Model-based thinking for community ecology

Warton, David I.; Foster, Scott D.; De’ath, Glenn; Stoklosa, Jakub; Dunstan, Piers K.

doi:10.1007/s11258-014-0366-3

Cited by 135 publications

(129 citation statements)

References 39 publications

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“…what are the key players, what influences them and how are they connected?) and using that to develop informative statistical models that can function at the scales required [106]. As with the minimum realistic process models, these statistical models focus on specific properties of the system (e.g.…”

Section: Drawbacksmentioning

confidence: 99%

Modelling marine protected areas: insights and hurdles

Fulton

Bax

Bustamante

et al. 2015

Phil. Trans. R. Soc. B

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Models provide useful insights into conservation and resource management issues and solutions. Their use to date has highlighted conditions under which no-take marine protected areas (MPAs) may help us to achieve the goals of ecosystem-based management by reducing pressures, and where they might fail to achieve desired goals. For example, static reserve designs are unlikely to achieve desired objectives when applied to mobile species or when compromised by climate-related ecosystem restructuring and range shifts. Modelling tools allow planners to explore a range of options, such as basing MPAs on the presence of dynamic oceanic features, and to evaluate the potential future impacts of alternative interventions compared with ‘no-action’ counterfactuals, under a range of environmental and development scenarios. The modelling environment allows the analyst to test if indicators and management strategies are robust to uncertainties in how the ecosystem (and the broader human–ecosystem combination) operates, including the direct and indirect ecological effects of protection. Moreover, modelling results can be presented at multiple spatial and temporal scales, and relative to ecological, economic and social objectives. This helps to reveal potential ‘surprises', such as regime shifts, trophic cascades and bottlenecks in human responses. Using illustrative examples, this paper briefly covers the history of the use of simulation models for evaluating MPA options, and discusses their utility and limitations for informing protected area management in the marine realm.

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Section: Drawbacksmentioning

confidence: 99%

Modelling marine protected areas: insights and hurdles

Fulton

Bax

Bustamante

et al. 2015

Phil. Trans. R. Soc. B

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“…“fuzzy” in Brown, ; and De Cáceres & Wiser, ) or hard (highest probability), providing information suitable for most current management frameworks. The underlying structure of the ecological data is also appropriately dealt with (Warton et al., ). A further strength of the approach is that it enables deductive predictions of vegetation responses when the environment changes, for example, through anthropogenic climate change.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous vegetation classification and mapping at large spatial scales

Lyons

Foster

Keith

2017

Journal of Biogeography

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Aim Multivariate mixture models offer the ability to streamline the typically multi‐stage process of vegetation classification and mapping into a single, simultaneous analytical step. Our aim is to demonstrate the model's utility over large and diverse areas, with comparison to existing classification and mapping conventions. Location New South Wales, Australia. Methods We demonstrate a statistical model that uses both multivariate species data and environmental covariate data to simultaneously classify observations into groups and predict those groups out into environmental and geographical space. We used two large data sets to demonstrate the method: an ~810,000 km2 area with 4,715 sites and 488 species and a ~220,000 km2 area with 5,183 sites and 446 species. A range of topographic, terrain, climate and soil predictors were used as environmental variables, including future projected climate variables. Models can be fit with the R package “RCPmod.” Results The method results in probabilistic memberships to vegetation assemblages, for both the classification and map. There was general agreement between our approach and existing vegetation classification conventions, but we explore some notable differences. We were able to examine the environmental gradients that define the predicted vegetation distributions, as well as make predictions about how the distribution of species assemblages might shift as a result of climate change. Main conclusions Our approach tightens the link between description of biodiversity patterns and their depiction in space, by considering both biotic and spatially explicit abiotic information simultaneously. The method allows uncertainty to be quantified objectively across a consistent set of groups for both vegetation classification and mapping, which is rarely the case in traditional multi‐stage approaches. A simultaneous modelling approach increases capacity to make predictions into varying spatial, temporal and environmental dimensions, providing new ecological insights and increasing capacity for evidence‐based decision making.

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“…A recent surge of interest in model-based methods for the analysis of multivariate data has resulted in an expanding number of statistical tools that offer an alternative to sitebased multivariate analyses by making species or species groups the response unit, thereby allowing formal description and inference about their relationship with environmental variables and a greater flexibility in modeling species co-existence (Warton et al, 2015b). These include methods for unconstrained ordination (Hui et al, 2014;Hui, 2015), correspondence analysis (Pledger and Arnold, 2014), exploring community-environment associations and fitting predictive models , model selection (Madon et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Predicting the Composition of Polychaete Assemblages in the Aegean Coast of Turkey

2016

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Benthic infaunal species and communities have been extensively used to evaluate quality of the marine environment. Within the MSFD, community composition is addressed most commonly through Descriptor 6 (Seafloor integrity), criterion 6.2 (Condition of benthic communities). At the same time, the Directive has stipulations for addressing and assessing indicators linked with pressures in an explicitly spatial manner. At larger scales, achieving this through point sampling may be impractical or unfeasible; hence predictive methods are being increasingly employed to produce the large scale spatial data that are often required for marine spatial planning and management. The aim of the current work was to develop statistical and spatial modeling tools that can predict the distribution of soft-sediment benthic polychaetes in the Aegean coast of Turkey. To do that, we employed Species Archetype Models (SAMs), a novel analytical and modeling framework which uses mixture models to cluster species responses to the environment, producing a number of "archetypal" responses assumed to represent species with similar ecological/physiological tolerances. Polychaete presence/absence data were obtained from the literature and modeling was performed against environmental variables reflecting the main natural and anthropogenic gradients in the region. The resulting models are interpreted in light of the sensitivity/tolerance classification scheme for benthic invertebrates. Three Species Archetypes were identified through the analysis. In brief, Species Archetype 1 consists of the most prevalent species in the dataset and primarily follows the salinity and temperature gradients. Species Archetype 2, present in the central and southern Aegean, is dominated by sensitive and indifferent species and responds negatively to chlorophyll a, whereas Species Archetype 3 represents mostly tolerant and opportunistic polychaetes with increased probability of occurrence in eutrophic, shallow, inshore areas throughout the region. Predictive performance was constrained by the information contained in our data. These results from a limited data set show promise that SAMs as a modeling tool can offer valuable insights into patterns of benthic species distribution and coexistence and increase our capacity to provide predictive advice.

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Model-based thinking for community ecology

Cited by 135 publications

References 39 publications

Modelling marine protected areas: insights and hurdles

Modelling marine protected areas: insights and hurdles

Simultaneous vegetation classification and mapping at large spatial scales

Predicting the Composition of Polychaete Assemblages in the Aegean Coast of Turkey

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