1. Ecological data are collected over vast geographic areas using digital sensors such as camera traps and bioacoustic recorders. Camera traps have become the standard method for surveying many terrestrial mammals and birds, but camera trap arrays often generate millions of images that are time-consuming to label. This causes significant latency between data collection and subsequent inference, which impedes conservation at a time of ecological crisis. Machine learning algorithms have been developed to improve the speed of labelling camera trap data, but it is uncertain how the outputs of these models can be used in ecological analyses without secondary validation by a human.2. Here, we present our approach to developing, testing and applying a machine learning model to camera trap data for the purpose of achieving fully automated ecological analyses. As a case-study, we built a model to classify 26 Central African forest mammal and bird species (or groups). The model generalizes to new spatially and temporally independent data (n = 227 camera stations, n = 23,868 images), and outperforms humans in several respects (e.g. detecting 'invisible' animals). We demonstrate how ecologists can evaluate a machine learning model's precision and accuracy in an ecological context by comparing species richness, activity patternsThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Recent seizures of illegally held wildlife indicate a mounting global trade in pangolins involving all eight species. Seizures of illegally traded African pangolins are increasing as wild populations of Asian species decline. We investigated trade in pangolins and law enforcement efforts in Gabon; a country likely to have intact wild populations of three of the four species of African pangolin. We compared village sales and trade chains between 2002‐2003 and 2014. Hunters reported pangolins to be the most frequently requested species in 2014, and the value of pangolins had increased at every point along their trade chain. In Libreville, giant pangolin prices increased 211% and arboreal pangolin prices 73% whilst inflation rose only 4.6% over the same period. We documented a low rate of interception of illegally traded pangolins despite increased law enforcement. Surveys of potential export routes detected exports across forest borders, in conjunction with ivory, but not through public transport routes. We conclude that whilst there is clear potential and likelihood that a wild pangolin export trade is emerging from Gabon, traditional bushmeat trade chains may not be the primary supply route. We recommend adjusting conservation policies and actions to impede further development of illegal trade within and from Gabon.

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Modeling fire spread as an infection process is intuitive: An ignition lights a patch of fuel, which infects its neighbor, and so on. Infection models produce nonlinear thresholds, whereby fire spreads only when fuel connectivity and infection probability are sufficiently high. These thresholds are fundamental both to managing fire and to theoretical models of fire spread, whereas applied fire models more often apply quasi-empirical approaches. Here, we resolve this tension by quantifying thresholds in fire spread locally, using field data from individual fires ( n = 1,131) in grassy ecosystems across a precipitation gradient (496 to 1,442 mm mean annual precipitation) and evaluating how these scaled regionally (across 533 sites) and across time (1989 to 2012 and 2016 to 2018) using data from Kruger National Park in South Africa. An infection model captured observed patterns in individual fire spread better than competing models. The proportion of the landscape that burned was well described by measurements of grass biomass, fuel moisture, and vapor pressure deficit. Regionally, averaging across variability resulted in quasi-linear patterns. Altogether, results suggest that models aiming to capture fire responses to global change should incorporate nonlinear fire spread thresholds but that linear approximations may sufficiently capture medium-term trends under a stationary climate.