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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.

Efforts to preserve, protect, and restore ecosystems are hindered by long delays between data collection and analysis. Threats to ecosystems can go undetected for years or decades as a result. Real-time data can help solve this issue but significant technical barriers exist. For example, automated camera traps are widely used for ecosystem monitoring but it is challenging to transmit images for real-time analysis where there is no reliable cellular or WiFi connectivity. Here, we present our design for a camera trap with integrated artificial intelligence that can send real-time information from anywhere in the world to end-users.We modified an off-the-shelf camera trap (Bushnell™) and customised existing open-source hardware to rapidly create a ‘smart’ camera trap system. Images captured by the camera trap are instantly labelled by an artificial intelligence model and an ‘alert’ containing the image label and other metadata is then delivered to the end-user within minutes over the Iridium satellite network. We present results from testing in the Netherlands, Europe, and from a pilot test in a closed-canopy forest in Gabon, Central Africa.Results show the system can operate for a minimum of three months without intervention when capturing a median of 17.23 images per day. The median time-difference between image capture and receiving an alert was 7.35 minutes. We show that simple approaches such as excluding ‘uncertain’ labels and labelling consecutive series of images with the most frequent class (vote counting) can be used to improve accuracy and interpretation of alerts.We anticipate significant developments in this field over the next five years and hope that the solutions presented here, and the lessons learned, can be used to inform future advances. New artificial intelligence models and the addition of other sensors such as microphones will expand the system’s potential for other, real-time use cases. Potential applications include, but are not limited to, wildlife tourism, real-time biodiversity monitoring, wild resource management and detecting illegal human activities in protected areas.

Forest edges that border savanna are dynamic features of tropical landscapes. Although the role of fire in determining edge dynamics has been relatively well explored, the role of mega-herbivores, specifically elephants, has not received as much attention. We investigated the role of forest elephants in shaping forest edges of the forest–savanna mosaic in Lope National Park, Gabon. Using forty camera traps, we collected 1.2 million images between May 2016 and June 2017. These images were classified by over 10,000 volunteers through an online citizen science platform. These data were combined with a 33-year phenology dataset on elephant-favoured fruiting tree species, and field measurements of elephant browsing preferences and damage. Our results showed a strong relationship between forest elephant density at the forest edge and fruit availability. When fruit availability was high, elephant density at the edge reached values nearly double the highest densities ever reported in any other part of the landscape (7.5 elephants km−2 in this study vs the previous highest estimate of 4 elephants km−2). The highest elephant densities occurred at the end of the dry season, but even outside of this high density period elephant density at the forest edge (2.4 elephants km−2) was more than double what other studies estimate for forest interiors with low human hunting pressure (1 elephant km−2). We found forest elephants to be selective browsers, but their browsing was non-destructive (in contrast to savanna elephants) and had little effect on tree size demography. Elephant paths acted as firebreaks during savanna burning, making them inadvertent protectors of the fire-sensitive forest and contributing to the stabilising feedbacks that allow forest and savanna to coexist in tropical landscapes.

Fruit decline threatens forest elephants Large mammal herbivores in African tropical forests are major consumers of fruit, and many tree species rely on these consumers for dispersal of their seeds. Bush et al. monitored fruit production over three decades in a protected national park in Gabon, showing an 80% decline across the 73 plant species monitored. At the same time, photographic records of forest elephants over the past decade indicate a substantial decline in body condition in these major herbivores. These results suggest that the capacity of the ecosystem to support the elephant population is decreasing, a worrying prospect in an environment that is still protected from other threats such as hunting and deforestation. Science , this issue p. 1219

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 and subsequent inference, which can impede conservation at a time of ecological crisis. Machine learning algorithms have been developed to improve camera trap data processing speeds, but these models are not considered accurate enough for fully automated labeling of images.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 (n = 4 species tested) and occupancy (n = 4 species tested) 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 species richness, activity patterns (n = 4 species tested) and estimating occupancy (n = 3 of 4 species tested) in completely out-of-sample test data (n = 227 camera stations, n = 23868 images). Simple thresholding (discarding uncertain labels) improved the model’s performance when calculating activity patterns and estimating occupancy, but did not improve estimates of species richness.We provide the user-community with a multi-platform, multi-language user interface for running the model offline, and conclude that high performance machine learning models can fully automate labeling of camera trap data.

Epiphytes in tropical montane cloud forests (TMCF) intercept atmospheric water and, as a result, form a vital part of the hydrological cycle of this ecosystem. Our study investigates the role of bryophytes in such systems on La Reunion Island (Mascarenes). To better understand ecohydrological functioning of the forest, we investigated cloud water interception (CWI) by two locally abundant liverwort species ( Bazzania decrescens and Mastigophora diclados ) using a novel lysimetric approach. We also evaluated biomass and water storage capacity of our study species, as well as of the entire bryophyte community in our plots, which we extrapolated to the forest community level. Both study species exhibited excellent abilities to intercept and store cloud water, and showed distinct diurnal variation in this ability according to varying climatic conditions. The two liverwort species’ response to climatic conditions differed dramatically from one another, with B. decrescens storing double the mean and maximum litres of water per hectare despite having less than half the abundance of M. diclados . Despite its lower water storage capacity, M. diclados had a greater ability to intercept atmospheric moisture than B. decrescens . The differences in CWI were attributed to differences in plant structure of these two species, which explains their microhabitat requirements in this system. Our two species in this system were estimated to store 34,569 l.ha −1 of water, the equivalent of 3.46 mm of rainfall. The abundance of our study species combined with their atmospheric water interception, storage, and regulated release ability make both species ecologically important in the forest’s microhydrological cycle. For the first time these data allow us to better understand the role of these plants in the microhydrological cycle of tropical montane cloud forests and to determine whether the diversity and functioning of these and similar systems will be at risk from predicted cloud layer/coverage lifting.

Forest-savanna mosaics are maintained by fire-mediated positive feedbacks; whereby forest is fire suppressive and savanna is fire promoting. Forest-savanna transitions therefore represent the interface of opposing fire regimes. Within the transition there is a threshold point at which tree canopy cover becomes sufficiently dense to shade out grasses and thus suppress fire. Prior to reaching this threshold, changes in fire behaviour may already be occurring within the savanna. Such changes are neither empirically described nor their drivers understood. Fire behaviour is largely driven by fuel flammability. Flammability can vary significantly between grass species and grass species composition can change near forest-savanna transitions. This study measured fire behaviour changes at eighteen forest-savanna transition sites in a vegetation mosaic in Lope National Park in Gabon, central Africa. The extent to which these changes could be attributed to changes in grass flammability was determined using species-specific flammability traits. Results showed simultaneous suppression of fire and grass biomass when tree canopy leaf area index (LAI) reached a value of 3, indicating that a fire suppression threshold existed within the forest-savanna transition. Fires became less intense and less hot prior to reaching this fire suppression threshold. These changes were associated with higher LAI values, which induced a change in the grass community, from one dominated by the highly flammable Anadelphia afzeliana to one dominated by the less flammable Hyparrhenia diplandra. Changes in fire behaviour were not associated with changes in total grass biomass. This study demonstrated not only the presence of a fire suppression threshold but the mechanism of its action. Grass composition mediated fire-behaviour within the savanna prior to reaching the suppression threshold, and grass species composition was mediated by tree canopy cover which was in turn mediated by fire-behaviour. These findings highlight how biotic and abiotic controls interact and amplify each other in this mosaicked landscape to facilitate forest and savanna co-existence.

Temperate forests are globally important carbon stores that are, in the face of recent improvements in their conservation, likely to increase their storage capacity in the future. Despite this, these ecosystems are poorly understood, especially over longer time periods. To remedy this and to better understand these important ecosystems, we monitored marked stems >5 cm diameter at breast height (DBH) in a 0.52 ha forest plot on the Cape Peninsula over 26 years. Aboveground biomass (AGB), calculated from stem diameter, increased from 203 to 226 Mg ha−1 over this period. The AGB residence time was greater than a century. Stem mortality was relatively high (1.2% per annum [p.a.]) and exceeded recruitment (0.2% p.a.). The recruitment of a large number of smaller stems of species not presently represented in the forest canopy suggests that compositional changes will occur in the future. Overall, these results suggest that the forest is in a post-disturbance recovery phase, although favourable climatic conditions over the last three decades may also have had an influence on AGB accumulation.Keywords: aboveground biomass, carbon sequestration, forest conservation, long-term monitoring, succession