This review highlights the latest developments associated with the use of the Normalized Difference Vegetation Index (NDVI) in ecology. Over the last decade, the NDVI has proven extremely useful in predicting herbivore and non-herbivore distribution, abundance and life history traits in space and time. Due to the continuous nature of NDVI since mid-1981, the relative importance of different temporal and spatial lags on population performance can be assessed, widening our understanding of population dynamics. Previously thought to be most useful in temperate environments, the utility of this satellite-derived index has been demonstrated even in sparsely vegetated areas. Climate models can be used to reconstruct historical patterns in vegetation dynamics in addition to anticipating the effects of future environmental change on biodiversity. NDVI has thus been established as a crucial tool for assessing past and future population and biodiversity consequences of change in climate, vegetation phenology and primary productivity.

Summary 1.Animal migration has long intrigued scientists and wildlife managers alike, yet migratory species face increasing challenges because of habitat fragmentation, climate change and over-exploitation. Central to the understanding migratory species is the objective discrimination between migratory and nonmigratory individuals in a given population, quantifying the timing, duration and distance of migration and the ability to predict migratory movements. 2. Here, we propose a uniform statistical framework to (i) separate migration from other movement behaviours, (ii) quantify migration parameters without the need for arbitrary cut-off criteria and (iii) test predictability across individuals, time and space. 3. We first validated our novel approach by simulating data based on established theoretical movement patterns. We then formulated the expected shapes of squared displacement patterns as nonlinear models for a suite of movement behaviours to test the ability of our method to distinguish between migratory movement and other movement types. 4. We then tested our approached empirically using 108 wild Global Positioning System (GPS)-collared moose Alces alces in Scandinavia as a study system because they exhibit a wide range of movement behaviours, including resident, migrating and dispersing individuals, within the same population. Applying our approach showed that 87% and 67% of our Swedish and Norwegian subpopulations, respectively, can be classified as migratory. 5. Using nonlinear mixed effects models for all migratory individuals we showed that the distance, timing and duration of migration differed between the sexes and between years, with additional individual differences accounting for a large part of the variation in the distance of migration but not in the timing or duration. Overall, the model explained most of the variation (92%) and also had high predictive power for the same individuals over time (69%) as well as between study populations (74%). 6. The high predictive ability of the approach suggests that it can help increase our understanding of the drivers of migration and could provide key quantitative information for understanding and managing a broad range of migratory species.

Understanding the causes and consequences of animal movements is of fundamental biological interest because any alteration in movement can have direct and indirect effects on ecosystem structure and function. It is also crucial for assisting spatial wildlife management under variable environmental change scenarios. Recent research has highlighted the need of quantifying individual variability in movement behavior and how it is generated by interactions between individual requirements and environmental conditions, to understand the emergence of population-level patterns. Using a multi-annual movement data set of 213 individual moose (Alces alces) across a latitudinal gradient (from 56 degrees to 67 degrees N) that spans over 1100 km of varying environmental conditions, we analyze the differences in individual and population-level movements. We tested the effect of climate, risk, and human presence in the landscape on moose movements. The variation in these factors explained the existence of multiple movements (migration, nomadism, dispersal, sedentary) among individuals and seven populations. Population differences were primarily related to latitudinal variation in snow depth and road density. Individuals showed both fixed and flexible behaviors across years, and were less likely to migrate with age in interaction with snow and roads. For the predominant movement strategy, migration, the distance, timing, and duration at all latitudes varied between years. Males traveled longer distances and began migrating later in spring than females. Our study provides strong quantitative evidence for the dynamics of animal movements in response to changes in environmental conditions along with varying risk from human influence across the landscape. For moose, given its wide distributional range, changes in the distribution and migratory behavior are expected under future warming scenarios.

Conventional approaches to natural resource management are increasingly challenged by environmental problems that are embedded in highly complex systems with profound uncertainties. These so‐called social‐ecological systems (SESs) are characterized by strong links between the social and the ecological system and multiple interactions across spatial and temporal scales. New approaches are needed to manage those tightly coupled systems; however, basic understanding of their nonlinear behavior is still missing. Modeling is a traditional tool in natural resource management to study complex, dynamic systems. There is a long tradition of SES modeling, but the approach is now being more widely recognized in other fields, such as ecological and economic modeling, where issues such as nonlinear ecological dynamics and complex human decision making are receiving more attention. SES modeling is maturing as a discipline in its own right, incorporating ideas from other interdisciplinary fields such as resilience or complex systems research. In this paper, we provide an overview of the emergence and state of the art of this cross‐cutting field. Our analysis reveals the substantial potential of SES models to address issues that are of utmost importance for managing complex human‐environment relationships, such as: (i) the implications of ecological and social structure for resource management, (ii) uncertainty in natural and social systems and ways to address it, (iii) the role of coevolutionary processes in the dynamics of SESs, and (iv) the implications of microscale human decision making for sustainable resource management and conservation. The complexity of SESs and the lack of a common analytical framework, however, also pose significant challenges for this emerging field. There are clear research needs with respect to: (i) approaches that go beyond rather simple specifications of human decision making, (ii) development of coping strategies to deal with (irreducible) uncertainties, (iii) more explicit modeling of feedbacks between the social and ecological systems, and (iv) a conceptual and methodological framework for analyzing and modeling SESs. We provide ideas for tackling some of these challenges and indicate potential key focal areas for SES modeling in the future.

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Ecoacoustics, the study of environmental sound, is a growing field with great potential for biodiversity monitoring. Audio recordings could provide a rapid, cost‐effective monitoring tool offering novel insights into ecosystem dynamics. More than 60 acoustic indices have been developed to date, which reflect distinct attributes of the soundscape, (i.e. the total acoustic energy at a given location, including noise produced by animals, machinery, wind and rain). However, reported patterns in acoustic indices have been contradictory, possibly because there is no accepted best practice for the collection and analysis of audio recordings. Here, we propose: (a) guidelines for designing studies using audio recordings for the rapid assessment of multiple sites; and (b) a workflow for comparing recordings with seven of the most commonly used indices, permitting discrimination among habitat‐specific soundscapes. We collected and analysed over 26,000 hr of recordings from 117 sites across a range of habitats in a human‐modified tropical landscape in central Panama; an order of magnitude more recordings than used in previously published studies. We demonstrate that: (a) Standard error variance of indices stabilizes within 120 hr of recordings from a single location. (b) Continuous recording should be used rather than subsample recording on a schedule; sub sampling is a common practice but delays capture of site variability and maximizing total duration of recording should be prioritized. (c) Use of multiple indices to describe soundscape patterns reveals distinct diel and seasonal soundscape patterns among habitats. We advocate collecting at least 120 hr of continuous recordings per site, and using a range of acoustic indices to categorize the soundscape, including the Acoustic Complexity Index, Acoustic Evenness Index, Acoustic Entropy Index and the Normalized Difference Soundscape Index. Differences among habitat types can be captured if multiple indices are used, and magnitude of variance is often more important than mean values. The workflow we provide will enable successful use of ecoacoustic techniques for environmental monitoring.

Conservation science is advancing rapidly, yet the majority of research overlooks a key factor that can play a major role in shaping the outcomes of conservation initiatives: collaboration. Here, we review the importance, benefits and limitations of incorporating collaboration into conservation and specifically into systematic conservation planning, providing a general framework for considering collaboration in conservation planning. Recent work shows that cross-boundary collaboration can have positive and negative impacts on the outcomes of conservation and management efforts for protected areas, ecosystems, threatened and invasive species. The feasibility of collaboration, its likely effects and associated trade-offs should be explicitly incorporated into conservation science and planning. This will ensure that conservation decisions avoid wasted funding when collaboration is infeasible, promoting collaboration when the benefits outweigh the costs.

Finding effective ways of conserving large carnivores is widely recognised as a priority in conservation. However, there is disagreement about the most effective way to do this, with some favouring top-down 'command and control' approaches and others favouring collaboration. Arguments for coercive top-down approaches have been presented elsewhere; here we present arguments for collaboration. In many parts of the developed world, flexibility of approach is built into the legislation, so that conservation objectives are balanced with other legitimate goals. In the developing world, limited resources, poverty and weak governance mean that collaborative approaches are likely to play a particularly important part in carnivore conservation. In general, coercive policies may lead to the deterioration of political legitimacy and potentially to non-compliance issues such as illegal killing, whereas collaborative approaches may lead to psychological ownership, enhanced trust, learning, and better social outcomes. Sustainable hunting/trapping plays a crucial part in the conservation and management of many large carnivores. There are many different models for how to conserve carnivores effectively across the world, research is now required to reduce uncertainty and examine the effectiveness of these approaches in different contexts.