Abstract
Conserving biogeographic regions with especially high biodiversity, known as biodiversity ‘hotspots’, is intuitive because finite resources can be focussed towards manageable units. Yet, biodiversity, environmental conditions and their relationship are more complex with multidimensional properties. Assessments which ignore this risk failing to detect change, identify its direction or gauge the scale of appropriate intervention. Conflicting concepts which assume assemblages as either sharply delineated communities or loosely collected species have also hampered progress in the way we assess and conserve biodiversity. We focus on the marine benthos where delineating manageable areas for conservation is an attractive prospect because it holds most marine species and constitutes the largest single ecosystem on earth by area. Using two large UK marine benthic faunal datasets, we present a spatially gridded data sampling design to account for survey effects which would otherwise be the principal drivers of diversity estimates. We then assess γ‐diversity (regional richness) with diversity partitioned between α (local richness) and β (dissimilarity), and their change in relation to covariates to test whether defining and conserving biodiversity hotspots is an effective conservation strategy in light of the prevailing forces structuring those assemblages. α‐, β‐ and γ‐diversity hotspots were largely inconsistent with each metric relating uniquely to the covariates, and loosely collected species generally prevailed with relatively few distinct assemblages. Hotspots could therefore be an unreliable means to direct conservation efforts if based on only a component part of diversity. When assessed alongside environmental gradients, α‐, β‐ and γ‐diversity provide a multidimensional but still intuitive perspective of biodiversity change that can direct conservation towards key drivers and the appropriate scale for intervention. Our study also highlights possible temporal declines in species richness over 30 years and thus the need for future integrated monitoring to reveal the causal drivers of biodiversity change.
Highlights
In light of the global biodiversity crisis (Butchart et al, 2010; Johnson et al, 2017; Loh et al, 2005; McRae et al, 2017), robust information on the spatial distribution of biodiversity and anthropogenic drivers of change are critical to direct conservation efforts towards the most effective conservation interventions (Edgar et al, 2016; McGill et al, 2015; Pimm et al, 2014)
The superorganism view is implicit in maps with hard edges between adjacent habitats and is widely applied to inform biodiversity conservation and monitoring strategies (e.g. EUNIS; Andersen et al, 2018; Coltman et al, 2008), such classifications can perform poorly when related to changes in community composition (Cooper et al, 2019)
We focus on two large faunal datasets and observations between 1985 and 2016 where complementary covariate data exist: the first was compiled for a UK benthic macrofaunal distribution study ( 'BM data'; Cooper & Barry, 2017) containing count data for 1,964 species with 23,153 multi-species observations; the second was downloaded from the largest publicly available online database documenting the global distribution of marine species, Ocean Biogeographic Information System (OBIS, 2020), which contained 3,187 benthic species with a mixture of count and incidence data with 23,646 multi-species observations ( 'OBIS data’)
Summary
In light of the global biodiversity crisis (Butchart et al, 2010; Johnson et al, 2017; Loh et al, 2005; McRae et al, 2017), robust information on the spatial distribution of biodiversity and anthropogenic drivers of change are critical to direct conservation efforts towards the most effective conservation interventions (Edgar et al, 2016; McGill et al, 2015; Pimm et al, 2014). Data relevant for estimating species distributions are often derived from museum collections or from surveys conducted for other reasons, including establishing protected areas or environmental impact assessment (Cooper & Barry, 2017; Engemann et al, 2015; Norman & White, 2019) As such, they may not be best suited to providing evidence of the underlying reasons for observed changes at large scales (Arkema et al, 2006; Dickey-Collas, 2014; Kupschus et al, 2016). This information will be valuable in highlighting current knowledge gaps, chief drivers of biodiversity change and the appropriate scales for assessment and conservation to operate
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