Modeling demersal fish and benthic invertebrate assemblages in support of marine conservation planning

Abstract

Marine classification schemes based on abiotic surrogates often inform regional marine conservation planning in lieu of detailed biological data. However, these schemes may poorly represent ecologically relevant biological patterns required for effective design and management strategies. We used a community-level modeling approach to characterize and delineate representative mesoscale (tens to thousands of kilometers) assemblages of demersal fish and benthic invertebrates in the Northwest Atlantic. Hierarchical clustering of species occurrence data from four regional annual multispecies trawl surveys revealed three to six groupings (predominant assemblage types) in each survey region, broadly associated with geomorphic and oceanographic features. Indicator analyses identified 3-34 emblematic taxa of each assemblage type. Random forest classifications accurately predicted assemblage distributions from environmental covariates (AUC > 0.95) and identified thermal limits (annual minimum and maximum bottom temperatures) as important predictors of distribution in each region. Using forecasted oceanographic conditions for the year 2075 and a regional classification model, we projected assemblage distributions in the southernmost bioregion (Scotian Shelf-Bay of Fundy) under a high emissions climate scenario (RCP 8.5). Range expansions to the northeast are projected for assemblages associated with warmer and shallower waters of the Western Scotian Shelf over the 21st century as thermal habitat on the relatively cooler Eastern Scotian Shelf becomes more favorable. Community-level modeling provides a biotic-informed approach for identifying broadscale ecological structure required for the design and management of ecologically coherent, representative, well-connected networks of Marine Protected Areas. When combined with oceanographic forecasts, this modeling approach provides a spatial tool for assessing sensitivity and resilience to climate change, which can improve conservation planning, monitoring, and adaptive management.

Keywords: climate change adaptation; community-level modeling; conservation planning; ecological coherence; ocean warming; representativity.

FIGURE 1

Study domain of four regions of the Northwest Atlantic: Northern Gulf of St. Lawrence (NGSL), Southern Gulf of St. Lawrence (SGSL), Newfoundland and Labrador (NL), and Maritimes (MAR). Shaded area indicates limits of regional multispecies bottom trawl surveys. Points are locations of individual trawl sets from 2007 to 2017. Surveys in the Northern and Southern Gulf of St. Lawrence partially overlap on the southern boundary of the Laurentian Trough

FIGURE 2

Dendrograms indicating similarity (calculated from Simpson dissimilarity) in taxonomic composition of bottom‐associated fish and benthic invertebrates between sites from annual multispecies trawl surveys in each of four regions of the Northwest Atlantic: NGSL, SGSL, NL, MAR (see Figure 1 caption for regional abbreviations). Nodes on lower branches indicate greater similarity between sites within that cluster. Colored groupings identify major clusters of sites sharing similar assemblage types (BoF, Bay of Fundy; ESS, Eastern Scotian Shelf; WSS, Western Scotian Shelf)

FIGURE 3

Predicted distribution of predominant assemblage types of bottom‐associated fish and benthic invertebrates in each of four regions of the Northwest Atlantic: NGSL, SGSL, NL, and MAR (see Figure 1 caption for regional abbreviations). Color denotes the expected assemblage type in a given 4‐km grid cell based on relationships with environmental correlates delineated by random forest classification (BoF, Bay of Fundy; ESS, Eastern Scotian Shelf; WSS, Western Scotian Shelf). Grid cells outlined in black indicate greater uncertainty in the model prediction (probability of assignment <0.70)

FIGURE 4

Variable importance plots indicating mean decrease in whole model accuracy resulting from randomly permuting values of environmental predictors among observations used in the four regional random forest classifiers: NGSL, SGSL, NL, and MAR (see Figure 1 caption for regional abbreviations). Variable summaries with prefix “Avg” indicate averages across years with available data. Abbreviations are SST, sea surface temperature; DO, dissolved oxygen; MLD, mixed layer depth; BPI, bathymetric position index; NS, north‐south; EW, east‐west; chl, chlorophyll; PP, primary production. Refer to Appendix S1: Table S4 for details and data sources for individual variables

FIGURE 5

Comparison of number of sites in the Maritimes region grouping with each of the six predominant assemblage types (indicated by color hue) between two time periods (darker shading, 2007–2011; lighter shading, 2012–2016) for sites sampled for taxonomic composition in both time periods (n = 238)

FIGURE 6

(a) Predicted relative change in area encompassed by spatial distributions of the six predominant assemblage types in Maritimes region in 2075 with bottom temperature and salinity conditions projected under RCP 8.5. Data are difference in area between 2075 and present expressed as percentage of present area. (b) Predicted 2075 assemblage distributions. Assemblage types are identified by color hue. Regions with darker shading and blue outline indicate areas susceptible to change under projected warming (i.e., predicted classification in 2075 differs from present)