Details

Autor(en) / Beteiligte

• Nitschke, Josh
• Baring, Ryan
• Kar, Fonti
• Dittmann, Sabine

Titel

Deriving biomass conversion factors for southern temperate benthic fauna; analytical methods matter

Ist Teil von

• Estuarine, coastal and shelf science, 2024-08, Vol.303, p.108794, Article 108794

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Biomass is a fundamental ecological measure used to quantify ecosystem structure and function. It is costly to measure but can be estimated using conversion factors based on established relationships between different biomass measures, however such conversion factors may not be applicable across broad geographic regions. For temperate benthic fauna, currently available mass-to-mass conversion factors were largely established in Northern Hemisphere ecosystems. Here, we present conversion factors for temperate Australian benthic fauna typically found in microtidal coastal bays and estuaries (14 taxa encompassing five classes: Polychaeta; Bivalvia; Gastropoda; Crustacea; Insecta). We derived 126 conversion factors using two statistical techniques between several mass measures (e.g. wet mass, dry mass, ash-free dry mass, shell-free masses) with specimens from three preservation methods (ethanol, formalin, frozen). Our conversion factors were generally not congruent with those obtained from the literature. We also found that the choice of prediction method used to derive conversion factors can be a substantial source of error in biomass estimation that has largely been overlooked in mass-to-mass conversion. Log-linear ordinary least squares regression models provided substantially more accurate predictions than simple mean ratios. We outline an analytical approach for future use to derive more globally comparable conversion factors for estuarine benthic fauna. •First biomass conversion factors for benthic fauna in southern temperate estuaries.•Choice of prediction method is a substantial source of error in biomass estimation.•Log-linear ordinary least squares regression models gave most accurate predictions.•Propose analytical approach for developing conversion factors.