Longitudinal sampling of four cohorts of Neopomacentrus filamentosus, a common tropical damselfish from Dampier Archipelago, Western Australia, revealed the evolution of size structure after settlement. Light traps collected premetamorphic individuals from the water column (“settlers”) to establish a baseline for each cohort. Subsequently, divers collected benthic juveniles (“recruits”) at 1–3‐month intervals to determine the relative impacts of post‐settlement mortality during the first three months. Growth trajectories for individual fish were back‐calculated from otolith records and compared with nonlinear mixed‐effects models. Size‐selective mortality was detected in all cohorts with the loss of smaller, slower growing individuals. Three months after settlement, recruits showed significantly faster growth as juveniles, faster growth as larvae, and larger sizes as hatchlings. The timing and intensity of post‐settlement selection differed among cohorts and was correlated with density at settlement. The cohort with the greatest initial abundance experienced the strongest selective mortality, with most of this mortality occurring between one and two months after settlement when juveniles began foraging at higher positions in the water column. Significant genetic structure was found between settlers and three‐month‐old recruits in this cohort as a result of natural selection that changed the frequency of mtDNA haplotypes measured at the control region. The extent of this genetic difference was enlarged or reduced by artificially manipulating the intensity of size‐based selection, thus establishing a link between phenotype and haplotype. Sequence variation in the control region of the mitochondrial genome has been linked to mitochondrial efficiency and weight gain in other studies, which provides a plausible explanation for the patterns observed here.

With the availability of low-cost and efficient digital cameras, ecologists can now survey the world’s biodiversity through image sensors, especially in the previously rather inaccessible marine realm. However, the data rapidly accumulates, and ecologists face a data processing bottleneck. While computer vision has long been used as a tool to speed up image processing, it is only since the breakthrough of deep learning (DL) algorithms that the revolution in the automatic assessment of biodiversity by video recording can be considered. However, current applications of DL models to biodiversity monitoring do not consider some universal rules of biodiversity, especially rules on the distribution of species abundance, species rarity and ecosystem openness. Yet, these rules imply three issues for deep learning applications: the imbalance of long-tail datasets biases the training of DL models; scarce data greatly lessens the performances of DL models for classes with few data. Finally, the open-world issue implies that objects that are absent from the training dataset are incorrectly classified in the application dataset. Promising solutions to these issues are discussed, including data augmentation, data generation, cross-entropy modification, few-shot learning and open set recognition. At a time when biodiversity faces the immense challenges of climate change and the Anthropocene defaunation, stronger collaboration between computer scientists and ecologists is urgently needed to unlock the automatic monitoring of biodiversity.

Wilderness areas offer unparalleled ecosystem conditions. However, growing human populations and consumption are among factors that drive encroachment on these areas. Here, we explore the threat of small‐scale fisheries to wilderness reefs by developing a framework and modeling fluctuations in fishery range with fuel costs and fish prices. We modeled biomass of four fishery groups across the New Caledonian archipelago, and used fish and fuel prices from 2005 to 2020 to estimate the extent of exploited reefs across three fishing scenarios. From 2012 to 2018, maximum profitable range increased from 15 to over 30 hr from the capital city, expanding to reefs previously uneconomic to fish, including a UNESCO heritage site. By 2020, over half of New Caledonian (∼17% global) wilderness reefs will become profitable to fish. Our results demonstrate that remoteness from humans should not be considered protection for wilderness coral reefs in the context of rising fish prices.

Ecological surveys of coral reefs mostly rely on visual data collected by human observers. Although new monitoring tools are emerging, their specific advantages should be identified to optimise their simultaneous use. Based on the goodness-of-fit of linear models, we compared the potential of passive acoustics and environmental data for predicting the structure of coral reef fish assemblages in different environmental and biogeographic settings. Both data types complemented each other. Globally, the acoustic data showed relatively low added value in predicting fish assemblage structures. The predictions were best for the distribution of fish abundance among functional entities (i.e., proxies for fish functional groups, grouping species that share similar eco-morphological traits), for the simplest functional entities (i.e., combining two eco-morphological traits), and when considering diet and the level in the water column of the species. Our study demonstrates that Passive Acoustic Monitoring (PAM) improves fish assemblage assessment when used in tandem with environmental data compared to using environmental data alone. Such combinations can help with responding to the current conservation challenge by improving our surveying capacities at increased spatial and temporal scales, facilitating the identification and monitoring of priority management areas.