Many ecological datasets exhibit spatial correlation in observed variables, due to biotic or abiotic processes such as dispersal limitation, social aggregation, and spatial structure in unobserved explanatory variables. Whether the observations are points (e.g. animal locations), counts (e.g. the numbers of animals in spatial samples) or values of some continuous variable (e.g. nutrient levels at sampled points), spatial correlation causes every observation to depend on every other observation within some unknown correlation range. Dealing with this requires models that are mathematically more complex and computationally more demanding than is the case when there is independence among observations. We account for spatial dependence by incorporating a Gaussian random field (GRF) into models. GRFs are spatially continuous random processes in which random variables at any point in space are normally distributed and are correlated with random variables at other points in space according to a continuous correlation process. GRFs provide a means of modelling the spatial signal in the observations that cannot be accounted for by covariates. In the case of point data and count data, the GRF is linked to the response variable by a log link function, to give a log Gaussian Cox process (LGCP) model (Møller, & Waagepetersen, 2007). (Called 'log Gaussian' because the log of the intensity at any point is assumed to be normally distributed, and 'Cox process' because a Poisson process that has a randomly varying intensity function is called a Cox

Distance sampling is a widely used method for estimating wildlife population abundance. The fact that conventional distance sampling methods are partly design-based constrains the spatial resolution at which animal density can be estimated using these methods. Estimates are usually obtained at survey stratum level. For an endangered species such as the blue whale, it is desirable to estimate density and abundance at a finer spatial scale than stratum. Temporal variation in the spatial structure is also important. We formulate the process generating distance sampling data as a thinned spatial point process and propose model-based inference using a spatial log-Gaussian Cox process. The method adopts a flexible stochastic partial differential equation (SPDE) approach to model spatial structure in density that is not accounted for by explanatory variables, and integrated nested Laplace approximation (INLA) for Bayesian inference. It allows simultaneous fitting of detection and density models and permits prediction of density at an arbitrarily fine scale. We estimate blue whale density in the Eastern Tropical Pacific Ocean from thirteen shipboard surveys conducted over 22 years. We find that higher blue whale density is associated with colder sea surface temperatures in space, and although there is some positive association between density and mean annual temperature, our estimates are consitent with no trend in density across years. Our analysis also indicates that there is substantial spatially structured variation in density that is not explained by available covariates. * The project is funded by the Engineering and Physical Sciences Research Council(EPSRC) -EP/K041061/1 and EP/K041053/1.

The utilization of marine renewable energies such as offshore wind farming leads to globally expanding human activities in marine habitats. While knowledge on the responses to offshore wind farms and associated shipping traffic is accumulating now at a fast pace, it becomes important to assess the population impacts on species affected by those activities. In the North Sea, the protected diver species Red-throated Diver (Gavia stellata) and Black-throated Diver (Gavia arctica) widely avoid offshore wind farms. We used an explicit spatio-temporal Bayesian model to get a robust estimate of the diver population during the spring season between 2001 and 2018, based on a set of aerial surveys from long-term monitoring programs within the German North Sea. Despite the erection of 20 offshore wind farms in the study area and marked responses of divers to wind farms, model results indicated that there was no population decline, and overall numbers fluctuated around 16,600 individuals, with average annual 95% CI ranging between 13,400 and 21,360 individuals. Although, avoidance behavior due to wind farm development led to a more narrowly focused spatial distribution of the birds centered in the persistent high concentration zone in the Eastern German Bight Special Protection Area, the results provide no indication of negative fitness consequences on these long-lived species. However, more research is needed on habitat use and food availability in this regard.

Understanding spatiotemporally varying animal distributions can inform ecological understanding of species' behavior (e.g., foraging and predator/prey interactions) and support development of management and conservation measures.Data from an array of echolocation-click detectors (C-PODs) were analyzed using Bayesian spatiotemporal modeling to investigate spatial and temporal variation in occurrence and foraging activity of harbor porpoises (Phocoena phocoena) and how this variation was influenced by daylight and presence of bottlenose dolphins (Tursiops truncatus).The probability of occurrence of porpoises was highest on an offshore sandbank, where the proportion of detections with foraging clicks was relatively low. The porpoises' overall distribution shifted throughout the summer and autumn, likely influenced by seasonal prey availability. Probability of porpoise occurrence was lowest in areas close to the coast, where dolphin detections were highest and declined prior to dolphin detection, leading potentially to avoidance of spatiotemporal overlap between porpoises and dolphins.