and activities such as fishing and offshore renewable energy production to combat climate change. Given the possibility of future changes, a baseline is needed to predict ecosystems responses to such changes. This study uses Bayesian techniques to find the data-driven estimates of interactions among a set of physical and biological variables and a human pressure within the last 30 years in a well-studied shallow sea (North Sea, UK) with four contrasting regions and their associated ecosystems. A hidden variable is incorporated to model functional ecosystem change, where the underlying interactions dramatically change, following natural or anthropogenic disturbance. Data-driven estimates of interactions were identified, highlighting physical (e.g. bottom temperature, potential energy anomaly) and biological variables (e.g. sandeel larvae, net primary production) to be strong indicators of ecosystem change. There was consistency in the physical and biological variables, identified as good indicators in three of the regions, however the shallower region (with depths < 50 m, that is targeted for static offshore wind developments) was the most dissimilar. The use of contrasting regions provided useful insights on responses linked to ecosystem disturbances and identified the top predators as better indicators for each region, with the harbour porpoise being a particularly valuable indicator of ecosystem change across most regions. Another important finding was the dramatic changes in the strength of many interactions over time. This suggests that physical and biological indicators should only be used with additional temporal information, as changes in strength led to the identification of two potentially significant periods of ecosystem change (after 2005 and after 2010), linked to physical pressures (e.g. cold-water anomalies, seen in bottom temperatures; salinity changes, seen in the potential energy anomaly) and primary production changes. The hidden variable also modelled a change in the early 2000s for all the regions and identified maximum chlorophyll-a and sea surface temperature as some of the better indicators of these ecosystem changes. [ABSTRACT FROM AUTHOR]