Research gaps and trends in the Arctic tundra: a topic-modelling approach

Fifteen research needs for understanding climate change impacts on ecosystems and society in the Norwegian High North

There is an urgent need to understand and address the risks associated with a warming climate for ecosystems and societies in the Arctic and sub-Arctic regions. There are major gaps in our understanding of the complex effects of climate change-including extreme events, cascading impacts across ecosystems, and the underlying socioecological dynamics and feedbacks-all of which need collaborative efforts to be resolved. Here, we present results where climate scientists, ecologists, social scientists, and practitioners were asked to identify the most urgent research needs for understanding climate change impacts and to identify the actions for reducing future risks in catchment areas in the Norwegian High North, a region that encompasses both Arctic and sub-Arctic climates in northern Norway. From a list of 77 questions, our panel of 19 scientists and practitioners identified 15 research needs that should be urgently addressed. We particularly urge researchers to investigate cross-ecosystem impacts and the socioecological feedbacks that could amplify or reduce risks for society.

causalizeR: a text mining algorithm to identify causal relationships in scientific literature

Complex interactions among multiple abiotic and biotic drivers result in rapid changes in ecosystems worldwide. Predicting how specific interactions can cause ripple effects potentially resulting in abrupt shifts in ecosystems is of high relevance to policymakers, but difficult to quantify using data from singular cases. We present causalizeR (https://github.com/fjmurguzur/causalizeR), a text-processing algorithm that extracts causal relations from literature based on simple grammatical rules that can be used to synthesize evidence in unstructured texts in a structured manner. The algorithm extracts causal links using the relative position of nouns relative to the keyword of choice to extract the cause and effects of interest. The resulting database can be combined with network analysis tools to estimate the direct and indirect effects of multiple drivers at the network level, which is useful for synthesizing available knowledge and for hypothesis creation and testing. We illustrate the use of the algorithm by detecting causal relationships in scientific literature relating to the tundra ecosystem.