Grass Species Flammability, Not Biomass, Drives Changes in Fire Behavior at Tropical Forest-Savanna Transitions

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 behavior may already be occurring within the savanna. Such changes are neither empirically described nor their drivers understood. Fire behavior 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 behavior changes at eighteen forest-savanna transition sites in a vegetation mosaic in Lopé 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 behavior 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-behavior 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-behavior. These findings highlight how biotic and abiotic controls interact and amplify each other in this mosaicked landscape to facilitate forest and savanna co-existence

Camera Trap Images used in "Identifying Animal Species in Camera Trap Images using Deep Learning and Citizen Science"

All images were downloaded from Zooniverse and have been resized to 330x330 pixels.This dataset provides the camera trap images used in "Identifying Animal Species in Camera Trap Images using Deep Learning and Citizen Science" as well as meta-data about the images. The Snapshop Serengeti collection includes 6,163,870 images in JPG format. The Snapshot Wisconsin collection includes 497,204 images in JPG format. The Camera CATalogue collection include 506,241 images in JPG format. Excluded are the images for the dataset "Elephant Expedition" which will be published separately outside DRUM. Also excluded are images of humans due to privacy reasons.This study was partially supported by the NSF under award IIS 1619177The development of the Zooniverse platform was partially supported by a Global Impact Award from Google.We also acknowledge support from STFC under grant ST/N003179/1.EE was funded by the University of Oxford’s Hertford College Mortimer May fund

The Role of Forest Elephants in Shaping Tropical Forest-Savanna Coexistence

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 Lopé 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

Understanding and modelling wildfire regimes: An ecological perspective

© 2021 The Author(s).Recent extreme wildfire seasons in several regions have been associated with exceptionally hot, dry conditions, made more probable by climate change. Much research has focused on extreme fire weather and its drivers, but natural wildfire regimes—and their interactions with human activities—are far from being comprehensively understood. There is a lack of clarity about the 'causes' of wildfire, and about how ecosystems could be managed for the co-existence of wildfire and people. We present evidence supporting an ecosystem-centred framework for improved understanding and modelling of wildfire. Wildfire has a long geological history and is a pervasive natural process in contemporary plant communities. In some biomes, wildfire would be more frequent without human settlement; in others they would be unchanged or less frequent. A world without fire would have greater forest cover, especially in present-day savannas. Many species would be missing, because fire regimes have co-evolved with plant traits that resist, adapt to or promote wildfire. Certain plant traits are favoured by different fire frequencies, and may be missing in ecosystems that are normally fire-free. For example, post-fire resprouting is more common among woody plants in high-frequency fire regimes than where fire is infrequent. The impact of habitat fragmentation on wildfire crucially depends on whether the ecosystem is fire-adapted. In normally fire-free ecosystems, fragmentation facilitates wildfire starts and is detrimental to biodiversity. In fire-adapted ecosystems, fragmentation inhibits fires from spreading and fire suppression is detrimental to biodiversity. This interpretation explains observed, counterintuitive patterns of spatial correlation between wildfire and potential ignition sources. Lightning correlates positively with burnt area only in open ecosystems with frequent fire. Human population correlates positively with burnt area only in densely forested regions. Models for vegetation-fire interactions must be informed by insights from fire ecology to make credible future projections in a changing climate.We gratefully acknowledge support from the Leverhulme Centre for Wildfires, Environment and Society, who organized the virtual mini-workshop which initiated the writing of this paper. RKN is supported by the Leverhulme Centre. SPH and YS acknowledge support from the ERC-funded project GC2.0 (Global Change 2.0: Unlocking the past for a clearer future, Grant Number 694481). ICP, KJB and ND acknowledge support from the ERC-funded project REALM (Re-inventing Ecosystem And Land-surface Models, Grant Number 787203). JCH acknowledges funding from the ERC project SCATAPNUT (Grant Number 681885). This work is a contribution to the LEMONTREE (Land Ecosystem Models based On New Theory, obseRvations and ExperimEnts) project, funded through the generosity of Eric and Wendy Schmidt by recommendation of the Schmidt Futures program (SPH, YS and ICP)

A distinct ecotonal tree community exists at central African forest-savanna transitions

1. Global change is expected to increase savanna woody encroachment as well as fire spreading into forest. Forest‐savanna ecotones are the frontier of these processes and can thus either mitigate or enhance the effects of global change. However, the ecology of the forest‐savanna ecotone is poorly understood. In this study, we determined whether a distinct ecotonal tree community existed between forest and savanna. We then evaluated whether the ecotonal tree community was more likely to facilitate fire spreading into the forest, woody encroachment of the savanna, or the stabilisation of both forest and savanna parts of the landscape. 2. We sampled twenty‐eight vegetation transects across forest‐savanna ecotones in a central African forest‐savanna mosaic. We collected data on the size and species of all established (basal diameter >3cm) trees in each transect. Split moving window dissimilarity analysis detected the location of borders delineating savanna, ecotone, and forest tree communities. We assessed whether the ecotonal tree community was likely to facilitate fire spreading into the forest by burning experimental fires and evaluating shade and grass biomass along the transects. To decide if the ecotone was likely to facilitate woody encroachment of the savanna we evaluated if ecotonal tree species were forest pioneers. 3. A compositionally distinct and spatially extensive ecotonal tree community existed between forest and savanna. The ecotonal tree community did not promote fire spreading into forest and instead acted as a fire buffer, shading out flammable grass biomass from the understorey and protecting the forest from 95% of savanna fires. The ecotone helped stabilise the forest‐savanna mosaic by allowing the fire‐dependant savanna to burn without exposing the fire‐sensitive forest to lethal temperatures. 4. The ecotonal tree community was comprised of many forest pioneer species that will promote woody encroachment in the savanna, especially if fire frequency is decreased. SYNTHESIS: A distinct fire‐buffering ecotonal tree community in this forest‐savanna mosaic landscape illustrated that savanna fires are unlikely to compromise forest integrity. Conversely, suppression of fire in this landscape will likely lead to loss of savanna as the ecotone becomes the frontier of woody encroachment. Regular burning is essential for the preservation of this forest‐savanna mosaic

Long-term collapse in fruit availability threatens Central African forest megafauna

Afrotropical forests host many of the world’s remaining megafauna, but even here they are confined to areas where direct human influences are low. We use a rare long-term dataset of tree reproduction and a photographic database of forest elephants to assess food availability and body condition of an emblematic megafauna species at Lopé National Park, Gabon. We show an 81% decline in fruiting over a 32-year period (1986-2018) and an 11% decline in body condition of fruit-dependent forest elephants from 2008-2018. Fruit famine in one of the last strongholds for African forest elephants should raise concern for the ability of this species and other fruit-dependent megafauna to persist in the long-term, with consequences for broader ecosystem and biosphere functioning

Robust ecological analysis of camera trap data labelled by a machine learning model

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 patterns (n = 4 species tested) and occupancy (n = 4 species tested) derived from machine learning labels with the same estimates derived from expert labels. 3. Results show that fully automated species 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 a large, completely out‐of‐sample test dataset. Simple thresholding using the Softmax values (i.e. excluding ‘uncertain’ labels) improved the model's performance when calculating activity patterns and estimating occupancy but did not improve estimates of species richness. 4. We conclude that, with adequate testing and evaluation in an ecological context, a machine learning model can generate labels for direct use in ecological analyses without the need for manual validation. We provide the user‐community with a multi‐platform, multi‐language graphical user interface that can be used to run our model offline.Additional co-authors: Cisquet Kiebou Opepa, Ross T. Pitman, Hugh S. Robinso

Real-time alerts from AI-enabled camera traps using the Iridium satellite network: a case-study in Gabon, Central Africa

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

Software, Data & Models used in "Identifying Animal Species in Camera Trap Images using Deep Learning and Citizen Science"

This dataset provides the software, the models, and other data used in "Identifying Animal Species in Camera Trap Images using Deep Learning and Citizen Science". This dataset contains the software to train convolutional neural networks, as well as all models trained for the study and code to apply them on new images. Additionally, data defining the conducted experiments are provided to ensure reproducibility.This study was partially supported by the NSF under award IIS 1619177The development of the Zooniverse platform was partially supported by a Global Impact Award from Google.We also acknowledge support from STFC under grant ST/N003179/1.EE was funded by the University of Oxford’s Hertford College Mortimer May fund

Winners and losers : Tropical forest tree seedling survival across a West African forest-savanna transition

Forest encroachment into savanna is occurring at an unprecedented rate across tropical Africa, leading to a loss of valuable savanna habitat. One of the first stages of forest encroachment is the establishment of tree seedlings at the forest-savanna transition. This study examines the demographic bottleneck in the seedlings of five species of tropical forest pioneer trees in a forest-savanna transition zone in West Africa. Five species of tropical pioneer forest tree seedlings were planted in savanna, mixed/transition, and forest vegetation types and grown for 12 months, during which time fire occurred in the area. We examined seedling survival rates, height, and stem diameter before and after fire; and seedling biomass and starch allocation patterns after fire. Seedling survival rates were significantly affected by fire, drought, and vegetation type. Seedlings that preferentially allocated more resources to increasing root and leaf starch (starch storage helps recovery from fire) survived better in savanna environments (frequently burnt), while seedlings that allocated more resources to growth and resource-capture traits (height, the number of leaves, stem diameter, specific leaf area, specific root length, root-to-shoot ratio) survived better in mixed/transition and forest environments. Larger (taller with a greater stem diameter) seedlings survived burning better than smaller seedlings. However, larger seedlings survived better than smaller ones even in the absence of fire. Bombax buonopozense was the forest species that survived best in the savanna environment, likely as a result of increased access to light allowing greater investment in belowground starch storage capacity and therefore a greater ability to cope with fire. Synthesis: Forest pioneer tree species survived best through fire and drought in the savanna compared to the other two vegetation types. This was likely a result of the open-canopied savanna providing greater access to light, thereby releasing seedlings from light limitation and enabling them to make and store more starch. Fire can be used as a management tool for controlling forest encroachment into savanna as it significantly affects seedling survival. However, if rainfall increases as a result of global change factors, encroachment may be more difficult to control as seedling survival ostensibly increases when the pressure of drought is lifted. We propose B. buonopozense as an indicator species for forest encroachment into savanna in West African forest-savanna transitions.</p