Systematic searching aims to find all possibly relevant research from multiple sources, the basis for an unbiased and comprehensive evidence base. Along with bibliographic databases, systematic reviewers use a variety of additional methods to minimise procedural bias. Citation chasing exploits connections between research articles to identify relevant records for a review by making use of explicit mentions of one article within another. Citation chasing is a popular supplementary search method because it helps to build on the work of primary research and review authors. It does so by identifying potentially relevant studies that might otherwise not be retrieved by other search methods;for example, because they did not use the review authors' search terms in the specified combinations in their titles, abstracts, or keywords. Here, we briefly provide an overview of citation chasing as a method for systematic reviews.Furthermore, given the challenges and high resource requirements associated with citation chasing, the limited application of citation chasing in otherwise rigorous systematic reviews, and the potential benefit of identifying terminologically disconnected but semantically linked research studies, we have developed and describe a free and open source tool that allows for rapid forward and backward citation chasing. We introduce citationchaser, an R package and Shiny app for conducting forward and backward citation chasing from a starting set of articles. We describe the sources of data, the backend code functionality, and the user interface provided in the Shiny app.

Landscape heterogeneity may influence ranging behaviour of mammals. Here we relate the home range size of elephants living in the Kruger National Park to the number of patches, proportion of each patch, spatial arrangement of patches, patch shape, and contrast between neighbouring patches. Home range sizes decreased exponentially with an increase in the number of patches per 100 km 2 and the home range sizes of bulls were in general more strongly related to measures of heterogeneity. This may reflect differences in perception of heterogeneity between the sexes.Key words: FRAGSTATS, home range, landscape heterogeneity RésuméIl se peut que la hétérogénéité du paysage agisse sur le comportement des mammifères au pâturage. Dans cette étude nous associons la taille des domaines vitaux des éléphants résidant dans le Parc National de Kruger au nombre de parcelles et leurs dimensions, disposition spatiale et forme, ainsi que le contraste entre des parcelles avoisinantes. La taille des domaines vitaux diminua exponentiellement avec l'augmentation dans le nombre de parcelles par 100 km 2 alors que les domaines vitaux des mâles furent plus fortement associés aux mesures d'hét-érogénéité en général. Cela est peut être dû aux différences au niveau de la perception d'hétérogénéité entre les sexes.

The Sustainable Development Goals (SDGs) are presented as highly connected: an 'interrelated' and 'indivisible' agenda with need of policy coherence for implementation. We analyse the relationships among SDGs using formal systems analysis and find that the connections between Goals are uneven, with a failure to integrate gender equality, and peace and governance concerns. This incoherence may undermine policy initiatives aimed to develop approaches to implement the SDGs.The SDGs were adopted by United Nations member states in September 2015 1 in the 2030 Agenda for Sustainable Development. They describe a "plan of action for people, planet and prosperity" to "stimulate action over the next 15 years in areas of critical importance for humanity and the planet". The SDGs are presented as "integrated and indivisible", whilst acknowledging differing priorities and capacities between countries, where responsibility for delivery lies 1 .

Conservation decisions are challenging, not only because they often involve difficult conflicts among outcomes that people value, but because our understanding of the natural world and our effects on it is fraught with uncertainty. Value of Information (VoI) methods provide an approach for understanding and managing uncertainty from the standpoint of the decision maker. These methods are commonly used in other fields (e.g. economics, public health) and are increasingly used in biodiversity conservation. This decision‐analytical approach can identify the best management alternative to select where the effectiveness of interventions is uncertain, and can help to decide when to act and when to delay action until after further research. We review the use of VoI in the environmental domain, reflect on the need for greater uptake of VoI, particularly for strategic conservation planning, and suggest promising areas for new research. We also suggest common reporting standards as a means of increasing the leverage of this powerful tool. The environmental science, ecology and biodiversity categories of the Web of Knowledge were searched using the terms ‘Value of Information,’ ‘Expected Value of Perfect Information,’ and the abbreviation ‘EVPI.’ Google Scholar was searched with the same terms, and additionally the terms decision and biology, biodiversity conservation, fish, or ecology. We identified 1225 papers from these searches. Included studies were limited to those that showed an application of VoI in biodiversity conservation rather than simply describing the method. All examples of use of VOI were summarised regarding the application of VoI, the management objectives, the uncertainties, the models used, how the objectives were measured, and the type of VoI. While the use of VoI appears to be on the increase in biodiversity conservation, the reporting of results is highly variable, which can make it difficult to understand the decision context and which uncertainties were considered. Moreover, it was unclear if, and how, the papers informed management and policy interventions, which is why we suggest a range of reporting standards that would aid the use of VoI. The use of VoI in conservation settings is at an early stage. There are opportunities for broader applications, not only for species‐focussed management problems, but also for setting local or global research priorities for biodiversity conservation, making funding decisions, or designing or improving protected area networks and management. The long‐term benefits of applying VoI methods to biodiversity conservation include a more structured and decision‐focused allocation of resources to research.

1. Aggregated species occurrence and abundance data from disparate sources are increasingly accessible to ecologists for the analysis of temporal trends in biodiversity. However, sampling biases relevant to any given research question are often poorly explored and infrequently reported; this can undermine statistical inference. In other disciplines, it is common for researchers to complete 'risk-ofbias' assessments to expose and document the potential for biases to undermine conclusions. The huge growth in available data, and recent controversies surrounding their use to infer temporal trends, indicate that similar assessments are urgently needed in ecology.2. We introduce ROBITT, a structured tool for assessing the 'Risk-Of-Bias In studies of Temporal Trends in ecology'. ROBITT has a similar format to its counterparts in other disciplines: it comprises signalling questions designed to elicit information on the potential for bias in key study domains. In answering these, users will define study inferential goal(s) and relevant statistical target populations. This information is used to assess potential sampling biases across domains relevant to the research question (e.g. geography, taxonomy, environment), and how these vary through time. If assessments indicate biases, then users must clearly describe them and/or explain what mitigating action will be taken.3. Everything that users need to complete a ROBITT assessment is provided: the tool, a guidance document and a worked example. Following other disciplines, the tool and guidance document were developed through a consensus-forming process across experts working in relevant areas of ecology and evidence synthesis.