Using joint species distribution modelling to predict distributions of seafloor taxa and identify vulnerable marine ecosystems in New Zealand waters
Authors:
Fabrice Stephenson, David A. Bowden, Ashley A. Rowden, Owen F. Anderson, Malcolm R. Clark, Matthew Bennion, Brittany Finucci, Matt H. Pinkerton, Savannah Goode, Caroline Chin, Niki Davey, Alan Hart, Rob Stewart
Effective ecosystem-based management of bottom-contacting fisheries requires understanding of how disturbances from fishing affect seafloor fauna over a wide range of spatial and temporal scales. Spatial predictions of abundance for 67 taxa were developed, using an extensive dataset of faunal abundances collected using a towed camera system and spatially explicit predictor variables including bottom-trawl fishing effort, using a Joint Species Distribution Model (JSDM). The model fit metrics varied by taxon: the mean tenfold cross-validated AUC score was 0.70 ± 0.1 (standard deviation) for presence–absence and an R2 of 0.11 ± 0.1 (standard deviation) for abundance models. Spatial predictions of probability of occurrence and abundance (individuals per km2) varied by taxon, but there were key areas of overlap, with highest predicted taxon richness in areas of the continental shelf break and slope. The resulting joint predictions represent significant advances on previous predictions because they are of abundance, allow the exploration of co-occurrence patterns and provide credible estimates of taxon richness (including for rare species that are often not included in more commonly used single-species distribution modelling). Habitat-forming taxa considered to be Vulnerable Marine Ecosystem (VME) indicators (those taxa that are physically or functionally fragile to anthropogenic impacts) were identified in the dataset. Spatial estimates of likely VME distribution (as well as associated estimates of uncertainty) were predicted for the study area. Identifying areas most likely to represent a VME (rather than simply VME indicator taxa) provides much needed quantitative estimates of vulnerable habitats, and facilitates an evidence-based approach to managing potential impacts of bottom-trawling.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Introduction
Effective ecosystem-based management of bottom-contacting fisheries requires understanding of how disturbances from fishing affect seafloor fauna and habitats over a wide range of spatial and temporal scales (Clark et al. 2016; Pitcher et al. 2017). A key element needed to generate such understanding is knowledge of the spatial distributions of seafloor fauna. Reliable benthic spatial information is rare however, particularly for areas where deep-sea fisheries occur and available data generally consist of point records of taxon presences assembled from disparate sources spanning many years or decades. Consequently, correlative modelling methods, in which statistical relationships between observed point occurrences of fauna and continuous environmental data layers are used to predict faunal occurrence across unsampled space (referred to as habitat-suitability, species-environment, or species-distribution models, e.g., Elith and Leathwick 2009; Guisan and Zimmermann 2000), are increasingly used to generate full-coverage maps of either predicted habitat suitability or taxon presence for use in assessments of seafloor (benthic) impacts (e.g., Mazor et al. 2021). Spatially explicit models for seafloor fauna distributions are the cornerstone of marine conservation planning (Loiselle et al. 2003; Marshall et al. 2014; Porfirio et al. 2014; Sundblad et al. 2011). However, in most cases there are many species’ distributions that are poorly described because there are too few records to generate robust species distribution models (SDMs) (Ellingsen et al. 2007). Consequently, the full complement of seafloor biodiversity is typically not represented in conservation planning, despite the important roles that less common species can play in the stability and functioning of marine ecosystems (Ellingsen et al. 2007; Zhang et al. 2018, 2020a).
In the New Zealand region, SDMs have been used for over two decades to provide predictions of seafloor faunal distributions in the deep sea to inform research, environmental management, and prediction of climate change effects across a range of spatial scales (see examples and references in Anderson et al. 2016, 2022; Rowden et al. 2017; Stephenson et al. 2021, 2023a; Tracey et al. 2011; Wood et al. 2013). Until recently, all these models (other than one small-scale study, Rowden et al. 2017) have been informed by faunal occurrence data compiled from research trawl bycatch and scientific museum/institute records, which offer information about the presence of a taxon at any given site but not its abundance (density) or absence (Bowden et al. 2021). Such models can only yield predictions of relative habitat suitability (the likely distribution of species), rather than predictions of expected abundance (Elith and Leathwick 2009). These existing models fulfil their purpose in that they provide the best estimates of the distributions of seafloor fauna in an environment that remains data-limited in terms of knowledge about both faunal distributions and the physical characteristics of their habitats. However, knowledge about spatial variations in species’ abundances is crucial for understanding ecosystem functioning (Rullens et al. 2022); for instance, the presence of a single bryozoan colony at a site will not have the same ecological influence, or conservation value, as a high density of bryozoan thickets (Wood et al. 2013). A number of studies have assessed whether presence–absence models can be used as surrogates for abundance distributions, with contrasting results; there is some evidence that the correlation between probability of occurrence and density is weaker for species with broader ecological niches than those with narrow niches (e.g., as in Rullens et al. 2021 and references therein).
Advertisement
While the value of single-taxon SDMs is certainly recognised, so too are their limitations (Bowden et al. 2021; Lee-Yaw et al. 2022; Loiselle et al. 2003), which include their disconnect from most ecological theory (Stephenson et al. 2022a; Zhang et al. 2020a). Combining or stacking SDM predictions for different species is often used to examine community-level species distribution patterns (Calabrese et al. 2014; Guisan and Rahbek 2011; Zhang et al. 2019). However, as these stacked or combination approaches model each taxon separately and combine spatial distributions post-hoc, species predictions are independent and do not allow for inter-species interactions and their combined relationships with the environment (e.g., Compton et al. 2013; Zhang et al. 2020a). In addition, rare species are often not included in community-level species distribution patterns because there are too few data to generate quantitative SDMs for these species (Zhang et al. 2020a) (but mechanistic models can be used for rarer species, e.g., Stephenson et al. 2020).
Joint species distribution models (JSDMs) offer a solution to these limitations (Ovaskainen et al. 2016a, b; Tikhonov et al. 2020; Warton et al. 2015; Zhang et al. 2018). Where SDMs link abiotic (environmental) covariates to species occurrences, JSDMs make use of latent factor approach as well (i.e., variables which are indirectly inferred within the model itself), describing occurrence as a function of environment but incorporating biotic associations such as competition, predation, or parasitism (Pichler and Hartig 2021). The latent factor approach makes use of residual correlation between species, where positive residual correlation is assumed to represent co-occurrence, and negative residual correlation means the species co-occur less often than expected, given the environment (Zurell et al. 2018). While unobserved biotic interactions are the target in the spatial latent factor approach, it is important to consider that unknown or unmeasured abiotic contributions could also be having an effect on distribution patterns (Blanchet et al. 2020; Pichler and Hartig 2021; Poggiato et al. 2021).
Since the early 2000s there has been a concerted effort in New Zealand to collect quantitative information of seafloor taxa abundances using non-destructive methods, i.e., using underwater cameras (e.g., Clark and Rowden 2009; Rowden et al. 2002). Since 2006 imagery data has been collected using the Deep Towed Imaging System (DTIS) (Bowden and Jones 2016; Hill 2009). These data provide the opportunity to generate spatial estimates of taxon abundances (rather than simply occurrences) and, in this case, to also test the use of JSDMs which may better account for species-interactions and more easily incorporate rarer species in a quantitative manner (Zhang et al. 2020a). Furthermore, outputs from JDSMs can be used to predict the occurrence of ecosystems represented by a composite of species (Ovaskainen et al. 2017), including Vulnerable Marine Ecosystems (VMEs) (Gros et al. 2022).
VMEs are ecosystems (comprised of species groups, communities or habitats) that are physically or functionally vulnerable to anthropogenic impacts; the most vulnerable ecosystems are those that are both easily disturbed and very slow to recover or may never recover. Examples of VMEs include cold-water coral reefs, sponge beds, and deep-sea hydrothermal vents (Roberts et al. 2009). VMEs are important because they may be unique, comprise structurally complex species that support and/or provide essential habitat for other species (including fish), and serve as natural carbon sinks (Cathalot et al. 2015; De Froe et al. 2019). However, VMEs are also threatened by human activities such as bottom trawling, oil and gas exploration, and deep-sea mining (Clark et al. 2016). Although VME is a term primarily associated with application in Areas Beyond National Jurisdiction (ABNJ), from an ecological and conceptual point of view, the concept of VME extends to all seafloor habitats, including areas within national jurisdictions.
Advertisement
Efforts to conserve VMEs are critical for maintaining healthy and resilient marine ecosystems (Van Dover 2010). Management measures can include the designation of marine protected areas and implementing regulations to reduce the impacts of human activities on VMEs (e.g., Australia and New Zealand 2020; Brodie and Clark 2003). One approach to VME management involves identifying VME indicator taxa (e.g., Parker and Bowden 2010), and then calculating vulnerability indices to assess the susceptibility of VMEs to various stressors, including fishing activities (Clark et al. 2016; Gros et al. 2023; Morato et al. 2018). If benthic taxa data exist, the vulnerability of areas based on the occurrence or abundance of VME indicator taxa can be used to highlight particular areas which may represent a VME (Gros et al. 2023). This approach can help policymakers and stakeholders prioritize areas for protection and management and make more informed decisions about the sustainable use of marine resources.
Using an extensive seafloor imagery dataset providing taxon occurrence and abundance estimates across a wide spatial area (Anderson et al. 2020; Bowden et al. 2019) and the recently developed JSDM approach (Ovaskainen and Soininen 2011; Warton et al. 2015; Ovaskainen et al. 2016a, b), we predict the distribution and densities of 67 invertebrate taxa (including 26 VME indicator taxa) and estimates of taxonomic richness. We then use the recently developed concept of VME indices (Gros et al. 2023; Morato et al. 2018) to generate spatial estimates of likely VME distribution (as well as associated estimates of uncertainty), and their relative vulnerability, for a large region of the New Zealand marine environment. Identifying areas most likely to represent VME (rather than simply VME indicator taxa) provides much needed quantitative estimates of the most vulnerable habitats and facilitates an evidence-based approach to management (Gros et al. 2022).
Methods
Study area
The study area spans a broad segment of New Zealand’s marine environment, encompassing two major topographic features; Chatham Rise and Campbell Plateau, and the intervening areas of the Otago continental shelf and slope, and Bounty Trough (Fig. 1). Chatham Rise is a continental rise extending eastwards from the South Island of New Zealand for approximately 1000 km. The crest of the Rise is at depths of 300–400 m, with shallower features at its western end rising to approximately 50 m depth in places, and the Chatham Islands mark its eastern end. The Sub-Tropical Front, which is a major oceanographic feature in the area, coincides with and is partially constrained by Chatham Rise and, in consequence, intense phytoplankton blooms occur along its length (Nodder and Northcote 2001; Stevens et al. 2019). The Chatham Rise is the most biologically productive large-scale, deep-sea fisheries region in New Zealand’s Exclusive Economic Zone (EEZ) (Clark et al. 2000; Marchal et al. 2009). Campbell Plateau is a broad submarine plateau extending to the south and southeast of New Zealand’s South Island. Much of the plateau lies at water depths of 500–1000 m but with shoal areas rising to the surface around islands in the west, and to less than 150 m on Pukaki Rise in the east (Fig. 1). On its western and southern boundaries, the plateau descends steeply to depths greater than 3000 m. The Sub-Antarctic Front, which forms at the northern boundary of the east-going Antarctic Circumpolar Current is constrained by the southern edge of the plateau. The Sub-Tropical Front lies to the north of the plateau, where warmer subtropical waters entrained down the west coast of the South Island recurve northwards, transporting nutrient rich, relatively colder waters towards the southern flank of Chatham Rise (Hayward et al. 2007; Hurlburt et al. 2008; Mackay et al. 2014; Murphy et al. 2001; Nelson and Cooke 2001). Biological productivity, in terms of water-column primary production, is lower than on Chatham Rise but is elevated above levels in the surrounding ocean (Gutierrez-Rodriguez et al. 2020).
Fig. 1
Map of study area with approximate extents of Chatham Rise (CR) and Campbell Plateau (CP) and survey sites from which benthic invertebrate abundance data were collated from seafloor photographic transects (red points). Isobaths represent the shallowest (100 m—blue line) and deepest (1500 m—white line) extents of the model predictions
×
Commercially important bottom trawl fisheries exploiting populations of scampi (Metanephrops challengeri), hoki (Macruronus novaezelandiae), orange roughy (Hoplostethus atlanticus), oreos (Pseudocyttus maculatus, Neocyttus rhomboidalis), arrow squid (Nototodarus spp.), jack mackerels (Trachurus spp.), southern blue whiting (Micromesistius australis—although largely midwater trawl) and others have operated across the area in depths down to approximately 1200 m since the 1970s (Fisheries New Zealand 2023). Recent summaries of bottom-contacting trawl history (Baird and Mules 2021) show highest trawling intensity, primarily from the hoki fishery, at 450–700 m depth west of Mernoo Bank and on the southern and northern central flanks of Chatham Rise.
Faunal data
The faunal dataset used is an augmented version of that developed by Bowden et al. (2019) and Anderson et al. (2020) for application as an independent test of existing SDM predictions (Bowden et al. 2021). In brief, these data were derived from analyses of seafloor video transects collected with the DTIS towed camera system during seven surveys spanning the period 2006 to 2020. Data consist of quantitative abundance estimates for invertebrate taxa at 467 seafloor sites: 358 from Chatham Rise and 109 from Campbell Plateau (Fig. 1). The survey data span a depth range from 49 to 1813 m but because there were few sites at the shallow and deep extremes of this range, model predictions here were restricted to depths from 100 to 1500 m (and to the spatial extent of the features, Fig. 1). This depth range encompassed 449 sites (96% of the total available).
More than 380 individual taxa were identified from these surveys but many of these were either operational taxonomic units (OTUs) or species-level names that were often not consistently recorded within and between surveys. In an extensive audit process (see Bowden et al. 2019), the full dataset was aggregated to a set of 139 taxa, spanning a range of taxonomic levels, which were reliably and consistently recorded across all studies and years (Table S1, supplementary materials). A subset of 67 taxa was then extracted from the aggregated taxon list by selecting only those that were observed at five or more survey sites—a minimum number of samples required for the JSDM, see next section for details (Table S1, Supplementary Materials 1 and Table S3).
Explanatory variables
Explanatory variables available for the JSDMs were those selected in previous species distribution models of invertebrate fauna developed for the region and were available at 1 km grid resolution (Anderson et al. 2016; Georgian et al. 2019; Stephenson et al. 2022b) (Table S2, Supplementary Materials 1). The 16 variables selected for consideration in this study describe seafloor characteristics (depth, depth standard deviation, profile curvature and Bathymetric Position Index), water chemistry at the seafloor (dissolved oxygen, salinity, silicate), water physics (temperature residuals, dynamic topography, tidal current speed and sea surface temperature gradient), productivity (Eppley-modified Vertically Generalised Production Model, Carbon-Based Productivity Model-2 and seabed particulate organic carbon flux), and bottom trawl fishing distribution and effort (swept area per 5 × 5 km grid cell for the period 1989 to 2016) (see Table S2 in Supplementary Materials 1 for further details).
Joint species distribution modelling
The faunal data were analysed using the JSDM method Hierarchical Modelling of Species Communities (HMSC), which yields inference both at single-taxon and community levels from a single modelling process (Ovaskainen et al. 2017). HMSC partitions variation in species occurrences or abundances into components that can be interpreted in relation to environmental filtering, species associations, and random processes. That is, in addition to modelling the relationship between biological data and explanatory variables, the HMSC framework can incorporate information on inter-species associations and the spatial context of the sampling design (Fig. 2A).
Fig. 2
Infographic depicting the data used and the key steps in the Joint Species Distribution Modelling (A–C) and the mapping of VME indices (D)
×
With most SDM methods, the inclusion of many explanatory variables should be avoided because they generally only provide minimal improvement in predictive accuracy, complicate interpretation of model outcomes and can lead to over-fitting and loss of generality (Elith et al. 2006). In order to produce a parsimonious and generally-applicable model (i.e., a model with appropriate explanatory or prediction power with as few predictor variables as possible), we firstly removed explanatory variables which had high co-linearity [i.e., ≥ 0.7, as per Dormann et al. (2013)]; the decision of which of the two co-linear variables to retain was based on expert ecological knowledge of the likely influence on faunal distributions. Following this process, variables standard deviation of depth, Bathymetric Position Index—broad, dissolved oxygen and particulate organic carbon export to seabed were removed. A further refinement of predictor variables was undertaken based on selection models with appropriate convergence scores (i.e., mean Gelman–Rubin Potential Scale Reduction Factor < 1.1). That is, models may have poor convergence scores when using environmental variables which do not explain patterns in variation of faunal presence/absence or abundance. Finally, exploration of model fits—focusing on increasing model fits for taxa with low model fit scores and increasing overall model fit across all taxa—was explored in preliminary JSDM models where at least one variable from each of the four explanatory variable categories was used. The decision to retain at least one variable from each of 4 explanatory variable categories was subjective, but based on these categories previously being identified as key drivers of species distributions of invertebrate fauna (Anderson et al. 2016; Georgian et al. 2019; Stephenson et al. 2022b). Seven explanatory variables were selected based on exploration of model fits and expert knowledge for use in final JSDMs (Table S2, Supplementary Materials 1): depth (bathy); tidal current speed (tidcurr); salinity (salinity); temperature residuals (tempres); profile curvature (profcurv); Eppley Primary Productivity (epp); and trawling history (trawl). This set of variables included measures associated with food supply (epp, tidcurr), depth (bathy), water mass (salinity and tempres), seafloor topography (profcurv), and trawling history (trawl), all of which were considered likely to influence distributions of fauna and community assembly processes.
HMSC from the hmsc R package (Tikhonov et al. 2020) was used to fit JSDMs to the seafloor data simultaneously combining information on environmental covariates, random spatial effects and inter-taxa associations in a single model [following guidelines from Ovaskainen and Abrego (2020) and Tikhonov et al. (2020)] (Fig. 2B). A two-part hurdle model was used to model distribution of abundance. In this procedure, a binomial model with a probit distribution was used initially to predict the probability of occurrence, followed by a separate model with a Gaussian distribution to estimate (log(x + 1) transformed) abundance for locations where presence was predicted (referred hereafter as abundance conditional on presence) (Fig. 2B). The outcomes of the presence–absence and abundance conditional on presence models were multiplied together (hurdled) to create an overall estimate of abundance. We refer to these final (hurdled) outputs as abundance. In addition to the explanatory variables (environmental data and trawling history) a random spatial effect (that also models co-occurrence among species) was included at the level of sampling station, using a latent factor approach (Ovaskainen et al. 2016a). The explained variation among the fixed and random effects was partitioned using methods described by Ovaskainen et al. (2017). The model was fitted to the data with Bayesian inference, using the posterior sampling scheme of Tikhonov et al. (2020) with four chains, each with 3750 iterations, of which the first 1250 iterations were removed as burn-in, and the remaining ones were thinned by 10 to yield 250 posterior samples per chain (i.e., a total of 1000 posterior samples across all chains). After model fitting, the MCMC convergence was examined using the potential scale reduction factor (Gelman and Rubin 1992).
Model fit metrics included AUC (area under the curve of the receiver operating characteristic) and R2 (coefficient of determination) for each taxon for the presence–absence and abundance conditional on presence models, respectively. Model fit metrics were calculated by performing a ten-fold cross-validation of randomly sampled withheld data (predictive power). AUC is an effective measure of model performance and a threshold-independent measure of accuracy (Allouche et al. 2006). AUC scores range from 0 to 1, with a score of 0.5 indicating model performance is equal to random chance. R2 measures the proportion of the variance in the dependent variable that is predictable from the independent variables. Overall model performance was assessed by calculating the mean model fits across all taxa for both presence–absence and abundance models.
Finally, the parameter estimates were explored, and spatial predictions and associated uncertainty (standard deviation of the mean) were made for the study area using the ‘constructGradient’ and ‘predict’ functions in the hmsc R package (Fig. 2C). Taxon richness was estimated by summing the individual taxa occurrence predictions (Calabrese et al. 2014) (Fig. 2C).
Mapping VME indices
Adapting the approach developed by Gros et al. (2023), the spatial predictions of abundance from the JSDMs were used to map areas most likely to contain VMEs (Fig. 2D). This approach consisted of: (1) assessing the vulnerability to bottom fishing of all taxa within our dataset that were identified as VME indicator taxa; (2) calculating and mapping an “abundance-based VME index”, using the cumulative abundance of VME indicator taxa weighted by a VME vulnerability score; (3) calculating and mapping a “richness-based VME index”, using the richness of VME indicator taxa weighted by the VME vulnerability score; (4) mapping a “confidence index” which estimates the confidence associated with the spatial predictions of the VME indicator taxa; and (5) identifying the most likely areas in which VMEs might occur, based on the overlap of the highest abundance-based and richness-based VME index scores in areas with the highest model prediction certainty. All R code used for JSDM modelling and mapping VME indices is available on Github (see data availability).
Assigning vulnerability scores to VME indicator taxa
In the first instance, VME indicator taxa were identified from the 67 taxa used in the JSDM models, following definitions from the Conservation and Management Measure for the Management of Bottom Fishing in the High Seas of the South Pacific (SPRFMO 2022). A vulnerability to bottom fishing score for each VME indicator taxon was assigned based on scores calculated for morphotaxa by Gros et al. (2023), which included consideration of six criteria: habitat forming; rare or unique; fragility; life history; larval dispersal and sessility [following Burgos et al. (2020); Morato et al. (2018)], which align with the Food and Agriculture Organization of the United Nations (FAO) criteria for characterising VMEs (FAO 2009). Where our VME indicator taxa contained multiple morphotaxa, we used the mean vulnerability scores for each morphotaxa (vulnerability scores and morphotaxa are available in Table 1 and Table S3, in Supplementary Materials 1).
Table 1
Overview of taxa records and JSDM model fits (ordered alphabetically by taxa, with VME indicator taxa used in this study shaded orange). Number of occurrences, prevalence (occurrences/number of samples), mean abundance (excluding absences), VME indicator taxa vulnerability scores (see Table S3 in Supplementary Material 1 for further detail on morphotypes), tenfold cross-validated AUC scores of presence–absence model and R2 of abundance conditional on presence model. Model fit metrics highlighted in red indicate models with poor model performance (subjectively defined as). (Color table online)
Number of occurrences, prevalence (occurrences/number of samples), mean abundance (excluding absences), VME indicator taxa vulnerability scores (see Table S3 in Supplementary Material 1 for further detail on morphotypes), tenfold cross-validated AUC scores of presence–absence model and R2 of abundance conditional on presence model. Model fit metrics highlighted in red indicate models with poor model performance (subjectively defined as < 0.65 AUC and < 0.05 R2)
The set of VME indicator taxa identified in SPRFMO (2022) includes some high-level taxonomic groupings that encompass morphotypes in our image-based dataset that have different vulnerabilities to fishing. Therefore, we reviewed the source observation data and excluded morphotaxa that were small in height, primarily associated with soft sediment, or were recorded at high densities in areas of intensive trawling (emphasising the “Vulnerable” element for this assessment). Five taxa recorded in the image data that would nominally be grouped as VME indicators were excluded from the VME analysis. (1) The grouping ‘Anemones’ included some large, erect morphotypes that were associated with seamounts and rocky reefs (high vulnerability) but also many small, low-profile morphotypes that occurred in high densities on soft sediments (low vulnerability). (2) Because identification of anemones in the dataset was generally at coarse taxonomic level it was not possible to separate the taxa reliably based on vulnerability, so the entire grouping Anemones, and the morphologically similar group Corallimorpharia, were excluded. (3) A distinctive pennatulacean taxon recorded as ‘Kophobelemnon spp.’ was only a few centimetres high and was recorded in very high densities on soft sediments in areas subject to intensive disturbance from trawl fisheries (low vulnerability). (4) Similarly, the solitary soft coral Taiaroa tauhou is small in size, can retract into the sediment, and occurred at high densities in areas of high fishing disturbance. (5) Finally, a hexactinellid sponge morphotype recorded as Cladorhiszidae was excluded as this is also small in stature and occurred only on soft sediments.
VME indices
For the abundance-based VME index, spatial predictions of abundance for each VME indicator taxon were first multiplied by their vulnerability scores, so that areas with high predicted densities would have higher vulnerability scores than those with low densities. Spatial estimates of taxon-specific vulnerability scores were then summed across all VME indicator taxa to produce an overall abundance-based VME index (Gros et al. 2023). For the richness-based VME index, spatial estimates of each taxon’s probability of occurrence were multiplied by its vulnerability to bottom fishing score and then summed, to produce a richness-based index of vulnerability (Gros et al. 2023). Despite the similarity in these two VME indices, abundance and richness represent distinct and important measures by which VMEs can be identified (Lockhart and Hocevar 2021). For example, in the High Seas of the South Pacific, an encounter with a potential VME can be judged both on the weight of bycatch of certain key VME indicator taxa (i.e., presumably representing areas of high abundance on the seafloor) or based on a lower weight of bycatch of three or more VME indicator taxa (i.e., representing areas of high diversity on the seafloor) (Geange et al. 2020; SPRFMO 2022).
Confidence index
Gros et al. (2023) developed a confidence index based on the quality of imagery used, but in our dataset all imagery was from the same camera system and thus of essentially the same quality (following aggregation of fauna to broader OTUs). We develop a different confidence index, here, by multiplying the spatially explicit uncertainty estimates (standard deviation of the mean abundance) for each VME indicator taxon by its associated VME vulnerability score. Uncertainty estimates for all VME indicator taxa were then summed to give an overall uncertainty index which was then divided by the abundance-based VME index. This approach is akin to calculating the coefficient of variation (CV, also sometimes known as relative standard deviation) of the abundance-based VME index and therefore represents a relative estimate of uncertainty. Here, we use a conservative threshold of 0.33, with a CV > 0.33 indicating moderate to high uncertainty, and therefore focus on areas of VME indices with CV < 0.33 (low uncertainty).
Identifying potential VME locations
In the absence of quantitative thresholds for defining VMEs, areas with the highest abundance-based and richness-based VME indices (subjectively defined as the 95th percentile) were used to identify likely locations of VMEs. Similarly to Gros et al. (2023), the areas of high abundance-based VME index and high richness-based VME index were mapped, along with the confidence index. The subjectively defined 95% percentile threshold aligns with examples of other studies predicting “primary habitat” or “core habitat”, i.e., areas which are most likely to contain the study taxa (Anderson et al. 2022; Stephenson et al. 2023b; e.g., Tong et al. 2013). The overlapping of areas with highest VME index scores (and high model confidence) are more likely to represent locations of VME, or areas of priority for future investigation/confirmation of VME presence.
Results
Model fits
Model fits for presence–absence and abundance models varied by taxon (Table 1). Model fits were reasonable, with a mean cross-validated AUC score of 0.70, but a lower mean cross-validated R2 of 0.11 (these values were near identical when calculated for VME and non-VME indicator taxa). The family Gorgonocephalidae (basket stars) had the lowest cross-validated AUC score (0.45), suggesting predictions for this taxon were no better than would be expected by random chance, whereas the species Enypniastes eximia (swimming sea cucumber) had the highest cross validated AUC score (0.94). Cladorhizidae (family of demosponges) had the lowest cross-validated R2 of − 0.26, (no predictive power), whereas the subclass Echiura (spoon worms) had the highest R2 of 0.49 (Table 1). Of the 67 taxa included in the JSDM models, 17 and 21 taxa were considered to have poor performance for presence–absence and abundance models, respectively (noting that these poorly performing models are predominantly for non-VME indicator taxa used in this study, Table 1).
Parameter estimates
There was strong evidence (95% posterior probability, or higher) of both positive and negative relationships between explanatory variables and taxon occurrence and abundance (see mean posterior values and support in Supplementary Materials 2). Relationships with environmental explanatory variables varied by taxon (see the variance explained by each explanatory variable and by the random spatial effect in Supplementary Materials 2) but there were general trends for both taxon occurrence and abundance to be negatively related to increasing depth (bathy) and decreasing salinity (salinity), and positively related to increasing profile curvature of the seabed (profcurv) and primary productivity (epp_mean). Trawl history (trawl) was negatively associated with the occurrence and abundance of many VME indicator taxa (e.g., Goniocorella dumosa, Demospongiae, Antipatharia, Scleractinia, Hydrozoa) but in some cases positively associated with occurrence of non-VME indicator taxa (e.g., Echinothurioida, Gastropoda, Buccinidae) (Supplementary Materials 2).
Two broad groups of pairwise taxon associations were observed when using presence–absence data, primarily split between taxa found in soft-sediment and on hard substrate (Supplementary Materials 2). Primarily soft sediment taxa such as Holothuroidea, M. challengeri, Anemones, and Pennatulacea (among others) had a strong positive association with each other (i.e., co-occurrence when accounting for the environmental niche) and a negative association with other taxa associated with hard substrates such as Goniocorella dumosa, Scleractinia (Enallopsammia spp., Madrepora spp., Solenosmillia spp.), Antipatharia and Demospongiae (amongst others). There was also strong support for positive association among a large number of other taxa which may represent several distinct communities, but which are not obviously apparent in the taxon-to-taxon association matrices (for details see Supplementary Materials 2).
Spatial predictions of taxon richness and abundance
Spatial predictions of probability of occurrence, abundance (individuals per km2), and associated uncertainty (standard deviation) varied by taxon (see Supplementary Material 3 for all predictions) but there were key areas of overlap in the distribution of taxa, which are reflected in the predicted patterns of taxon richness. The highest predicted taxon richness occurs in depths from 400 to 800 m on the northern and southern flanks on the western part of Chatham Rise and adjacent areas of the continental shelf break and slope (Fig. 3). There were also discrete (relatively small) areas of high richness areas of the continental shelf break and slope across Campbell Plateau and just off the coast of Fiordland (western part of the study area—Fig. 3).
Fig. 3
Predicted distribution of: A Taxon richness; and B associated uncertainty (standard deviation) estimated from occurrence predictions of the joint species distribution model using 67 taxa
×
Predicted spatial patterns of abundance for Goniocorella dumosa (Fig. 4) and Hexactinellid sponges (Fig. 5) are presented here as example taxa because they are considered key habitat-forming VME indicator taxa in the study area (and based on observations from the DTIS imagery) (Tracey et al. 2011). Many of the habitat-forming taxa associated with VMEs had restricted distributions, with low to moderate probability of occurrence and abundance over most of the study area and concentrated areas of high probability of occurrence and abundance, primarily along steep slopes and on seamounts (e.g., Figs. 4 and 5, but also see Supplementary Materials 2).
Fig. 4
Predictions from joint species distribution models. Predicted abundance of Goniocorella dumosa (individuals per km2). Inset maps: A North East Solander Trough, B North West Bounty Trough, C Graveyard Knolls (centre-north Chatham Rise), D Andes Knolls (South East Chatham Rise), E Urry Knolls (South West Chatham Rise)
Fig. 5
Prediction from joint species distribution models. Predicted abundance of Hexactinellid sponges (individuals per km2). Inset maps: A North East Solander Trough, B North West Bounty Trough, C Graveyard Knolls (centre-north Chatham Rise), D Andes Knolls (South East Chatham Rise), E Urry Knolls (South West Chatham Rise)
×
×
The highest abundances for Goniocorella dumosa were predicted on seamounts and areas of steep slopes across the study area (e.g., North East Solander Trough, North West Bounty Trough, Graveyard Seamounts (centre-north Chatham Rise), South East Chatham Rise, South West Chatham Rise, Fig. 4), with moderate abundance predicted across central parts of Chatham Rise and on deeper parts of the continental shelf, especially in northern parts of the study area (Fig. 4). Abundances were predicted to be low in most parts of the study area (Fig. 4). Predictions of Hexactinellid sponge abundance were low across most of the study area and highest on seamounts and areas of steep slopes (Fig. 5). However, in contrast to Goniocorella dumosa, areas of moderate abundance were restricted to very deep parts of the study area (Fig. 5), noting these areas also have the highest model uncertainty (e.g., see Supplementary Materials 2).
Spatial predictions of VME indices and potential VME locations
Values of both the abundance-based and the richness-based VME indices were predicted to be low across most of the study area (approx. 1.17 million km2, yellow in Fig. 6), with the threshold upper values (95th percentile) having restricted distributions that differed between the two indices (Fig. 6, and see detail of abundance-based and the richness-based VME indices in Fig. S2, in Supplementary Materials 1). The abundance-based VME index was highest on the shelf break, shallower banks, and around offshore islands, as well as on seamount complexes throughout the study area (approx. 59,000 km2, blue in Fig. 6). The richness-based VME index was highest on slopes, particularly in the deeper waters on the northern and southern flanks of Chatham Rise (approx. 59,000 km2, green in Fig. 6).
Fig. 6
Spatial prediction of VME indices (following methods adapted from, and colour scheme used by Gros et al. (2023). The cells are colour coded based on the level of VME indices (abundance-based VME index and richness-based VME index) thresholded at the top 95 percentile (see Fig. S2 in Supplementary Materials 1). Hashed areas represent low–moderate confidence based on a Coefficient of Variation (CV) > 0.33 of the abundance predictions for VME indicator taxa; clear areas represent low uncertainty. Inset maps: A North East Solander Trough, B North West Bounty Trough, C Graveyard Knolls (centre-north Chatham Rise), D Andes Knolls (South East Chatham Rise), E Urry Knolls (South West Chatham Rise)
×
There were numerous areas of small spatial extent where the highest predicted values of both abundance-based and the richness-based VME indices coincided, and thus where VMEs are most likely to occur (approx. 5500 km2, or 0.4% of the study area, red in Fig. 6, and see inset maps). Most of the areas where the highest predicted values of the two VME indices coincided were also in areas with low model uncertainty (i.e., no hashing in Fig. 6) providing greater confidence that these areas represent the most likely areas where VMEs may occur.
Discussion
Using a spatially extensive dataset of faunal abundance in areas around New Zealand we developed spatial predictions of distribution for 67 taxa using recently developed JSDMs. The resulting predictions were obtained from a joint model (i.e., each taxa is predicted simultaneously rather than sequentially, Wilkinson et al. 2021), therefore allowing the exploration of co-occurrence patterns and providing credible estimates of taxon richness (Ovaskainen et al. 2016a; Zhang et al. 2019). Most importantly, the spatial predictions of abundance for a large number of taxa are an advance on previous work in the region, which has either consisted of spatial predictions of abundance for a limited number of taxa across relatively small areas (e.g., Rowden et al. 2020) or are predictions of probability of occurrence, or habitat suitability (Stephenson et al. 2021, 2023a). Probability of occurrence or habitat suitability only indicates the likelihood of a taxon occurring in a particular area but does not necessarily provide information on relative abundance; in many cases, the highest abundances are found in smaller, more localised areas than the wider occurrence predictions (e.g., Rullens et al. 2021). This lack of direct information for abundance has important implications for conservation planning because the more extensive area identified from the occurrence models may not have the highest abundances and therefore may not be the best areas for conservation (Stephenson et al. 2022a). In addition, in the case of habitat formers (e.g., as several of the VME indicator taxa are in this study) if abundance information is not used it means there is no spatial information about the locations of what might be classified as actual VMEs. The presence of a coral VME indicator taxa does not indicate that there is a coral reef VME at that location (Howell et al. 2011; Rowden et al. 2017). Here we provide abundance estimates for 67 taxa, including for several habitat-forming taxa considered VME indicator taxa. We also, for the first time, explore how these abundance data can be directly related to one or more of the FAO (2009) functional definitions of a VME (e.g., as implemented by Gros et al. 2023) to highlight important areas which are most likely to contain VMEs and may be at most risk from the impact of bottom trawling. Information of this nature is of critical importance for effective spatial management that aims to prevent or mitigate significant adverse impacts to VMEs (Gros et al. 2022).
Critical appraisal of JSDM
Given that JSDMs can explain spatial variation in community composition via inclusion of a spatial latent factor, they show promise as an improvement to generic niche models for a variety of applications and have been shown to have some of the highest predictive power compared to other commonly used SDM approaches (Norberg et al. 2019; Zhang et al. 2020b). Inclusion of inter-species associations means that aspects of community ecological theory are implicit in JSDM predictions to inform spatial planning to identify priority areas for protection. For example, we would expect that species with mutualistic associations would have positive residual correlation, given their co-occurrence (Zurell et al. 2018). Therefore, priority areas for protection inferred from JSDM spatial predictions should better identify overlapping areas for community-level protection (via positive residual correlations), for both conservation targets and the interspecies relationships they depend on. In contrast, negative residual correlations between species will infer where differences lie. The ability to consider these interspecies relationships (differences and similarities) will empower conservation planners and reduce the likelihood of conservation conflicts (Inoue et al. 2017).
JSDMs also provide the opportunity to perform conditional joint predictions (i.e., predicting species’ occurrence or abundance given the known occurrence or abundance of other species, Ovaskainen and Abrego 2020; Zhang et al. 2020a). This approach was not undertaken in the present study but may provide gains in predictive power, particularly for rare taxa, and may be especially useful in accounting for (assumed) biotic interactions from the co-occurrence matrix output from JSDM (Roberts et al. 2022; Zhang et al. 2019, 2020a). The availability of conditional joint predictions shows promise when predicting distributions under future conditions due to climate change because it may allow environmental preferences of species and their interactions to be better captured than simply predicting each species separately under future conditions (Stephenson et al. 2022a; Zhang et al. 2020b). This capability will be particularly relevant for New Zealand because the region is predicted to be a hotspot of climate change-related impacts (Law et al. 2018; Rickard et al. 2016).
Conditional joint predictions may also facilitate the identification of community assemblages. Further work to develop working definitions of VMEs (consider the community assemblages) for the New Zealand regions would be of interest. However, care must be taken when interpreting the inter-taxa co-occurrence matrices from JSDM as they may not always represent biotic interactions but can instead reflect other spatial processes not accounted for in the model (i.e., missing important co-variates) (Ovaskainen et al. 2016a; Poggiato et al. 2021).
Fishing impacts and identification of VMEs
Many benthic taxa, particularly deep-sea taxa, are vulnerable to the direct and indirect impacts of fishing, in particular, bottom-contact trawling (e.g., Reed et al. 2007). Several taxa in our study, identified as VME indicator taxa, were found to have negative relationships between taxa occurrence and abundance with bottom trawl fishing distribution and effort in the JSDM models. Similarly to other studies, we observed that these relationships varied based on biological traits (i.e., vulnerability), and the spatial overlap between taxa and the distribution of fishing (Clark et al. 2016; Goode et al. 2020; Roberts and Hirshfield 2004).
The effect of anthropogenic stressors on species’ distributions are rarely accounted for in species distribution modelling (Elith and Leathwick 2009), yet predictions that incorporate historic and current impacts are likely to represent more realistic (reduced or expanded) estimates of current distribution (Bowden et al. 2021). For example, in our study, the erect branching coral Goniocorella dumosa was negatively associated with fishing effort and therefore the predicted distribution or densities were reduced compared to a prediction using environmental estimates alone (the latter was not presented in this study). In contrast, the predatory scavenging whelk taxon Buccinidae was positively associated with fishing effort distributions, with densities predicted to be higher compared to predictions using environmental estimates alone. Given the potential for altered taxa distributions resulting from the inclusion of anthropogenic impacts, it is important to use impact-adjusted species distribution or habitat suitability layers in spatial planning processes (Moilanen et al. 2011; Stephenson et al. 2023b). Taking this approach will avoid the possibility of designing ineffective conservation measures by protecting areas which previously had high abundances, but which may no longer be of high conservation value due to an anthropogenic impact (e.g., in the case of bottom trawling distribution and effort; Rowden et al. 2019). Nevertheless, previously high abundance but impacted areas could also be considered for their value as areas which may support recovery (Baco et al. 2019); these areas should especially be considered in cases where there are few pristine areas within a species range.
Bottom trawl fisheries are sometimes focused where benthic taxa are particularly abundant or where VMEs are present (e.g., seamounts) and the impact of bottom fisheries can be profound for these seabed ecosystems (e.g., Baco et al. 2020; Clark et al. 2016; FAO 2009; Goode et al. 2020). In line with this issue, many nation states have recognised the threat posed to VMEs by bottom-contact trawling and have sought to identify and protect these ecosystems (Morato et al. 2010), including New Zealand. Here, we identify areas that are most likely to contain VMEs in part of the New Zealand EEZ where bottom trawling occurs (acknowledging that these areas are what is predicted to remain since bottom trawling distribution and effort are incorporated as a predictor in the models for VME indicator taxa). Given these already potentially reduced distributions, the estimates presented here of where and how much potential VME area occurs could be used as relevant and up-to-date information for assessing current marine protected areas, which were designed in part to protect VMEs. For example, the prediction maps could be used to inform modification of the boundaries of current Seamount Closure Areas and Benthic Protection Areas (Brodie and Clark 2003; Helson et al. 2010).
Some of the areas with high VME indices identified by this study, such as the Graveyard and Andes seamount complexes, have been well studied and VMEs in the form of coral reefs are known to occur on these seamounts (Bowden et al. 2019) supporting the contention that high value indices likely indicate the presence of VMEs. But other areas, such as the north-east Solander Trough and the north-west Bounty Trough have not been well studied, and thus present opportunities to independently assess the usefulness of the spatial predictions and VME indices method. Furthermore, Gros et al. (2023) noted that the determination and categorisation of the VME indices into two relative (not absolute) levels (low versus high) was not ideal, and that future studies should “determine and validate how to reliably identify priority sites from the gradient of VME index values”. Therefore, we recommend that future sampling and analyses of underwater camera imagery from the New Zealand EEZ undertake further exploration of the usefulness of the approach for identifying VMEs demonstrated here. In particular, to identify ecologically justifiable thresholds for identifying VMEs (rather than the arbitrary threshold used here) for conservation and management purposes.
Following development of JSDMs for benthic taxa for the entire New Zealand EEZ, and on the completion of the further sampling and analyses suggested above, it may be possible to quantitatively incorporate robust predictions of VMEs into future spatial planning efforts in the region. Ideally, this planning would also include other anthropogenic threats to the biodiversity supported by VMEs, including climate change effects (e.g., as in Anderson et al. 2022). Achieving these steps would provide a wealth of additional information on seafloor communities and habitats for use in future spatial planning efforts.
Acknowledgements
We thank Mary Livingston at Fisheries New Zealand for supporting this work and project governance at Fisheries New Zealand. Model development work was funded under Objectives 1 and 3 of Fisheries New Zealand project ZBD2019-01 and the writing of this manuscript was supported by: National Institute of Water and Atmospheric Research Coast and Oceans SSIF funding (projects COME2201 & CEME2303) and Structure and Function of Marine Ecosystems (OCES2301), and the Sustainable Seas – National Science Challenge project ‘Communicating risk and uncertainty to aid decision making’ (C01X1901).
Declarations
Competing interests
The authors have no relevant financial or non-financial interests to disclose.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Using joint species distribution modelling to predict distributions of seafloor taxa and identify vulnerable marine ecosystems in New Zealand waters
Authors
Fabrice Stephenson David A. Bowden Ashley A. Rowden Owen F. Anderson Malcolm R. Clark Matthew Bennion Brittany Finucci Matt H. Pinkerton Savannah Goode Caroline Chin Niki Davey Alan Hart Rob Stewart
