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Spatial Modelling of Coastal Fish – Methods and Applications
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Genetics, Limnology.
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Environmental factors influence species and habitats on multiple scales creating a mosaic of distribution patterns. Studying factors shaping these patterns are central to our understanding of population dynamics and ultimately ecosystem functioning. Information on the distribution of resources and conservation values are also highly needed in marine management as coastal areas are increasingly influenced by human activities.

In this thesis, large-scale field data is used to explore how strong environmental gradients found on multiple scales in the coastal areas of the Baltic Sea influence fish habitats. The underlying concepts are based in the field of species distribution modelling, whereby habitat maps can be produced using environmental layers in a geographic information system. Distribution modelling is further used to address both ecological and applied questions by examining effects of habitat limitation on fish population sizes and to evaluate management actions aimed at habitat conservation.

I show that specific habitat requirements for fish species of both freshwater and marine origin can be described using environmental variables and that species-environment relationships can be used to predict the distribution of early life-stages of fish in the Baltic Sea archipelagos. Further, predicted habitat availability of a specific life-stage was directly related to adult population size of Eurasian perch Perca fluviatilis, signifying that the abundance of large predatory fish can be limited by specific recruitment habitats. Lastly, by predicting the distribution of an assemblage of coastal fish species and their associated habitats, an assessment of a network of marine protected areas was performed. Results revealed large gaps in the current network and identified areas suitable for future protection. By demonstrating how current habitat protection can be improved by including critical habitats for coastal fish population sizes this thesis points to the benefits of integrating nature conservation and fisheries management.

Based on these findings I conclude that species distribution modelling provides a suitable analytical framework for assessing the habitat requirements of organisms. An increased understanding of habitat-population relationships and an ability to accurately map ecologically important features will be of great value for an ecosystem-based marine management. ­

 

 

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis , 2010. , p. 43
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 779
Keywords [en]
habitat, niche, species distribution modelling, juvenile, fish, larvae, spawning
National Category
Ecology Natural Sciences Ecology Biological Sciences
Research subject
Biology with specialization in Limnology; Biology with specialization in Animal Ecology; Limnology
Identifiers
URN: urn:nbn:se:uu:diva-132620ISBN: 978-91-554-7928-2 (print)OAI: oai:DiVA.org:uu-132620DiVA, id: diva2:358833
Public defence
2010-12-10, Friessalen, Evolutionary Biology Centre (EBC), Uppsala University, Norbyvägen 18, 752 36 Uppsala, Uppsala, 10:00 (English)
Opponent
Supervisors
Note
Felaktigt tryckt som Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 709Available from: 2010-11-19 Created: 2010-10-22 Last updated: 2011-03-21Bibliographically approved
List of papers
1. Characterisation of juvenile flatfish habitats in the Baltic Sea
Open this publication in new window or tab >>Characterisation of juvenile flatfish habitats in the Baltic Sea
2009 (English)In: Estuarine, Coastal and Shelf Science, ISSN 0272-7714, E-ISSN 1096-0015, Vol. 82, no 2, p. 294-300Article in journal (Refereed) Published
Abstract [en]

Survival and growth of the earliest life-stages is considered a key factor in determining the abundance of many marine fish species. For flatfishes, the availability of high quality nursery areas is essential for successful recruitment. Regarding the Baltic Sea, there are large gaps in knowledge on factors that influence the distribution of flatfishes during this sensitive stage. To identify the characteristics of important nursery areas in the Baltic for flounder (Platichthys flesus) and turbot (Psetta maxima), a field survey with push net sampling was conducted in the northern Baltic proper during autumn 2006. The sampling stations were stratified to cover several different habitat types defined by substrate and wave exposure. Apart from density of young-of-the-year (YOY) flatfishes, a number of ecological characteristics of the habitat were recorded. Physical habitat variables included substrate type, salinity, depth, turbidity, vegetation and habitat structure. Variables describing biotic processes, such as prey availability and abundance of competitors, were also sampled. The relationships between the spatial distribution of species and these ecological characteristics were fitted to presence/absence data of juvenile flatfish using generalized additive models (GAM). The best habitat descriptors for flounder in order of contribution were: substrate, habitat structure, salinity, wave exposure and occurrence of filamentous algae. Positive effects of increasing wave exposure, salinity and structure were detected while a high cover of filamentous algae had a negative effect. Sand and gravel were preferred over soft and stony substrates. For turbot the best habitat descriptors in order of contribution were: occurrence of filamentous algae, substrate and turbidity. Turbot showed a preference for areas with a low cover of filamentous algae, high turbidity and sandy substrate. Prey availability and abundance of competitors were not included in the models, indicating that the distribution of flatfishes at the scales studied (tens of kilometres) is mainly governed by physical habitat properties. These results constitute the basis for future efforts on mapping of essential flatfish habitats in the Baltic Sea.

Keywords
habitat, Baltic Sea, Platichthys flesus, Psetta maxima, modelling
National Category
Biological Sciences
Identifiers
urn:nbn:se:uu:diva-111603 (URN)10.1016/j.ecss.2009.01.012 (DOI)
Available from: 2009-12-17 Created: 2009-12-17 Last updated: 2017-12-12Bibliographically approved
2. Habitat selectivity of substrate-spawning fish: modelling requirements for the Eurasian perch Perca fluviatilis
Open this publication in new window or tab >>Habitat selectivity of substrate-spawning fish: modelling requirements for the Eurasian perch Perca fluviatilis
Show others...
2010 (English)In: Marine Ecology Progress Series, ISSN 0171-8630, E-ISSN 1616-1599, Vol. 398, p. 235-243Article in journal (Refereed) Published
Abstract [en]

Substrate spawning fish are believed to be selective in their choice of spawning habitat,yet few studies have shown the relative importance of different characteristics in terms of habitatquality. We used an extensive and detailed dataset to identify the factors that govern both large-scale(1 000 to 100 000 m) and local-scale (10 to 100 m) selection by a substrate-spawning fish, the Eurasian perchPerca fluviatilis L. Distribution of spawning habitat was strongly dependent on habitat characteristicsdefined by substrate, wave exposure, temperature and depth. The most important predictor was thetype of spawning substrate, which generally consisted of different types of vegetation. Substratesproviding rigidity and structural complexity were preferred, despite abundant presence of other substratetypes. Shallow depth and sheltered areas were also selected habitat characteristics. Theresponse to temperature was scale-dependent, with a stronger selection expressed at the local scale.The specific selectivity suggests that spawning patterns can be successfully modelled with sufficientdetail using only a few fundamental environmental variables. Wave exposure and depth are readilyavailable for large-scale spatial predictions, while temperature and substrate require further developmentin most coastal areas. The high specificity of the characteristics determining habitat qualitysuggests that it should be possible to apply this modelling approach for identification and conservationof spawning habitats of Eurasian perch and other substrate-spawning fishes in coastal waters.

Keywords
Habitat modelling, Nursery areas, Large-scale maps, Macrophytes, Behaviour; Oviposition, Generalised additive model, GAM
Identifiers
urn:nbn:se:uu:diva-132618 (URN)10.3354/meps08313 (DOI)000273968500018 ()
Available from: 2010-10-22 Created: 2010-10-22 Last updated: 2017-12-12Bibliographically approved
3. Transferability of predictive fish distribution models in two coastal systems
Open this publication in new window or tab >>Transferability of predictive fish distribution models in two coastal systems
Show others...
2009 (English)In: Estuarine, Coastal and Shelf Science, ISSN 0272-7714, E-ISSN 1096-0015, Vol. 83, no 1, p. 90-96Article in journal (Refereed) Published
Abstract [en]

Species distribution modelling has emerged as a tool both for exploring niche theory and for producing distribution maps for management. To understand and predict potential effects of large scale habitat change there is a need for proper model validation and applicability also in unstudied areas. However, knowledge about factors influencing the transferability of distribution models, i.e. the accuracy of the models when applying them in a new geographical area, is limited. We have successfully modelled the larval distribution of two fish species, northern pike (Esox lucius L.) and roach (Rutilus rutilus L.), on a regional scale in the Baltic Sea using a few and easily measured environmental variables. When models were transferred from the training area to the testing area the models showed reasonable to very good discrimination (ROC 0.75 and 0.93) based on external validation using independent data separated also in time (1–2 years). The predicted larval distribution also overlapped with the distribution of young-of-the-year fish later in the season. Performance when reversing the transfer, by constructing the models in the testing area and predicting back to the original training area, was less successful. This discrepancy was species-specific and could be explained by differences in the species presence ranges along the predictor variables in the testing area compared to the training area. Our results illustrate how transferability success can be influenced by area-specific differences in the range of the predictor variables and show the necessity of validating model predictions properly.

 

Keywords
prediction, modelling, fish larvae, habitat selection, GAM, Baltic Sea
National Category
Biological Sciences
Identifiers
urn:nbn:se:uu:diva-111709 (URN)10.1016/j.ecss.2009.03.025 (DOI)000266540000010 ()
Available from: 2009-12-21 Created: 2009-12-21 Last updated: 2017-12-12Bibliographically approved
4. Ecological coherence of Marine Protected Area networks: A spatial assessment using species distribution models
Open this publication in new window or tab >>Ecological coherence of Marine Protected Area networks: A spatial assessment using species distribution models
2011 (English)In: Journal of Applied Ecology, ISSN 0021-8901, E-ISSN 1365-2664, Vol. 48, no 1, p. 112-120Article in journal (Refereed) Published
Abstract [en]

The juvenile stages of fish are often dependent on specific habitat types for their survival. Protecting these habitats may be crucial for maintaining strong adult stocks. The Natura 2000 network of the European Union offers protection of marine habitats that are essential for the recruitment of many fish species. By protecting these critical habitats the network may be important for maintaining the stocks of these fish species. 2.We present a spatially explicit, GIS-based, assessment of two important components of the ecological coherence of Marine Protected Area (MPA) networks: representativity and connectivity. Representativity can be measured as the proportion of each conservation feature that is protected, whereas connectivity assesses the spatial configuration of the network. We apply these analyses to study the ecological coherence of the Natura 2000 network in a 30 000-km2 archipelago in the Baltic Sea, with respect to a coastal fish assemblage and associated habitats. The analyses are based on fish distribution maps that have been constructed by statistically relating life stage specific occurrence to environmental variables, and thereafter making spatial predictions based on maps of the environmental variables. 3.The map-based analyses show that both the representativity and the connectivity of the network are poor with respect to the studied fish species. In total, 3.5% (11 km2) of the assemblage recruitment habitat was protected and 48% of the potentially connected habitats were included in the MPA network. 4.The assessment explicitly identified geographical areas, visually communicated using maps, where the network should be improved to ensure ecological coherence. 5.Synthesis and applications.Many MPA networks around the world, such as the Natura 2000 network in Europe, have recently come into effect. Establishment of the networks has often been governed by opportunity rather than by strict ecological analyses, primarily because distribution maps of species and habitats have been unavailable. Map-based assessments of the strengths and weaknesses of evolving MPA networks, such as the one presented here, are needed in adaptive management. They can provide an efficient tool for visualising and communicating the results to stakeholders and policy makers in the process of working towards ecological coherence.

Keywords
Baltic Sea, Connectivity, Essential fish habitats, Habitat modelling, Habitats directive, Marine reserves, Natura 2000
National Category
Biological Sciences
Identifiers
urn:nbn:se:uu:diva-132619 (URN)10.1111/j.1365-2664.2010.01892.x (DOI)000286000100014 ()
Available from: 2010-10-22 Created: 2010-10-22 Last updated: 2017-12-12Bibliographically approved
5. Spatial analysis shows recruitment habitat limitation of large predatory fish
Open this publication in new window or tab >>Spatial analysis shows recruitment habitat limitation of large predatory fish
(English)Manuscript (preprint) (Other academic)
Identifiers
urn:nbn:se:uu:diva-132622 (URN)
Funder
Note
Manuskript till sammansatt avhandling av förste författaren Available from: 2010-10-22 Created: 2010-10-22 Last updated: 2011-01-13

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