Abstract
Ecological data are increasingly collected over vast geographic areas using arrays of digital sensors. Camera trap arrays have become the ‘gold standard’ method for surveying many terrestrial mammals and birds, but these arrays often generate millions of images that are challenging to process. This causes significant latency between data collection
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and subsequent inference, which can impede conservation at a time of ecological crisis. To address this, machine learning algorithms have been developed to improve data processing speeds, but these models are not considered accurate enough for fully automated labeling. Here, we present a new approach to building and testing a high performance machine learning model for fully automated labeling of camera trap images. As a case-study, the model classifies 26 Central African forest mammal and bird species (or groups). The model was trained on a relatively small dataset ( c .300,000 images) but generalizes to fully independent data and outperforms humans in several respects (e.g. detecting ‘invisible’ animals). We show how the model’s precision and accuracy can be evaluated in an ecological modeling context by comparing species richness, activity patterns and occupancy derived from machine learning labels with the same estimates derived from expert labels. Results show that fully automated labels can be equivalent to expert labels when calculating these widely-used ecological metrics. We provide the user-community with a multi-platform user interface for running the model offline, and conclude that high performance machine learning models can fully automate labeling of camera trap data. Significance statement Large-scale ecological monitoring can be used to detect ecosystem change. Ecological sensors such as camera traps are deployed across large spatial and temporal scales to monitor species and communities. Camera trap data are often vast (millions of images) and manual processing times cause significant latency between data collection and ecological inference. Existing machine learning models can reduce processing times but are rarely used in fully automated workflows for ecological analyses, mainly because users lack confidence in the model’s precision and accuracy. Here, we show a new, high performance machine learning model can be used to make ecological inference that is equivalent to using manually generated, expert labels. These results pave the way for large-scale, fully automated biodiversity monitoring and forecasting using camera trap arrays. ### Competing Interest Statement The authors have declared no competing interest.
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