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Identifying taxonomic and functional surrogates for spring biodiversity conservation
Author(s) -
Jyväsjärvi Jussi,
Virtanen Risto,
Ilmonen Jari,
Paasivirta Lauri,
Muotka Timo
Publication year - 2018
Publication title -
conservation biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.2
H-Index - 222
eISSN - 1523-1739
pISSN - 0888-8892
DOI - 10.1111/cobi.13101
Subject(s) - taxonomic rank , biodiversity , taxon , ecology , bryophyte , biology , selection (genetic algorithm) , geography , machine learning , computer science
Surrogate approaches are widely used to estimate overall taxonomic diversity for conservation planning. Surrogate taxa are frequently selected based on rarity or charisma, whereas selection through statistical modeling has been applied rarely. We used boosted‐regression‐tree models (BRT) fitted to biological data from 165 springs to identify bryophyte and invertebrate surrogates for taxonomic and functional diversity of boreal springs. We focused on these 2 groups because they are well known and abundant in most boreal springs. The best indicators of taxonomic versus functional diversity differed. The bryophyte Bryum weigelii and the chironomid larva Paratrichocladius skirwithensis best indicated taxonomic diversity, whereas the isopod Asellus aquaticus and the chironomid Macropelopia spp. were the best surrogates of functional diversity. In a scoring algorithm for priority‐site selection, taxonomic surrogates performed only slightly better than random selection for all spring‐dwelling taxa, but they were very effective in representing spring specialists, providing a distinct improvement over random solutions. However, the surrogates for taxonomic diversity represented functional diversity poorly and vice versa. When combined with cross‐taxon complementarity analyses, surrogate selection based on statistical modeling provides a promising approach for identifying groundwater‐dependent ecosystems of special conservation value, a key requirement of the EU Water Framework Directive.