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This is the final version. Available on open access from Springer via the DOI in this record ; Data availability: Data used in the econometric analysis are available from the authors on request. ; The covid-19 pandemic led to rapid and large-scale government intervention in economies and societies. A common policy response to covid-19 outbreaks has been the lockdown or quarantine. Designed to slow the spread of the disease, lockdowns have unintended consequences for the environment. This article examines the impact of Colombia's lockdown on forest fires, motivated by satellite data showing a particularly large upsurge of fires at around the time of lockdown implementation. We find that Colombia's lockdown is associated with an increase in forest fires compared to three different counterfactuals, constructed to simulate the expected number of fires in the absence of the lockdown. To varying degrees across Colombia's regions, the presence of armed groups is correlated with this fire upsurge. Mechanisms through which the lockdown might influence fire rates are discussed, including the mobilisation of armed groups and the reduction in the monitoring capacity of state and conservation organisations during the covid-19 outbreak. Given the fast-developing situation in Colombia, we conclude with some ideas for further research. ; Natural Environment Research Council (NERC)

This is the author accepted manuscript. the final version is available from Elsevier via the DOI in this record ; Tropical biomes are the most diverse plant communities on Earth, and quantifying this diversity at large spatial scales is vital for many purposes. As macroecological approaches proliferate, the taxonomic uncertainties in species occurrence data are easily neglected and can lead to spurious findings in downstream analyses. Here, we argue that technological approaches offer potential solutions, but there is no single silver bullet to resolve uncertainty in plant biodiversity quantification. Instead, we propose the use of artificial intelligence (AI) approaches to build a data-driven framework that integrates several data sources – including spectroscopy, DNA sequences, image recognition, and morphological data. Such a framework would provide a foundation for improving species identification in macroecological analyses while simultaneously improving the taxonomic process of species delimitation. ; European Union ; Agence Nationale de la Recherche (CEBA)