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- Gonzalez-Mirelis, Genoveva, 1977, et al.
(författare)
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Predicting the distribution of out-of-reach biotopes with decision trees in a Swedish marine protected area
- 2012
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Ingår i: Ecological Applications. - 1051-0761. ; 22:8, s. 2248-2264
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Tidskriftsartikel (refereegranskat)abstract
- Through spatially explicit predictive models, knowledge of spatial patterns of biota can be generated for out-of-reach environments, where there is a paucity of survey data. This knowledge is invaluable for conservation decisions. We used distribution modeling to predict the occurrence of benthic biotopes, or megafaunal communities of the seabed, to support the spatial planning of a marine national park. Nine biotope classes were obtained prior to modeling from multivariate species data derived from point source, underwater imagery. Five map layers relating to depth and terrain were used as predictor variables. Biotope type was predicted on a pixel-by-pixel basis, where pixel size was 15315 m and total modeled area was 455 km(2). To choose a suitable modeling technique we compared the performance of five common models based on recursive partitioning: two types of classification and regression trees ([1] pruned by 10-fold cross-validation and [2] pruned by minimizing complexity), random forests, conditional inference (CI) trees, and CI forests. The selected model was a CI forest (an ensemble of CI trees), a machine-learning technique whose discriminatory power (class-byclass area under the curve [AUC] ranged from 0.75 to 0.86) and classification accuracy (72%) surpassed those of the other methods tested. Conditional inference trees are virtually new to the field of ecology. The final model's overall prediction error was 28%. Model predictions were also checked against a custom-built measure of dubiousness, calculated at the polygon level. Key factors other than the choice of modeling technique include: the use of a multinomial response, accounting for the heterogeneity of observations, and spatial autocorrelation. To illustrate how the model results can be implemented in spatial planning, representation of biodiversity in the national park was described and quantified. Given a goal of maximizing classification accuracy, we conclude that conditional inference trees are a promising tool to map biota. Species distribution modeling is presented as an ecological tool that can handle a wide variety of systems (e.g., the benthic system).
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- Gonzalez-Mirelis, Genoveva, 1977
(författare)
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Spatial distribution and conservation planning of seabed biological diversity
- 2011
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Knowledge of spatial patterns of biota has become a commodity for conservation practitioners and spatial ecologists alike. This type of information enables the identification of representative and unique biological features (at some nominal scale) which itself constitutes the application par excellence of knowledge of biodiversity distribution as it relates to the design of reserve networks and the realization of spatial planning. Furthermore, insight into the spatial distribution of the various components of biological diversity provides a way of addressing issues of fundamental ecology relating to the processes influencing the variation of ecosystem structure across space. The present thesis is concerned with methods (and related issues) to document the spatial distribution of diversity at the assemblage (or community) level, which is later proven to be an adequate surrogate for diversity patterns at the species level, and therefore an adequate approach for identifying sites representative of regional biodiversity. This was investigated across the benthic portion of a Marine National Park off the West coast of Sweden. At the center of this thesis is the production of a map of benthic biotopes by use of automated, objective methods, of measurable accuracy, and that can support marine spatial planning. In Papers I and II, I address various aspects related to the data model underpinning this map. Paper I deals with patterns of spatial patchiness of benthic communities, which helped determine the appropriate resolution at which epibenthic biological diversity in this area is best investigated. Here, spatial autocorrelation is measured at a range of scales and used to determine an appropriate grain size for subsequent sampling. This will become a backbone of this study, as it determines the (only) spatial scale for which the findings are relevant. In Paper II I assess the performance of classifications of communities at varying levels of compositional detail as a way of calibrating the classification scheme to be used as the basis for the map. Paper III is a case study of predictive mapping of communities. The process was driven by patterns of occurrence of benthic communities, which were then extrapolated using observed biota-environment relationships, by means of full-coverage variables derived from multibeam data. This approach draws heavily from the field of distribution modelling of species and/or communities. Further, I present a number of analysis techniques that are new to benthic ecology, and virtually new to predictive mapping in general (albeit not to the field of predictive, statistical modelling, and classification algorithms). In Paper IV I evaluate the applicability of the produced map of benthic biotopes as a tool for conservation planning. Particularly, I test the value of the outputs from the model introduced in Paper III (i.e., the biotopes with their associated spatial attributes) as conservation features, or surrogates for biodiversity, in the context of systematic conservation planning, to represent biodiversity at other hierarchical levels and across ecological niches.
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- Gonzalez-Mirelis, Genoveva, 1977, et al.
(författare)
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Using Vessel Monitoring System Data to Improve Systematic Conservation Planning of a Multiple-Use Marine Protected Area, the Kosterhavet National Park (Sweden)
- 2014
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Ingår i: Ambio. - : Springer Science and Business Media LLC. - 0044-7447 .- 1654-7209. ; 43:2, s. 162-174
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Tidskriftsartikel (refereegranskat)abstract
- When spatial fishing data is fed into systematic conservation planning processes the cost to a fishery could be ensured to be minimal in the zoning of marine protected areas. We used vessel monitoring system (VMS) data to map the distribution of prawn trawling and calculate fish- ing intensity for 1-ha grid cells, in the Kosterhavet National Park (Sweden). We then used the software Marxan to generate cost-efficient reserve networks that represented every biotope in the Park. We asked what were the potential gains and losses in terms of fishing effort and species conservation of different planning scenarios. Given a conservation target of 10 % representation of each bio- tope, the fishery need not lose more than 20 % of its fishing grounds to give way to cost-efficient conservation of ben- thic diversity. No additional reserved area was needed to achieve conservation targets while minimizing fishing costs. We discuss the benefits of using VMS data for conservation planning.
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