Variation among northeast Atlantic regions in the responses of zooplankton to climate change: Not all areas follow the same path

Broad scale climate forcing can interact with local environmental processes to affect the observed ecological phenomena. This causes potential problems of over-extrapolation for results from a limited number of sites or the averaging out of region-specific responses if data from too wide an area are combined. In this study, an area similar in extent to the Celtic Biscay Large Marine Ecosystem, but including off-shelf areas, was partitioned using clustering of satellite chlorophyll (chl-a) measurements. The resulting clusters were used to define areas over which to combine copepod data from the Continuous Plankton Recorder. Following filtering due to data limitations, nine regions were defined with sufficient records for analysis. These regions were consistent with known oceanographic structure in the study area. Off-shelf regions showed a progressively later timing in the seasonal peak of chl-a measurements moving northwards. Generalised additive models were used to estimate seasonal and multiannual signals in the adult and juvenile stages of Calanus finmarchicus, C. helgolandicus and the Paracalanus–Pseudocalanus group. Associations between variables (sea surface temperature (SST), phenology and annual abundance) differed among taxonomic groups, but even within taxonomic groups, relationships were not consistent across regions. For example, in the deep waters off Spain and Portugal the annual abundance of Calanus finmarchicus has a weak positive association with SST, in contrast to the pattern in most other regions. The regions defined in this study provide an objective basis for investigations into the long term dynamics of plankton populations and suggest suitable sub regions for deriving pelagic system indicators.

A new approach for objective identification of turns and steps in organism movement data relevant to random walk modelling

1. A first step in the analysis of complex movement data often involves discretisation of the path into a series of step-lengths and turns, for example in the analysis of specialised random walks, such as Lévy flights. However, the identification of turning points, and therefore step-lengths, in a tortuous path is dependent on ad-hoc parameter choices. Consequently, studies testing for movement patterns in these data, such as Lévy flights, have generated debate. However, studies focusing on one-dimensional (1D) data, as in the vertical displacements of marine pelagic predators, where turning points can be identified unambiguously have provided strong support for Lévy flight movement patterns. 2. Here, we investigate how step-length distributions in 3D movement patterns would be interpreted by tags recording in 1D (i.e. depth) and demonstrate the dimensional symmetry previously shown mathematically for Lévy-flight movements. We test the veracity of this symmetry by simulating several measurement errors common in empirical datasets and find Lévy patterns and exponents to be robust to low-quality movement data. 3. We then consider exponential and composite Brownian random walks and show that these also project into 1D with sufficient symmetry to be clearly identifiable as such. 4. By extending the symmetry paradigm, we propose a new methodology for step-length identification in 2D or 3D movement data. The methodology is successfully demonstrated in a re-analysis of wandering albatross Global Positioning System (GPS) location data previously analysed using a complex methodology to determine bird-landing locations as turning points in a Lévy walk. For this high-resolution GPS data, we show that there is strong evidence for albatross foraging patterns approximated by truncated Lévy flights spanning over 3·5 orders of magnitude. 5. Our simple methodology and freely available software can be used with any 2D or 3D movement data at any scale or resolution and are robust to common empirical measurement errors. The method should find wide applicability in the field of movement ecology spanning the study of motile cells to humans.

Scaling laws of ambush predator 'waiting' behaviour are tuned to a common ecology

The decisions animals make about how long to wait between activities can determine the success of diverse behaviours such as foraging, group formation or risk avoidance. Remarkably, for diverse animal species, including humans, spontaneous patterns of waiting times show random ‘burstiness’ that appears scale-invariant across a broad set of scales. However, a general theory linking this phenomenon across the animal kingdom currently lacks an ecological basis. Here, we demonstrate from tracking the activities of 15 sympatric predator species (cephalopods, sharks, skates and teleosts) under natural and controlled conditions that bursty waiting times are an intrinsic spontaneous behaviour well approximated by heavy-tailed (power-law) models over data ranges up to four orders of magnitude. Scaling exponents quantifying ratios of frequent short to rare very long waits are species-specific, being determined by traits such as foraging mode (active versus ambush predation), body size and prey preference. A stochastic–deterministic decision model reproduced the empirical waiting time scaling and species-specific exponents, indicating that apparently complex scaling can emerge from simple decisions. Results indicate temporal power-law scaling is a behavioural ‘rule of thumb’ that is tuned to species’ ecological traits, implying a common pattern may have naturally evolved that optimizes move–wait decisions in less predictable natural environments.

iMarNet: an ocean biogeochemistry model intercomparison project within a common physical ocean modelling framework

Ocean biogeochemistry (OBGC) models span a wide variety of complexities, including highly simplified nutrient-restoring schemes, nutrient–phytoplankton–zooplankton–detritus (NPZD) models that crudely represent the marine biota, models that represent a broader trophic structure by grouping organisms as plankton functional types (PFTs) based on their biogeochemical role (dynamic green ocean models) and ecosystem models that group organisms by ecological function and trait. OBGC models are now integral components of Earth system models (ESMs), but they compete for computing resources with higher resolution dynamical setups and with other components such as atmospheric chemistry and terrestrial vegetation schemes. As such, the choice of OBGC in ESMs needs to balance model complexity and realism alongside relative computing cost. Here we present an intercomparison of six OBGC models that were candidates for implementation within the next UK Earth system model (UKESM1). The models cover a large range of biological complexity (from 7 to 57 tracers) but all include representations of at least the nitrogen, carbon, alkalinity and oxygen cycles. Each OBGC model was coupled to the ocean general circulation model Nucleus for European Modelling of the Ocean (NEMO) and results from physically identical hindcast simulations were compared. Model skill was evaluated for biogeochemical metrics of global-scale bulk properties using conventional statistical techniques. The computing cost of each model was also measured in standardised tests run at two resource levels. No model is shown to consistently outperform all other models across all metrics. Nonetheless, the simpler models are broadly closer to observations across a number of fields and thus offer a high-efficiency option for ESMs that prioritise high-resolution climate dynamics. However, simpler models provide limited insight into more complex marine biogeochemical processes and ecosystem pathways, and a parallel approach of low-resolution climate dynamics and high-complexity biogeochemistry is desirable in order to provide additional insights into biogeochemistry–climate interactions.

Transdisciplinary Science: A Path to Understanding the Interactions Among Ocean Acidification, Ecosystems, and Society

The global nature of ocean acidification (OA) transcends habitats, ecosystems, regions, and science disciplines. The scientific community recognizes that the biggest challenge in improving understanding of how changing OA conditions affect ecosystems, and associated consequences for human society, requires integration of experimental, observational, and modeling approaches from many disciplines over a wide range of temporal and spatial scales. Such transdisciplinary science is the next step in providing relevant, meaningful results and optimal guidance to policymakers and coastal managers. We discuss the challenges associated with integrating ocean acidification science across funding agencies, institutions, disciplines, topical areas, and regions, and the value of unifying science objectives and activities to deliver insights into local, regional, and global scale impacts. We identify guiding principles and strategies for developing transdisciplinary research in the ocean acidification science community.