Effects of noise and by-catch on a Danish harbour porpoise population

The initial response of individuals to human-induced environmental change is often behavioural. This can improve the performance of individuals under sudden, large-scale perturbations and maintain viable populations. The response can also give additional time for genetic changes to arise and, hence, facilitate adaptation to new conditions. On the other hand, maladaptive responses, which reduce individual fitness, may occur when individuals encounter conditions that the population has not experienced during its evolutionary history, which can decrease population viability. A growing number of studies find human disturbances to induce behavioural responses, both directly and by altering factors that influence fitness. Common causes of behavioural responses are changes in the transmission of information, the concentration of endocrine disrupters, the availability of resources, the possibility of dispersal, and the abundance of interacting species. Frequent responses are alterations in habitat choice, movements, foraging, social behaviour and reproductive behaviour. Behavioural responses depend on the genetically determined reaction norm of the individuals, which evolves over generations. Populations first respond with individual behavioural plasticity, whereafter changes may arise through innovations and the social transmission of behavioural patterns within and across generations, and, finally, by evolution of the behavioural response over generations. Only a restricted number of species show behavioural adaptations that make them thrive in severely disturbed environments. Hence, rapid human-induced disturbances often decrease the diversity of native species, while facilitating the spread of invasive species with highly plastic behaviours. Consequently, behavioural responses to human-induced environmental change can have profound effects on the distribution, adaptation, speciation and extinction of populations and, hence, on biodiversity. A better understanding of the mechanisms of behavioural responses and their causes and consequences could improve our ability to predict the effects of human-induced environmental change on individual species and on biodiversity. © 2010 The Authors. Biological Reviews © 2010 Cambridge Philosophical Society.

We argue that population growth rate is the key unifying variable linking the various facets of population ecology. The importance of population growth rate lies partly in its central role in forecasting future population trends; indeed if the form of density dependence were constant and known, then the future population dynamics could to some degree be predicted. We argue that population growth rate is also central to our understanding of environmental stress: environmental stressors should be defined as factors which when first applied to a population reduce population growth rate. The joint action of such stressors determines an organism's ecological niche, which should be defined as the set of environmental conditions where population growth rate is greater than zero (where population growth rate = r = log(e)(N(t+1)/N(t))). While environmental stressors have negative effects on population growth rate, the same is true of population density, the case of negative linear effects corresponding to the well-known logistic equation. Following Sinclair, we recognize population regulation as occurring when population growth rate is negatively density dependent. Surprisingly, given its fundamental importance in population ecology, only 25 studies were discovered in the literature in which population growth rate has been plotted against population density. In 12 of these the effects of density were linear; in all but two of the remainder the relationship was concave viewed from above. Alternative approaches to establishing the determinants of population growth rate are reviewed, paying special attention to the demographic and mechanistic approaches. The effects of population density on population growth rate may act through their effects on food availability and associated effects on somatic growth, fecundity and survival, according to a 'numerical response', the evidence for which is briefly reviewed. Alternatively, there may be effects on population growth rate of population density in addition to those that arise through the partitioning of food between competitors; this is 'interference competition'. The distinction is illustrated using a replicated laboratory experiment on a marine copepod, Tisbe battagliae. Application of these approaches in conservation biology, ecotoxicology and human demography is briefly considered. We conclude that population regulation, density dependence, resource and interference competition, the effects of environmental stress and the form of the ecological niche, are all best defined and analysed in terms of population growth rate.

The world's oceans are undergoing profound changes as a result of human activities. However, the consequences of escalating human impacts on marine mammal biodiversity remain poorly understood. The International Union for the Conservation of Nature (IUCN) identifies 25% of marine mammals as at risk of extinction, but the conservation status of nearly 40% of marine mammals remains unknown due to insufficient data. Predictive models of extinction risk are crucial to informing present and future conservation needs, yet such models have not been developed for marine mammals. In this paper, we: (i) used powerful machine-learning and spatial-modeling approaches to understand the intrinsic and extrinsic drivers of marine mammal extinction risk; (ii) used this information to predict risk across all marine mammals, including IUCN "Data Deficient" species; and (iii) conducted a spatially explicit assessment of these results to understand how risk is distributed across the world's oceans. Rate of offspring production was the most important predictor of risk. Additional predictors included taxonomic group, small geographic range area, and small social group size. Although the interaction of both intrinsic and extrinsic variables was important in predicting risk, overall, intrinsic traits were more important than extrinsic variables. In addition to the 32 species already on the IUCN Red List, our model identified 15 more species, suggesting that 37% of all marine mammals are at risk of extinction. Most at-risk species occur in coastal areas and in productive regions of the high seas. We identify 13 global hotspots of risk and show how they overlap with human impacts and Marine Protected Areas.