Conservation Biology

Over the past two decades, significant population recoveries of tigers and leopards have driven a substantial rise in human-carnivore conflict across South Asia. Sustaining the long-term conservation of both species demands effective, adaptive conflict management. However, existing research is largely confined to isolated landscapes or protected areas, hindering the identification of spatial conflict drivers across broader, heterogeneous regions. To address this gap, this study compiled 4,457 human leopard conflict (HLC) and 2,563 human tiger conflict (HTC) records spanning 2000 to 2024 across Bangladesh, Bhutan, India, and Nepal. We evaluated conflict locations against 33 landscape, anthropogenic, climatic, topographic, and hydrological predictors across three spatial scales using binomial generalized linear regression models and three alternative background sampling methods. Model performance peaked at the largest spatial scale for both species and remained consistent across sampling strategies. HTC correlated positively with forest fragmentation and proximity to human settlements, but decreased with higher overall anthropogenic disturbance. Conversely, HLC concentrated in highly productive, high-precipitation, human-modified landscapes. These contrasting spatial patterns highlight distinct ecological responses to habitat modification and carry unique conservation implications. Ultimately, this study underscores the need to integrate carnivore abundance, wild prey densities, and socioeconomic factors to achieve a holistic understanding of conflict dynamics.

Information on the long-term dynamics of species distributions is essential for assessing global biodiversity change and its causes, and for informed conservation decisions. A valuable source of such historical information are atlases. They provide spatial standardisation, extensive geographic coverage, temporal replication, documented sampling protocols, and span multiple species. Here we present model-based occupancy probabilities for 1,055 bird and 58 butterfly species, derived from nine gridded atlas datasets spanning several editions over the past 50 years. These are: Japan, New Zealand, New York state (US), the states of Alberta, Quebec, and The Maritimes (Canada), Czech Republic, United Kingdom, and Europe. Despite standardisation and coordination during data collection, the raw atlas data still contain some variation in sampling effort. We accounted for this using a two-step process: (1) we estimated a proxy for sampling effort in each grid cell within each atlas period using the Frescalo Algorithm, and (2) we ran a Bayesian spatial occupancy model for each species and atlas period, using the estimated sampling effort as the single predictor of detection in the observation process of the model. As the main product, we provide occupancy probabilities for species across grid cells and atlas periods. We also report uncertainty around each probability estimate and flag ~21% (rare) species per atlas for which the model did not fit well. The probability distributions are fit for purpose and served in a user-friendly database with standardised geographic projection, metadata, and taxonomy.

Despite the popularity of carbon credits as a tool to offset greenhouse gas emissions, there are growing concerns over whether their climate mitigation claims are robust. Most attention has focused on natural risks to forest carbon projects, such as wildfire, while social risks (e.g., those driven by fraud, mismanagement, and land conflicts) remain largely unquantified. Here we analyze 244 forest-based carbon projects to assess the vulnerability of the voluntary carbon market to social risks and find that projects systematically underestimate their risk of failure. Even under optimistic assumptions, projects are roughly twice as likely to fail within 25 years as their self-assessed risk implies over a 100-year horizon. Projecting these trends forward, we show that credits issued to failed projects could exceed the reserves registries hold to mitigate these risks within 1-2 decades. Overall, our results suggest stronger mechanisms and reserves are needed to ensure carbon credits deliver the promised climate benefits.

1. Occurrence records underpin biodiversity analyses, but their coordinates are often erroneous or missing. Misplaced coordinates distort environmental predictors and range estimates used in conservation assessment. Existing tools flag suspect records or georeference locality text, but rarely correct coordinates, provide calibrated uncertainty, or quantify downstream consequences. 2. We present GeoMend, a deterministic, rule-based method that flags and corrects suspect coordinates and georeferences records lacking coordinates from locality text, assigning each a precision tier or uncertainty radius. It uses no machine learning or artificial intelligence. We evaluate it against expert coordinates for 16,913 records and 736 species on Hispaniola, compare georeferencers using 5,000 localities from 72 countries, and measure changes in environmental predictors and the extent of occurrence. 3. On held-out Hispaniola records, GeoMend flags 92% of coordinates more than 5 km from expert truth, versus 27% for CoordinateCleaner. Among the accepted corrections with paired coordinates, the median error falls from 2.19 to 0.82 km, and 68% of the complete held-out pairs improve. On records placed by both GeoMend and GEOLocate that also have original coordinates, the median error is 0.67 km for GeoMend versus 10.69 km for GEOLocate. For records lacking coordinates, evaluated uncertainty radii contain expert truth about 92% of the time in well-supported gazetteer classes. Correction reduces the mean standardized environmental distance from expert truth by 38% and moves the extent of occurrence toward expert truth by a median of 8% (mean of 79%). Corrections and newly georeferenced records give 17 Hispaniola species their first range estimate. 4. On the global benchmark, GeoMend is more accurate than GPT-4o for two-thirds of the records placed by all four comparators, with median errors of 4.4 and 10.8 km, respectively. Applied to Global Biodiversity Information Facility (GBIF) records lacking coordinates, it places records from 82,180 previously unmapped species across 233 countries, ranging from precise locality interpretations to administrative centroids. Automated placement makes the global backlog more approachable, while explicit tiers distinguish uses supported by precise and coarse coordinates. Further georeferencing requires regional expertise to combine automated placement with local knowledge.

Human-wildlife conflicts over shared resources are often interpreted as direct competition, whereby resources consumed by wildlife are unavailable to people. Yet, individuals of wild populations can differ in their propensity to exploit resources, and the demographic, ecological, and economic consequences of such behavioural heterogeneity remain poorly understood. We developed an integrated demographic-bioeconomic model of Atlantic cod (Gadus morhua), grey seals (Halichoerus grypus), and a coastal gillnet fishery. Stage-structured population models incorporated density dependence, predator-prey interactions, adaptive harvesting, and transitions of adult seals into a bold, depredating state. We compared four scenarios of increasing complexity over 50 years across 100 coupled demographic realisations and examined sensitivity to the energetic contribution of depredated catch, harvest levels, and social amplification of depredation behaviour. Cod and seals persisted in all demographic realisations, supporting our ecological-persistence hypothesis. Contrary to our predictions, however, adding bold seals neither reduced long-term fishery performance nor destabilised the system. Median net present value was approximately three-fold greater in the full system than without depredation, and was greater in 76% of paired realisations. The full system was also the most temporally stable: median cod variability declined by 74% relative to the otherwise equivalent fishery without depredation, while seal variability declined by 45%. Behavioural depredation therefore altered predator population structure and imposed direct catch losses, but these effects were counterbalanced by indirect demographic feedbacks. Synthesis and applications. Depredating predators should not necessarily be viewed simply as competitors that reduce contested resources. Behavioural heterogeneity can introduce compensatory pathways that alter population stability and long-term economic outcomes. Management should therefore consider not only predator abundance and immediate catch losses, but also how harvest creates opportunities for behavioural change and how those changes propagate through coupled ecological-economic systems.

We used Scopus data on 243949 top-cited scientists to map scientific cosmopolitanism defined as having at least two different countries that contribute alone at least 10 percent of an author published record as indexed in Scopus. 20.4 percent of the top-cited authors were cosmopolitan based on this definition. Based on current author affiliation, rates varied widely from 9% to 76% across countries with at least 250 top-cited authors. Countries with few top-cited authors had almost always high rates of cosmopolitanism. The proportion of top-cited authors who are cosmopolitan has steadily increased over time in younger generations among those who started publishing after 1965 reaching 43% recently. Large differences were also seen across scientific subfields from 1% in General Psychology and Cognitive Sciences to 46% in Econometrics. Across the 30 country pairs that were most over-represented, countries shared borders, languages, most common religion pairs, or any of these features in 15, 19, 26, and 29 cases, respectively. The map of cosmopolitan authors reveals a large and increasing proportion of the most influential scientists who share two or more scientific homes that each has made a substantial contribution to their career. Cosmopolitanism among scientists requires better study to help facilitate its potential positive contributions not only to individual scientists but to science and society more broadly.

Global biodiversity recovery demands restoration at landscape scales, where ecological outcomes are shaped not only by habitat amount but also spatial arrangement. Alternative landscape restoration strategies can increase habitat amount, improve quality, and alter configuration in fundamentally different ways and may therefore vary in their efficiency. Here, we conceptualise five landscape restoration strategies and use a spatially explicit metapopulation framework to compare their ability to increase predicted species persistence across heterogeneous landscapes and trait space defined by dispersal ability and specialisation. We find that restoration outcomes are strongly trait-dependent, with restoration of existing degraded habitats yielding the highest benefits - particularly for specialists. Improvement of the landscape matrix to create low-quality habitat primarily benefits generalists, while strategies creating new habitat patches or enlarging patches provide more consistent but modest gains. These results reveal fundamental trade-offs among restoration strategies and highlight the utility of trait-explicit approaches to inform their implementation and evaluation.

1. Assessing the conservation value of secondary forests and reforestation is important for conservation planning. Butterflies are widely used as indicators, and fruit-feeding butterflies are often used in biodiversity assessments in tropical landscapes. 2. We examined fruit-feeding butterfly diversity, abundance, and community composition across a primary forest, a secondary forest, and a plantation forest in northern Vietnam. We sampled butterflies nearly monthly during one year using baited traps. 3. Primary and secondary forest supported higher species richness than plantation forest. Moreover, while secondary forest had mainly a subset of species found in primary forest, the plantation forest had a distinct butterfly fauna. 4. Butterfly abundance and community composition varied across months, without showing a clear seasonal pattern. 5. Our results suggest that secondary and plantation forests do not fully replace primary forests, but they contribute meaningfully to butterfly conservation in human-modified tropical landscapes.

Aim: To test whether individual movement mechanisms inferred from animal tracking data can be scaled up to explain landscape-scale patterns of population density and improve predictions of population redistribution under future environmental change. Location: Texas Gulf Coast, USA Time Period: 2009-2018 Major Taxa Studied: Whooping Crane (Grus americana) Methods: We parameterized individual-level step-selection functions using 14,898 GPS locations from 22 Whooping Cranes and propagated the resulting movement and habitat-selection mechanisms across the landscape to derive steady-state distributions of space use. We compared these movement-informed predictions with correlative species distribution models fitted to the same data. Predictions were evaluated using withheld tracking observations and fully independent population-level aerial-survey data. We then projected both approaches under sea-level-rise scenarios to assess how future habitat loss and fragmentation alter crane space use. Results: Movement mechanisms derived from individual-level tracking data captured the mechanistic dependence of population density on habitat quality and spatial configuration. When projected across the landscape, these mechanisms reproduced fully independently observed density patterns and outperformed conventional species distribution models. Because predictions arose from movement mechanisms rather than occurrence-environment correlations, movement-informed models also captured the effects of habitat reconfiguration under sea-level rise. They projected pronounced population redistribution and an expansion of core space-use areas of up to 80% which conventional models missed. Main Conclusions: Individual movement mechanisms derived from biologging data can scale up to explain current population structure and predict future redistribution under environmental change. By mechanistically accounting for how habitat configuration constrains accessibility, movement-informed models reveal severe impacts of fragmentation that are missed by conventional distribution models. Rapidly expanding biologging datasets therefore offer a scalable basis for connecting individual behavior to macroecological patterns and improving biodiversity forecasts in changing landscapes.

1. Common dolphins (Delphinus delphis) are the most abundant cetaceans off mainland Portugal and a species of conservation and bycatch concern, so reliable abundance estimates are a management priority. Yet no single survey provides adequate spatiotemporal coverage: dedicated programmes offer sparse snapshots, while opportunistic programmes are spatially constrained by their routes. Integrating them is methodologically demanding, and the existing methods that do so in a single model estimate the density of animal groups, not of the individual animals that management often requires. 2. We develop a spatiotemporal marked log-Gaussian Cox process that integrates four structurally distinct surveys through a shared intensity surface, comprising environmental covariates, a latent spatial field, and survey-specific detection functions. Group size enters as a zero-truncated negative binomial mark with its own spatiotemporal field. We apply it to a dedicated aerial survey and three opportunistic ship-based programmes off mainland Portugal, comprising 162,514 km of on-effort transects and 1,756 sightings of 17,000 animals. We fit the model in a Bayesian framework with integrated nested Laplace approximations, yielding seasonally resolved group and animal density surfaces across the Portuguese Exclusive Economic Zone, and abundance estimates with fully propagated uncertainty. 3. Predicted animal density concentrated over the shelf and upper slope in every season, peaking northwest, and increased in shallower, cooler, more productive waters and over steeper seabed. Abundance nearly doubled from summer (77,000 [95% credible interval: 60,000, 98,000]) to winter (146,000 [99,000, 208,000]). Seasons shared 56% of the spatial field's variance, so effort from any programme in any season informed all others, and the seasonal contrast remained estimable despite uneven coverage. 4. Synthesis and applications. Our framework allows managers to combine dedicated surveys with existing platforms of opportunity to estimate animal density by season. Because group size is modelled explicitly, densities are estimated in animals rather than groups, as required for bycatch risk assessment and marine spatial planning. Because each survey enters through its own observation model, the approach accommodates programmes that differ in platform and protocol and transfers to any species or region where several surveys each cover part of a population.

Climate change will alter habitat suitability for plant communities in mountain ecosystems of the Western Himalaya. There will be contrasting magnitudes and spatial configurations of these changes for different plant species, and it is an important question how to translate these contrasts into effective climate-proof multi-species conservation planning. This study assessed current habitat associations and projected changes in suitable habitat for two contrasting species, Meconopsis aculeata and Rhododendron campanulatum, in Himachal Pradesh, India. Field surveys and spatially filtered occurrence records were combined with MaxEnt modelling using 10 non-collinear environmental predictors. Habitat suitability was projected to 2050 and 2070 under Shared Socioeconomic Pathways; SSP1-2.6, SSP2-4.5 and SSP5-8.5 using HadGEM3-GC31-LL. Field observations showed contrasting habitat associations, with M. aculeata occurring mainly in open alpine herbaceous communities and R. campanulatum in subalpine woody communities. Elevation was an important predictor for both species while climatic predictors contributed differently to their relative importance and response patterns. Future projections indicated reductions in suitable habitat of approximately 25% for M. aculeata and 27-28% for R. campanulatum. Habitat-change maps showed heterogeneous patterns of loss, gain and persistence rather than uniform contraction, and centroid displacement remained small (7.7-8.5 km). We performed proximity analyses to show which currently suitable areas are most close to areas of future gain, in order to support the design of climate-proof protected areas. We discuss how interspecific differences in response are relevant to multi-species conservation planning that considers the spatial relationship between current high-suitability areas and the gain, loss and redistribution of future suitable habitat.

Climate change poses a major threat to marine mammal populations, many of which remain vulnerable after years of overexploitation during the 17th to 20th centuries. Extreme climate events have grown in both frequency and severity in recent decades, and this trend is projected to accelerate under continued greenhouse gas emissions. Here, we assess how this intensification affects a top marine predator at the epicentre of extreme El Nino events - the Peruvian population of Otaria byronia (South American sea lion). We first apply a Bayesian population model that best predicts observed population dynamics to reconstruct female O. byronia population trends, estimate vital rates, and evaluate the effects of environmental drivers on survival and reproduction. We then use the same model to project extirpation risk under multiple future climate scenarios reflecting anticipated increases in both the intensity and frequency of extreme El Nino events. Our reconstruction reveals that the Peruvian O. byronia population has shown considerable resilience to episodic El Nino events over the past quarter-century, likely sustained by high female survival during normal and favourable years. However, under a scenario of moderate increases in El Nino frequency and intensity, the probability of extirpation over the next 50 years is 83.4% higher than under current conditions. As disturbance regimes intensify globally, we suggest that resilience-oriented management frameworks, grounded in species-specific demographic data and environmental context, as demonstrated here, will be essential for safeguarding marine mammal populations against an increasingly volatile climate.

Urban protected areas (PAs) have become important habitats for wildlife in rapidly expanding human-dominated landscapes. These PAs may provide refuge and continuously abundant resources, which may enable some adaptable carnivores to persist and attain high abundances. However, little is known about the population ecology and interactions between carnivores sharing such spatially restricted and disturbed urban habitats. We evaluated the densities and spatio-temporal interactions of leopards (Panthera pardus) and striped hyenas (Hyaena hyaena) in Jhalana-Amagarh Conservation Reserve, a protected area (PA) embedded within the metropolis of Jaipur, India. During May-July 2025, we deployed 30 paired camera-traps and used spatially explicit capture-recapture models to estimate density and individual activity centres. We further quantified their diel activity and temporal overlap using kernel density estimation. Leopard density was estimated at 7.03 {+/-} 1.31 individuals/10 km sq and striped hyena density at 4.06 {+/-} 0.96 individuals/10 km sq, with activity centres of both species occurring in close proximity and frequently towards the reserve periphery. Both species were predominantly nocturnal and showed high temporal overlap ({Delta}4 = 0.801, CI=0.73-0.86). Our results report exceptionally high leopard and striped hyena densities in an urban PA and exemplify the importance of small PAs within rapidly expanding cities, stressing the need for finer-scale studies more broadly to understand the mechanisms shaping interactions between the two species. However, such systems require careful management of food subsidies, habitat quality and connectivity along with long-term monitoring of population viability, disease dynamics and human-wildlife conflict.

Management of marine protected areas (MPAs) require long-term monitoring and evaluation that is often limited by the cost and effort of conventional visual based surveys. We evaluated a community science-based eDNA approach to MPA monitoring over two years in the Point Dume State Marine Reserve, the Point Dume State Marine Conservation Area, and two paired unprotected reference sites. Across the sampling period, we detected 119 species of fish and marine mammals, including 19 of the 22 regional management-priority species for Southern CA MPAs. Protected and unprotected comparison sites were broadly similar in overall assemblage with 93 species shared across all sites. Multivariate analyses revealed site-level differences, although season and year explained more variation than protection status. Species presence also shifted during a La-Nina transition, highlighting the importance of long-term monitoring under different climatological contexts. Community scientists greatly expanded the temporal and spatial scope of eDNA sampling and generated high-quality data. Subsampling analysis showed that quarterly sampling, with three sample replicates and sequencing depths between 700k-990k, recovered 95% of observed biodiversity of the full time series. These results demonstrate that a community science-based eDNA program could provide a standardized, scalable, low-cost, and management-relevant approach for long-term monitoring across California's MPA network.

Land-use and climate change are dominant drivers of terrestrial biodiversity change, yet are usually analysed separately although they co-occur and may interact. We integrated global bird assemblage records from PREDICTS with temperature anomalies and species' ecological characteristics to test how land use modifies biodiversity responses to climate warming across three dimensions: local abundance and richness, compositional dissimilarity among assemblages, and species occurrence across ecological groups. Land use modified warming associations for both abundance and richness. Relative to primary vegetation, warming associations were more positive in low-intensity agriculture for both responses, but more negative for richness in high-intensity agriculture and urban land. Predicted richness remained below the reference, defined as primary vegetation at temperature anomaly 0, across all modified land uses, with the largest declines in high-intensity agriculture (-17.9%) and urban land (-21.8%). Within land-use classes, greater warming exposure was generally associated with higher compositional similarity, consistent with warming associated biotic homogenisation. Occurrence models revealed ecological filtering: broad-niched generalists were favoured in warmer modified habitats, whereas range-restricted habitat specialists were filtered out under intensive land use and warming. Conservation assessments should therefore integrate land-use-warming interactions with changes in assemblage composition and ecological strategies, particularly in agricultural and urban landscapes where warming further disadvantaged range-restricted specialists.

Environmental concerns and the need to mitigate the effects of climate change have driven the adoption of renewable energy sources, such as offshore wind farms (OWFs), making it essential to assess their overlap with seabird habitats to estimate potential impacts. This study is the first to estimate the vulnerability of coastal and seabirds to OWFs at a national scale by integrating biological trait data from the literature, spatial occurrence records from citizen science platforms, and expert knowledge (elicited from 23 specialists) into established quantitative risk frameworks. Collision risk and susceptibility scores were calculated at the family and subfamily levels, as well as disturbance and displacement indices by species and susceptibility by state. The groups with the highest collision risk and susceptibility were Fregatidae (0.354; 0.486), Sulidae (0.199; 0.419), Sterninae (0.190; 0.384) and Diomedeidae (0.190; 0.416). The extreme risk for Fregatidae is driven by their specific flight characteristics, with impacts concentrated in the highly frequented southeast and northeast regions. Regarding disturbance and displacement, Limnodromus griseus exhibited the highest national score (MMA: 0.098), while Diomedea dabbenena had the highest global score (IUCN: 0.092), highlighting the critical need to integrate both regional and global conservation statuses. Spatially, the state exhibiting the highest overall vulnerability to OWF impacts was Rio Grande do Sul (23.78; 10.69), a critical hotspot characterized by high species richness and oceanographic productivity. These findings establish an indispensable national baseline for Marine Spatial Planning, reinforcing the need for targeted pre-installation assessments, the identification of sentinel species, and spatially differentiated management during environmental licensing processes.

Forests are known to burn and desiccate under a changing climate; less appreciated is that they also drown. Across much of the globe, a warming atmosphere is amplifying the length and intensity of both droughts and floods. Whereas massive die-offs from drought and fire are widely studied as consequences of climate change on forests, inundation-induced mortality remains poorly quantified. Using approximately 1 m resolution aerial imagery and deep learning, we tracked over 260 million dead trees across the United States' Great Lakes and ocean coastlines from 2012 to 2023. Within 10 km of the coasts, more than half of mapped mortality was concentrated in just 10% of forests, primarily in high-elevation inland wetlands and low-lying forests prone to inundation. Mortality rates doubled within a decade around expanding freshwater lakes and in inland wetlands experiencing intensifying regional-scale precipitation, while tripling in low-lying coastal forests where salinization compounded inundation in sea-level rise hotspots. Our findings identify rising waters as a widespread, underappreciated driver of forest die-off that can extend far inland, underscoring the need to reassess the impacts of climate change on the functioning, demography, and carbon-methane budgets amongst the world's most carbon-rich forests as seas rise and hydroclimate shifts globally.

Disturbances alter ecosystem resilience by killing organisms and leaving behind material legacies. We tested how material legacies from marine heat waves and cyclones govern transitions between coral- and seaweed-rich states on tropical reefs. Heat waves kill coral tissue but leave their skeletons to erode gradually, whereas cyclones scour away emergent biota. Field experiments revealed that dead skeletons protect seaweeds from herbivory, catalyzing a transition to a seaweed-rich state that becomes self-sustaining after skeletons disappear. After four years, 73% of skeleton-added (heat wave) plots transitioned to seaweeds compared to 0% of scoured (cyclone) plots that lacked skeletons, while 87% of seaweed-rich plots scoured to simulate a cyclone stayed seaweed-free. Reef trajectories after natural disturbances were similar: four years after heat wave-induced coral mortality, 63% of time-series plots had transitioned to seaweeds with none recovering to coral; four years after a cyclone removed skeletons from a prior disturbance, none had transitioned to seaweeds and 60% had returned to coral. A disturbance not only can displace an ecosystem state, it can rapidly reshape the resilience landscape through the material legacies it creates or removes. Our findings also show that successive disturbances need not have cumulative negative effects: after a reef is driven into a seaweed-rich state, a cyclone can act as a rescue disturbance by removing established seaweeds and, when structural refugia are also removed, shrinking the seaweed stability domain. By reshaping resilience through contrasting material legacies, disturbance type and sequence can be as important as frequency and severity in determining ecosystem trajectories.

Reliable estimates of population size are fundamental to understanding wildlife population status and informing conservation. Obtaining such estimates remains challenging for unmarked species, particularly in tropical countries where logistical and financial constraints limit intensive monitoring. The endangered dhole (Cuon alpinus), a highly social canid without natural markings, is one such species for which robust population estimates remain scarce. We estimated dhole density in the Valparai plateau, a human-dominated agroforest-forest mosaic in India's Western Ghats. We compared Spatially Explicit Capture-Recapture (SECR) models based on individual identification from fecal DNA and Space-to-Event (STE) models based on camera-trap detections. We additionally applied both approaches to individually identifiable leopards (Panthera pardus) to cross-validate STE versus SECR estimates. SECR estimated dhole density at 22 individuals per 100 sq.km, with STE estimates varying substantially depending on how the camera viewshed area was defined. STE estimates based on post hoc viewshed measurements, particularly after reducing the viewshed by 10%, were comparable to SECR estimates. In contrast, STE underestimated leopard density relative to SECR (7 versus 12 per 100 sq.km), although uncertainty intervals overlapped. Ecologically, our results indicate that human-use agroforest mosaics can support dhole densities comparable to, or exceeding, those reported from several Protected Areas, highlighting their potential importance for dhole conservation. More broadly, we argue that STE models may offer a practical and relatively inexpensive approach for baseline monitoring of dholes from camera-trap surveys, but their reliability depends strongly on accurate characterization of the effective viewshed and may vary across species and landscapes.

This study examined the spatial distribution of potential human encounters with dangerous large mammals in and adjacent to the Ifakara-Lupiro-Mang'ula Wildlife Management Area (ILUMA WMA) and Nyerere National Park (NNP) in southern Tanzania. Using published survey data collected over one year, this secondary analysis focused on four potentially dangerous large mammals: elephant (Loxodonta africana), buffalo (Syncerus caffer), hippopotamus (Hippopotamus amphibius) and hyena (Crocuta crocuta). Potential encounter risk was estimated using overlap indices based on co-occurrence of signs of wildlife and human activities. Potential encounter risk was found to be concentrated around readily identifiable foci within the study area and differed between species and distinguishable human livelihood activities. Potential for encountering the two most apparently important dangerous animals, elephant and buffalo, was closely associated with authorized fishing camps within the ILUMA WMA and the ecotone between substantially disturbed and largely intact natural land cover. The greatest potential for encountering hippopotamus occurred largely around two river beds, while hyena encounter potential was more closely associated with established villages and converted lands close to the western boundary of the ILUMA WMA. These new maps of hazard and risk suggest that effective human-wildlife conflict (HWC) mitigation in this WMA is unlikely to arise from any stand-alone intervention, but from targeted actions that reduce spatiotemporal overlap between people and wildlife by keeping them apart and/or protecting them against each other. Overall, this study demonstrates that mapping potential encounter risk based on spatiotemporal overlap of indirect activity indicators like tracks, spoor and signs provides a proactive and statistically powerful way to identify where hazardous encounters are most likely to occur, offering considerable advantages over retrospective assessments of much sparser observations of actual realised conflict events after they have already occurred. This new spatial risk mapping approach provides a framework for shifting management from reactive responses after conflict occurs towards proactive prevention.