Modelling species presence–absence in the ecological niche theory framework using shape-constrained generalized additive models

Citores, Leire; Ibaibarriaga, Leire; Lee, Dae‐Jin; Brewer, Mark J.; Santos, M.B.; Chust, Guillem

doi:10.1016/j.ecolmodel.2019.108926

Cited by 28 publications

(18 citation statements)

References 86 publications

(122 reference statements)

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“…In this study, we successfully integrated the outputs of a statistical habitat model into a spatial trophic model. The limited complexity of the GAM smoothers employed here, together with the choice of modeling probabilities of presence and not abundances with the GAMs, are in line with the ecological niche theory (Citores et al, 2020). The European IBTS survey data employed in this study encompassed a large range of environmental values, hence providing insights into the potential response of species groups to the warmer conditions that could become the norm in the Celtic Sea by the end of the century.…”

Section: Habitat Modelingmentioning

confidence: 73%

The Celtic Sea Through Time and Space: Ecosystem Modeling to Unravel Fishing and Climate Change Impacts on Food-Web Structure and Dynamics

et al. 2020

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Both trophic structure and biomass flow within marine food webs are influenced by the abiotic environment and anthropogenic stressors such as fishing. The abiotic environment has a large effect on species spatial distribution patterns and productivity and, consequently, spatial co-occurrence between predators and prey, while fishing alters species abundances and food-web structure. In order to disentangle the impacts of the abiotic environment and fishing in the Celtic Sea ecosystem, we developed a spatio-temporal trophic model, specifically an Ecopath with Ecosim with Ecospace model, for the period 1985–2016. In this model, particular attention was paid to the parameterization of the responses of all trophic levels to abiotic environmental changes. Satellite remote sensing data were employed to determine the spatial distribution and annual fluctuations of primary production (PP). Spatial and temporal changes in the habitat favorable for zooplankton were predicted with a novel ecological-niche approach using daily detection of productivity fronts from satellite ocean color. Finally, functional responses characterizing the effect of several abiotic environmental variables (including, among others, temperature, salinity and dissolved oxygen concentration, both at the surface and at the bottom) on fish species groups’ habitat suitability were produced from the predictions of statistical habitat models fitted to presence-absence data collected by multiple fisheries-independent surveys. The dynamic component of our model (Ecosim) was driven by time-series of fishing effort, PP, zooplankton habitat suitability and abiotic environmental variables, and was fitted to abundance and fisheries catch data. The spatial component of our model (Ecospace) was constructed, for specific years of the period 1985–2016 with contrasted abiotic environmental conditions, to predict the variable distribution of the biomass of all functional groups. We found that fishing was the main driver of observed ecosystem changes in the Celtic Sea over the period 1985–2016. However, the integration of the environmental variability into the model and the subsequent improvement of the fit of the dynamic Ecosim component highlighted (i) the control of the overall pelagic production by PP and (ii) the influence of temperature on the productivity of several trophic levels in the Celtic Sea, especially on trophic groups with warm and cold water affinities. In addition, Ecospace predictions indicated that the spatial distributions of commercial fish species may have substantially changed over the studied period. These spatial changes mainly appeared to be driven by temperature and may, therefore, largely impact future fisheries given the continuity of climatic changes.

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Section: Habitat Modelingmentioning

confidence: 73%

The Celtic Sea Through Time and Space: Ecosystem Modeling to Unravel Fishing and Climate Change Impacts on Food-Web Structure and Dynamics

et al. 2020

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“…GAMs and machine-learning analyses provide for flexible, nonlinear response curves, but they can also lead to overfitting, particularly when data gaps exist. To better conform to ecological niche theory, shape-constrained GAMs have been introduced to SDMs [ 3 ]. These models constrain response curves to concave functions with the expectation of a unimodal relationship, and the models are particularly applicable when investigating global maxima for variables such as temperature [ 3 ].…”

Section: Discussionmentioning

confidence: 99%

“…To better conform to ecological niche theory, shape-constrained GAMs have been introduced to SDMs [3]. These models constrain response curves to concave functions with the expectation of a unimodal relationship, and the models are particularly applicable when investigating global maxima for variables such as temperature [3]. Given the various spatial scales of predictors influencing marine species' distributions [24,62], we suggest explicit consideration of potential interactions or techniques that automatically compute interactions (e.g., boosted regression trees).…”

Section: Plos Onementioning

confidence: 99%

“…Species distribution models (SDMs), and similar methods of spatial modeling, have proliferated in the last two decades with the growing availability of geospatial data, increased computational capacity, and new approaches to model building. Spatial modeling approaches have changed dramatically from habitat suitability indices developed in the 1970s [ 1 ] to correlative species distribution models [ 2 ], shape-constrained models [ 3 ], individual-based models [ 4 ], mechanistic or physiology-based models [ 5 ], bioclimatic envelopes [ 6 ], and machine learning techniques with a basis in predictive ability [ 7 ]. However, the number of SDMs developed for marine species has lagged behind the effort of terrestrial counterparts, which has been growing rapidly since the 1990s [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

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A systematic review of spatial habitat associations and modeling of marine fish distribution: A guide to predictors, methods, and knowledge gaps

et al. 2021

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As species distribution models, and similar techniques, have emerged in marine ecology, a vast array of predictor variables have been created and diverse methodologies have been applied. Marine fish are vital food resources worldwide, yet identifying the most suitable methodology and predictors to characterize spatial habitat associations, and the subsequent distributions, often remains ambiguous. Our objectives were to identify knowledge gaps in fish guilds, identify research themes, and to determine how data sources, statistics, and predictor variables differ among fish guilds. Data were obtained from an international literature search of peer-reviewed articles (2007–2018; n = 225) and research themes were determined based on abstracts. We tested for differences in data sources and modeling techniques using multinomial regressions and used a linear discriminant analysis to distinguish differences in predictors among fish guilds. Our results show predictive studies increased over time, but studies of forage fish, sharks, coral reef fish, and other fish guilds remain sparse. Research themes emphasized habitat suitability and distribution shifts, but also addressed abundance, occurrence, stock assessment, and biomass. Methodologies differed by fish guilds based on data limitations and research theme. The most frequent predictors overall were depth and temperature, but most fish guilds were distinguished by their own set of predictors that focused on their specific life history and ecology. A one-size-fits-all approach is not suitable for predicting marine fish distributions. However, given the paucity of studies for some fish guilds, researchers would benefit from utilizing predictors and methods derived from more commonly studied fish when similar habitat requirements are expected. Overall, the findings provide a guide for determining predictor variables to test and identifies novel opportunities to apply non-spatial knowledge and mechanisms to models.

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“…In contrast, ecological niche theory expects unimodal relationships with respect to environmental gradients (Hutchinson 1957). Therefore, it may be important to use shape-constrained effects (Citores et al 2020) and/or visual confirmation to make sure that our SDMs fit meaningful relationships (Elith & Leathwick 2009).…”

Section: Modelling Spatiotemporal Fish Distribution Using Standard Sumentioning

confidence: 99%

Evidence for spatiotemporal shift in demersal fisheries management priority areas in the western Mediterranean

Paradinas

Giménez

Conesa

et al. 2021

Preprint

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Fishery restricted areas (FRAs) are pivotal for the mitigation of fishery impacts, but the designation of optimum FRAs is complex, and currently there is no quantitative guideline to assess the spatio-temporal variability of conservation priority areas. Fishery systems are naturally dynamic, and shifts may at two different levels, the species level and the community level. As a consequence, the identification of optimum FRAs using standard fishery data should follow a spatiotemporal procedure that accounts for both levels of dynamism. Here, we describe an approach to optmise the location of FRAs assessing the spatiotemporal dynamism of conservation priority areas at both the species and the multi-species level. To do so, we first assess species-specific dynamisms through Species Distribution Models (SDMs). Then, we use SDM results to explore different spatial prioritisation software configurations to assess the suitability of fixed, progressive, or more complex fishery restricted area designs. Finally, we assess the ecologic and socioeconomic impact of FRA candidates based on our SDM estimates and fishery footprint data, respectively. The proposed method is illustrated through a western Mediterranean case study using scientific trawl survey data on six commercially important species collected over 17 years. Following this approach, we identified two main priority area patterns in the study area. Each pattern is predominant during a different period of the study, constituting a clear example of the importance of regularly re-assessing FRA designs. We conclude that a spatiotemporal assessment of conservation priority areas using long-term data is essential to inform the location of new FRAs.

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Modelling species presence–absence in the ecological niche theory framework using shape-constrained generalized additive models

Cited by 28 publications

References 86 publications

The Celtic Sea Through Time and Space: Ecosystem Modeling to Unravel Fishing and Climate Change Impacts on Food-Web Structure and Dynamics

The Celtic Sea Through Time and Space: Ecosystem Modeling to Unravel Fishing and Climate Change Impacts on Food-Web Structure and Dynamics

A systematic review of spatial habitat associations and modeling of marine fish distribution: A guide to predictors, methods, and knowledge gaps

Evidence for spatiotemporal shift in demersal fisheries management priority areas in the western Mediterranean

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