Modeling the Distribution of Habitat-Forming, Deep-Sea Sponges in the Barents Sea: The Value of Data

Abstract

The use of species occurrence as a proxy for habitat type is widespread, probably because it allows the use of species distribution modeling (SDM) to cost-effectively map the distribution of e.g., vulnerable marine ecosystems. We have modeled the distribution of epibenthic megafaunal taxa typical of soft-bottom, Deep-Sea Sponge Aggregations (DSSAs), i.e., “indicators,” to discover where in the Barents Sea region this habitat is likely to occur. The following taxa were collectively modeled: Hexadella cf. dedritifera, Geodia spp., Steletta sp., Stryphnus sp. The data were extracted from MarVid, the video database for the Marine AREAl database for NOrwegian waters (MAREANO). We ask whether modeling density data may be more beneficial than presence/absence data, and whether using this list of indicator species is enough to locate the target habitat. We use conditional inference forests to make predictions of probability of presence of any of the target sponges, and total density of all target sponges, for an area covering a large portion of the Norwegian Barents Sea and well beyond the data’s spatial range. The density models explain <31% of the variance, and the probability models have high classificatory power (AUC > 0.88), depending on the variables/samples used to train the model. The predicted surfaces were then classified on the basis of a probability threshold (0.75) and a density threshold (13 n/100 m2) to obtain polygons of “core area” and “hotspots” respectively (zones). The DSSA core area comprises two main regions: the Egga shelf break/Tromsøflaket area, and the shelf break southwest of Røst bank in the Træna trench. Four hotspots are detected within this core area. Zones are evaluated in the light of whole-community data which have been summarized as taxon richness and density of all megafauna. Total megafaunal density was significantly higher inside the hotspots relative to the background. Richness was not different between zones. Hotspots appeared different to one another in their richness and species composition although no tests were possible. We make the case that the effectiveness of the indicator species approach for conservation planning rests on the availability of density data on the target species, and data on co-occurring species.

Environmental matching reveals non-uniform range-shift patterns in benthic marine Crustacea

“…Moreover, in models constructed with the inclusion of depth (63 shallow-water species; 12 deep-sea species; see Appendix S1 Table S1.3), depth was recovered as most relevant for both groups, followed by temperature and salinity. This aligns well with the findings of other studies (e.g., Pearman et al, 2020;Gonzalez-Mirelis et al 2021), which have indicated that depth along with temperature and salinity are relevant for defining benthic species distributions. It is relevant to note that, previous studies have highlighted the relevance of oxygenation, pH and food supply (or POC flux) in the maintenance of the ecosystem functioning (Sweetman et al 2017).…”

Environmental and Geomorphological Effects on the Distribution of Deep-Sea Canyon and Seamount Communities in the Northwest Atlantic

“…Using biological abundance data, rather than presence/absence data, retains information about abundances within spatiallyexplicit segments observed in this study. This makes the community-environment relationship model more sensitive to variation among communities (Cushman and McGarigal, 2004) which was recently exemplified in a study that compared the usefulness of presence/absence and species abundance data for determination of areas that may require management attention (Gonzalez-Mirelis et al, 2021). They found presence/absence to be insufficient for applied management as modeled presence/absence data highlighted areas that were either too large or too small for their purposes.…”

“…Further, many studies obtain presence/absence data either to establish a species inventory or due to the difficulties in obtaining accurate counts of organisms per unit area (Hargrave et al, 2004;Mortensen and Buhl-Mortensen, 2005), which limits the usefulness of the data for detecting areas of high abundances or biodiversity for management applications (Gonzalez-Mirelis et al, 2021). Using species abundance data can provide a more comprehensive representation of benthic communities that can be used to improve models designed to understand species distribution patterns under a changing climate (Morato et al, 2020) or improve species distribution models.…”

Using Spatial Validity and Uncertainty Metrics to Determine the Relative Suitability of Alternative Suites of Oceanographic Data for Seabed Biotope Prediction. A Case Study from the Barents Sea, Norway

“…The most recent maps [11] incorporate near-bottom oceanographic predictor variables (temperature, salinity, currents), and highlight how important such variables are in shaping biotope distribution. Similarly, early MAREANO models of vulnerable biotopes [12], a sister product to the general biotope maps, did not use oceanographic data but later updates have benefitted considerably from their inclusion [13].…”