The challenge of scaling up from individual physiology to population level effects:: Using the Dynamic Energy Budget to describe and predict crustacean responses to climate variability

Abstract

Predicting how marine communities will be affected by environmental change is one of the most significant challenges facing researchers today. In order to tackle this challenge, a mechanistic understanding of climate impacts at the individual level is necessary, as variations in species physiological responses are often reflected in patterns at higher organisational levels such as populations and communities. In order to explore the relationship between individual physiology and higher-level dynamics more fully, the swimming crab Liocarcinus depurator (Linnaeus, 1758) was selected as a model species for experimental work in which whole organism responses (growth, respiration and allocation to reproduction) to climate drivers were investigated using a bio-energetic modelling approach. This species was selected as a model organism after analysis of epibenthic time-series from the Western English Channel monitoring Station L4 revealed that decapod crustaceans played a key role in structuring the benthic community, and that L. depurator was one of the most dominant species in the area, in terms of both abundance and biomass. A bio-energetic approach was used as the same time-series analysis identified water temperature and seasonal phytodetrital input (e.g. food) as the predominant drivers of variation in benthic community wet biomass at L4, with the two drivers appearing to primarily influence community biomass at different times of the year. It is possible that warmer water temperatures in the autumn trigger gonad development and a consequent increase in reproductive biomass, while the sedimentation of the spring phytoplankton bloom drives an increase in somatic biomass. This time-series analysis clearly highlighted the role of organism energetics, and the environmental conditions that influence energy allocation, in structuring benthic communities. Further work elucidated the relationship between environmental variables and individual energy budgets. L. depurator responses to climate drivers (temperature, hypoxia and ocean acidification) were tested experimentally, and a mechanistic Dynamic Energy Budget (DEB) model was parameterised to describe the life history characteristics of crustaceans. At an individual level the model was able to accurately describe and predict observed responses to environmental drivers, both in isolation and in multiple stressor scenarios. Experimental results suggested that L. depurator was broadly tolerant of those climate drivers tested in the short term. Over the longer term however, model scenarios suggested that OA and the combined stressors may have an adverse effect on growth. When the multi-stressor model was forced with environmental projections from a coupled hydrodynamic-biogeochemical model (NEMO-ERSEM), it could be used to make predictions regarding ultimate carbon mass, age-at-maturity and cumulative allocation to reproduction, which were used to infer possible population level effects such as species distributions and population viability. Model scenarios suggested that, in the future, the optimum settlement time for juvenile L. depurator would shift forward across the north-west European shelf, and that this crustacean species may be able to expand its range further into the northern North Sea. The DEB model presented here can provide a mechanistic underpinning of observed species responses to climate drivers, and more broadly, the thesis demonstrates how multi-stressor models can be built from data collected in single stressor experiments, thereby providing a way of synthesising single stressor data into a modelling environment. This approach allows us to simulate more complex, ecologically relevant conditions. At a broader scale, the coupled DEB-ERSEM model showed that it can provide insight into why changes in species’ distributions are predicted, as these distributions are an emergent property of the processes being modelled.

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ORCID for Christopher Hauton: orcid.org/0000-0002-2313-4226

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