Evaluating eutrophication management scenarios in the Baltic Sea using species distribution modelling

Abstract

Summary1. Eutrophication is severely affecting species distributions and ecosystem functioning in coastal areas. Targets for eutrophication reduction have been set in the Baltic Sea Action Plan (BSAP) using Secchi depth, a measure of water transparency, as the main status indicator. Despite the high economic costs involved, the potential effects of this political decision on key species and habitats have not been assessed. 2. In a case study including species central to coastal ecosystem functioning, we modelled the effects of changing Secchi depth on the distribution of bladderwrack Fucus vesiculosus and eelgrass Zostera marina vegetation as well as recruitment areas of the main predatory fish species, perch Perca fluviatilis and pikeperch Sander lucioperca. Specifically, we explored the effects of changing Secchi depth on species distributions under a set of scenarios based on the BSAP, using three fundamentally different modelling techniques: maximum entropy, generalized additive and random forest modelling. 3. Improved Secchi depth (reduced eutrophication) was predicted to cause a substantial increase in the distribution of bladderwrack, while the distribution of eelgrass remained largely unaffected. For the fish, a large increase in perch recruitment areas was predicted and a concurrent decrease in recruitment areas of pikeperch. These changes are likely to have effects on biodiversity and ecosystem services. 4. The three modelling methods exposed differences in the quantitative predictions for species with a weaker coupling to Secchi depth. Qualitatively, however, the results were consistent for all species. 5. Synthesis and applications. We show how ecological effects of environmental policies can be evaluated in an explicit spatial context using species distribution modelling. The modelspecific responses to changes in eutrophication status emphasize the importance of using ensemble modelling for exploring how species distributions may respond to alternative management regimes. A pronounced difference in response between species suggests that eutrophication mitigation will have consequences for ecosystem functioning, and thus ecosystem goods and services, by inducing changes in the simple food webs of the Baltic Sea. These model predictions form a basis for spatially explicit cost-benefit estimates under different scenarios, providing valuable information for both decision-makers and the wider society.

Biodiversity and ecosystem services in Nordic coastal ecosystems

“…Fish are strongly affected by eutrophication, in a number of, at time contradictory, ways. Perch are disadvantaged by eutrophication, whereas, for example, pike perch benefit (Bergström et al, 2013).…”

“…For perch, pikeperch and northern pike, lacking access to spawning and nursery areas has large impacts on adult stocks (Bergström et al, 2013). Shallow and sheltered bays are important for aquatic plants, which provide both food and shelter for fish and other organisms.…”

“…However, large-scale drainage for agriculture and forestry has had negative impacts. Restoration of wetlands can be a good fish conservation measure (Bergström et al, 2013).…”

Untitled

“…GAMs have been widely used in ecological research such as forest biota and vegetation distribution modeling (Austin, 2002;Drexler & Ainsworth, 2013;Moisen et al, 2006;Yee & Mackenzie, 2002), marine fisheries (Bergstrom, Sundblad, Downie, Snickars, & Lindegarth, 2013;Denis, Lejeune, & Robin, 2002;Elith et al, 2006;França & Cabral, 2015), and freshwater fisheries (Alexander, 2016;Jowett, Parkyn, & Richardson, 2008;Leathwick, Elith, & Hastie, 2006). The results of ecological models such as GAMs can be crucial for decision making for environmental management.…”

Shoreline development and degradation of coastal fish reproduction habitats

“…Recent decades have, however, seen a rapid development of species distribution modeling methods, allowing researchers and managers to produce predictive maps of the underwater environment and its associated biota (Elith and Leathwick 2009). In the Baltic Sea, several recent research programs have significantly benefited our understanding and knowledge of habitat distributions in general (Al-Hamdani and Reker 2007;Bučas et al 2013;Lindegarth et al 2014) and coastal fish habitats in particular (Härmä et al 2008;Kallasvuo et al 2009;Sundblad et al 2009Sundblad et al , 2011Sundblad et al , 2013Snickars et al 2010;Bergström et al 2013). For instance, using statistical non-linear relationships between life-stage specific occurrence and environmental descriptors, Sundblad et al (2011) used predictive distribution models to map key reproduction habitats of three of the most common species in the Baltic Sea coastal fish community, northern pike (Esox lucius), Eurasian perch (Perca fluviatilis) and roach (Rutilus rutilus).…”