Combined species occurrence and density predictions to improve marine spatial management

Rullens, Vera; Stephenson, Fabrice; Lohrer, Andrew M.; Townsend, Michael; Pilditch, Conrad A.

doi:10.1016/j.ocecoaman.2021.105697

Cited by 21 publications

(12 citation statements)

References 59 publications

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“…a greater density of conservation target(s) (Johnston et al, 2015). Here, models that predict habitat suitability were used in place of abundance models, but caution must be placed on the interpretation of habitat suitability as it may not correspond directly with abundance (Rullens et al, 2021; Stephenson et al, 2022; Watson et al, 2022). In some cases, the main drivers of taxon density can be different to drivers of the probability of occurrence (Johnston et al, 2015; Lester et al, 2022; Tôrres et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

Modelling spatial distributions of biogenic habitat‐forming taxa to inform marine spatial planning

Bennion,

Brough,

Leunissen

et al. 2024

Aquatic Conservation

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Biogenic habitats are foundational habitats for species assemblages and drive a range of ecosystem functions. The Hauraki Gulf/Tiikapa Moana is the most intensively used coastal area in Aotearoa/New Zealand, and decades of commercial fishing, sedimentation and industrialization have degraded biogenic habitats in the Gulf. In response, the marine spatial plan ‘Sea Change’ includes proposals to create new and extend existing marine protected areas (MPAs) and restrict the area open to mobile bottom‐impact fishing methods to conserve and help the recovery of biogenic habitats. To assess the benefits of different spatial planning scenarios for biogenic habitats, information on their spatial distribution is needed, but data limitations are a significant challenge. Here, an approach is detailed that maximized the information extracted from limited species occurrence data, by incorporating expert knowledge to develop and evaluate models of biogenic habitat‐forming taxa. Ensemble habitat suitability models (using boosted regression tree and random forest models) were created for 20 biogenic habitat groups. Using withheld data for validation, area under curve (AUC) scores ranged from 0.58 to 0.95 and true skill statistics (TSS) ranged from 0.37 to 0.84, though three models were further evaluated as insufficient representations of known ecological habitats by expert assessors. Models produced here provide substantially increased information to inform progress on implementation of the Sea Change plan. Two stakeholder processes have been held, resulting in the development of spatial plans for bottom trawl mitigation that are currently under public consultation. Systematic surveys that gather abundance and high‐resolution environmental data should be a priority to improve the utility of predictive models and inform future management processes in the Hauraki Gulf, and funding has been allocated to support additional data collection to determine the effectiveness of spatial management measures in the Hauraki Gulf.

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Section: Discussionmentioning

confidence: 99%

Modelling spatial distributions of biogenic habitat‐forming taxa to inform marine spatial planning

Bennion,

Brough,

Leunissen

et al. 2024

Aquatic Conservation

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“…Three types of uncertainty were considered, namely the uncertainty in bivalve density predictions generated by the SDMs, ES model parameter settings for weights and scoring thresholds (Figure 1). Two measures of spatially explicit uncertainty from the SDMs in Rullens et al (2021) were considered for the sensitivity analysis.…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…Environmental coverage is a metric that reflects how well the environmental conditions in an unsampled gridcell were represented by the sampled sites (Smith et al, 2013;Stephenson et al, 2020). Environmental coverage was modelled using Boosted Regression Trees (BRTs) for sampled sites (n = 156 for Austrovenus and n = 170 for Paphies) and randomly selected unsampled sites (n = 1000), in relation to the 6 environmental variables used in the SDMs (Rullens et al, 2021). BRTs with a Bernoulli error distribution were fitted to the 'present' sampled sites and the 'absent' unsampled box 2, Section 2.3) and Uncertainty analysis (box 3, Sections 2.4 and 2.5). sites, with a learning rate yielding approximately 2000 trees and interaction depth of 2.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Accounting for uncertainty in marine ecosystem service predictions for spatial prioritisation

Rullens,

Stephenson,

Townsend

et al. 2024

Diversity and Distributions

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AimSpatial assessments of Ecosystem Services (ES) are increasingly used in environmental management, but rarely provide information on the prediction accuracy. Uncertainty estimates are essential to provide confidence in the quality and credibility of ES assessments for informed decision making. In marine environments, the need for uncertainty assessments for ES is unparalleled as they are data scarce, poorly (spatially) defined, with complex interconnectivity of seascapes. This study illustrates the uncertainty associated with a principle‐based method for ES modelling by accounting for model variability, data coverage and uncertainty in thresholds and parameters.LocationTauranga, New Zealand.MethodsA sensitivity analysis was applied on ES models for marine bivalves (Austrovenus stutchburyi and Paphies australis) and their contribution to Food provision, Water quality regulation, Nitrogen removal and Sediment stabilisation. ES estimates from the sensitivity analysis were compared against baseline ES predictions. Spatial uncertainty patterns were analysed for individual ES through bi‐plots and multiple ES through spatial prioritisation using Zonation.ResultsOur study showed spatially explicit differences in uncertainty patterns for ES and between species. Food provision had highest maximum uncertainty (>5 points) but also the largest area of high ES and high certainty conditions. Zonation analysis conducted on baseline and conservative ES values showed overall robust outcomes of top 30% area, but important nuances through shifts in top 5% and 10% areas that allowed for a consistently better representation of ES when accounting for uncertainty.Main ConclusionsThe spatial prioritisation in combination with the ES uncertainty biplots provide tools for spatial planning of individual and multiple ES to focus on area of highest value with highest certainty and can thereby help reduce risk and aid decision‐making at acceptable confidence levels. This type of information is urgently needed in marine ES assessments and their management, but likewise extends to other environments to improve transparency.

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“…Most marine SDM studies have focused on predicting species occurrence, whereas abundance is much less frequently predicted (Melo-Merino et al, 2020). A comparison of the spatial predictions of occurrence compared to density (abundance/area) of Austrovenus revealed that density estimates were much more constrained than the broader and less informative predictions of occurrence (Rullens et al, 2021). This has important implications for conservation planning because the more extensive area identified from the occurrence models may not have the highest densities and therefore may not be the best areas for conservation (Rullens et al, 2021).…”

Section: Incorporation Of Biotic Variables In Species Distribution Mo...mentioning

confidence: 99%

Inclusion of biotic variables improves predictions of environmental niche models

Stephenson

Gladstone‐Gallagher

Bulmer

et al. 2022

Diversity and Distributions

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Aim Species Distribution Models (SDMs) are correlative models that predict the occurrence or abundance of species in relation to predictor variables. SDMs have become an important part of resource management and conservation biology yet they rarely incorporate species’ biology or demography into their predictions. To explore the possible influence of biotic relationships in explaining patterns of species’ distribution, abundance and explanatory power of SDMs, we chose two intertidal shellfish species with overlapping but different environmental preferences (Austrovenus stutchburyi and Macomona liliana) and modelled their distributions with and without biotic variables. Location New Zealand. Methods The relationship between environmental and biotic variables on the abundance of our two species was investigated using Boosted Regression Trees (BRTs) with increasing model complexity: (1) BRT models using environmental variables were fitted to each species; (2) BRT models using environmental variables and the co‐occurring abundance of the study taxa not being modelled were fitted; (3) BRT models using environmental variables, the co‐occurring abundance and the estimated abundance of the species’ patch of the study taxa not being modelled were fitted. Results A strong, non‐linear effect of the abundance of Austrovenus on Macomona was observed but only a weak effect of Macomona on Austrovenus. The inclusion of biotic variables improved the model fit metrics for both species, as assessed by withheld evaluation data, markedly so for Macomona. The overall deviance explained by the models increased, the correlation of predicted vs observed abundance data increased and the variability in these measures decreased. Main conclusions The combination of the improvement in model performance and changes in the influence of variables with the inclusion of biotic variables is of importance when predicting into unsampled space (e.g. when predicting impacts of climate change). Our approach improves classic SDMs by integrating ecological theories of how species interactions can alter species distributions across environmental gradients.

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Combined species occurrence and density predictions to improve marine spatial management

Cited by 21 publications

References 59 publications

Modelling spatial distributions of biogenic habitat‐forming taxa to inform marine spatial planning

Modelling spatial distributions of biogenic habitat‐forming taxa to inform marine spatial planning

Accounting for uncertainty in marine ecosystem service predictions for spatial prioritisation

Inclusion of biotic variables improves predictions of environmental niche models

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