Population Ecology

Environmental variability can strongly alter coexistence among competing species and their extinction risk, particularly when population dynamics are shaped by behavioral interactions, such as fear. In this work, we develop a novel stochastic differential equation competition model that incorporates both non-consumptive fear effects and environmental variability to investigate how behavioral interactions influence species coexistence under random fluctuations. Our result reveals that environmental stochasticity can drive species to extinction even when the corresponding deterministic system admits coexistence. In particular, under an explicit stability condition on the fear and competition parameters and sufficiently strong averaged noise intensities, we prove that both competing species become extinct exponentially almost surely. Conversely, we derive a stochastic persistence criterion in terms of fear, competition, and noise-induced suppression parameters for the fearful species. We further demonstrate that environmental noise may reverse classical competition-exclusion outcomes, leading to qualitatively different long-term dynamics from those predicted deterministically. These results provide rigorous thresholds separating stochastic extinction from persistence and highlight the critical role of environmental variability in fear-mediated competitive ecosystems. From an applied perspective, these results provide insight into how behavioral interactions and environmental variability influence species survival, with potential applications in ecological management and conservation.

In order to feed a growing global population without silencing nature, conceiving agricultural management strategies reconciling yield and conservation goals is key. Using numerical simulations of a metacommunity model, we explore the possibilities for compromise offered by spatial management strategies of farmed areas. Each strategy is characterized by its farming intensity, the proportion of farmed lands and their spatial aggregation. We show that achieving equitable yield-biodiversity compromise is difficult. While conciliatory strategies offering top yield and biodiversity are typically not possible, accepting slightly lower yields (ie, "Pretty Good Yield strategies") allows to recover substantial biodiversity. Such reconciliation possibilities are limited for species with small dispersal. Yield increases mainly through farmland expansion, whereas farming intensity strongly influences biodiversity, increasing it at low intensity before decreasing with further intensification. Finally, we demonstrate that reconciliation is easier if agricultural production relies on biodiversity through ecosystem services.

Effective conservation depends on demographic metrics that reliably reflect species status, particularly population abundance. For elusive species occurring at low densities, however, such metrics remain difficult to obtain. Spatial capture-recapture (SCR) models are the standardized approach for estimating density in marked populations, but their data requirements, especially the need for multiple spatial recaptures across individuals, often limit applicability in small or data-poor populations. This constraint has resulted in knowledge gaps for some of the most vulnerable species, undermining evidence-based conservation planning and management. Using camera-trap data and SCR-derived density estimates from data-rich populations, we evaluated alternative, less data-demanding metrics and tested the hypothesis: Space to Event (STE), Mean Local Abundance (MLA), and Relative Abundance Index (RAI) exhibit predictable relationships with SCR-derived density; if supported, these metrics can reliably estimate density in populations where SCR models cannot be implemented. We applied this framework to the European wildcat (Felis silvestris), an elusive small felid with highly fragmented populations across Europe, for which density estimates are largely lacking despite growing conservation concern. Across 21 study areas spanning most of the species' range, our results indicate that European wildcats generally occur at lower densities than previously reported. SCR-derived estimates (n=10) averaged 10.32 {+/-} 11.56 inds/100km2, while STE enabled density estimation in five additional data-poor areas (mean 5.52 {+/-} 5.33 inds/100km2). STE showed a strong linear relationship with SCR-derived density (R2=0.98), supporting its use as a viable alternative when SCR is infeasible, although it tended to underestimate compared to SCR, especially at higher densities. In contrast, MLA and RAI showed weaker and non-linear relationships with SCR-derived density (R2=0.65), indicating substantially lower explanatory power and suggesting their estimates are more strongly influenced by confounding processes. By explicitly calibrating alternative metrics across a wide density gradient throughout most of the species' distribution, this study provides a transferable methodological framework for estimating density in low-density wildlife populations and the first continent-wide, standardized density assessment of a carnivore species. From a management perspective, our findings identify populations that may be most vulnerable, particularly those with the lowest densities, and highlight the need to prioritize absolute abundance monitoring.

Despite the ecological importance of ungulate migrations, we lack a complete understanding of why some ungulates migrate and others do not. Though progress has been made towards understanding differences across species and between populations, migratory behavior varies even within populations: in many populations, some individuals remain behind as residents (partial migration). Theoretical population-level work has suggested that these different migratory tactics can coexist, but such approaches stop short of providing insights into how individuals make the decision to stay or go each year. Using long-term data from three ungulate populations, we find that individuals probabilities of migrating are highly variable across years, which points to a non-trivial context-dependent decision-making process, whose underlying mechanisms must be probed via individual-level modeling. Drawing on existing knowledge, we propose a decision-making model of ungulate migration onset wherein individuals probabilistically decide to start migrating based on the local intensity of environmental and/or social cues. Residents arise as a robust collective organization phenomenon in our model. At sufficiently large population sizes, the number of residents is invariant with total population size, consistent with empirical patterns. Instead, resident numbers are influenced by the severity of the bad season, by relevant character differences among individuals, and by how individuals contribute and respond to environmental and/or social cues; for instance, when social cues contribute to decision-making in addition to environmental ones, fewer residents result, and migration is more likely to be complete. Overall, our model provides a potential mechanistic explanation for how residents might emerge within migratory ungulate populations.

Under complex perturbation regimes, biodiversity dynamics show temporal variability in species and community abundance around long-term population trends. Many species indeed show long-term declines while other species increase, putting natural communities far from stationary regimes, while variability is often studied near equilibrium. We contribute to bridging this gap by investigating population and community variability during long-term trends caused by press perturbations in stochastic models of population dynamics. By estimating the deterministic changes in mean and variance during the transient regime, we show that population variability deviates from stationary expectations. Moreover, the deviation strongly depends on the sign of the population trends: increases generate excesses of variability while declines generate deficits. Scaling up to community variability, we propose a decomposition of community variability deviation, allowing to highlight that community variability in the transient regime depends on how the press perturbation is distributed within species relative abundances and growth rates. These results challenge the equilibrium assumption and open new perspectives for the study of the variability of ecological systems under multiple perturbation types.

Predators are often considered regulators of disease in prey populations, a concept central to the "healthy herd hypothesis". This hypothesis suggests that by preferentially removing infected individuals, predators can reduce parasite prevalence. However, predators may also act as disease vectors, facilitating the spread of parasites. We investigated whether stickleback fish (Gasterosteus aculeatus) can act as vectors for the transmission of the obligate bacterial parasite Pasteuria ramosa to its Daphnia host, a widespread freshwater zooplanktor. We fed infected D. magna to sticklebacks, and subsequently analysed faecal samples for the presence, viability, and infectivity of parasite transmission stages (= spores). We recovered approximately 60% of the consumed spores from fish faeces and these spores did not suffer from reduced infectivity to D. magna. Additionally, spores associated with sloppy feeding did not reduce infection rates. Thus, consumption of infected hosts by fish does not eliminate the parasite, but in contrary, may contribute to the spread and persistence of P. ramosa in natural populations, potentially influencing parasite dynamics in natural freshwater ecosystems.

Habitat degradation and biodiversity loss in the Black and Azov Seas necessitate improved tools for spatially explicit conservation planning. We employed stacked species distribution modelling (SSDM) to assess habitat quality for the three resident cetacean species, the common dolphin (Delphinus delphis ponticus), the bottlenose dolphin (Tursiops truncatus ponticus), and the harbour porpoise (Phocoena phocoena relicta), which serve as apex predators and indicators of ecosystem health. Occurrence data were compiled from the Global Biodiversity Information Facility (GBIF), and ensemble species distribution models (ESDMs) were constructed using nine algorithms within the SSDM framework, with eight environmental predictors extracted from Bio-ORACLE v3.0. Individual ESDMs demonstrated excellent predictive performance (AUC: from 0.82 to 0.83; TSS: from 0.65 to 0.67; prop.correct: from 0.82 to 0.83). However, the initial continuous stacking method (pSSDM) yielded low community-level prediction success (0.36), prompting evaluation of three correction approaches. The Probability Ranking Rule (PRR) substantially improved performance (prediction.success = 0.459, sensitivity = 0.704, Jaccard = 0.465), effectively mitigating the overprediction bias inherent in stacked models. Species richness mapping identified multi-species hotspots along the southwestern Black Sea shelf, the Crimean coast, the Kerch Strait, and parts of the eastern coast, while the deep central basin exhibited the lowest richness. Variable importance ranking revealed bathymetry as the primary community-level driver (41.2%), followed by dissolved oxygen (13.8%), sea surface temperature (11.9%), and salinity (10.4%). Species-specific importance patterns confirmed ecological niche segregation, with common dolphins favouring deeper offshore waters and bottlenose dolphins and harbour porpoises associated with shallower shelf environments. The moderate richness observed in the highly productive northwestern shelf, despite high nutrient inputs, may reflect a combination of natural factors (elevated turbidity, reduced salinity) and anthropogenic pressures (fisheries bycatch, shipping, coastal development, and military activity) that limit species co-occurrence. Our findings demonstrate that PRR-corrected SSDM provides a robust framework for mapping cetacean habitat quality and identifying conservation priorities in the Black and Azov Seas, offering an evidence-based tool to inform ecosystem-based management in this ecologically unique and increasingly pressured marine region.

While emerging diseases pose a global threat to amphibians, the dynamics of understudied vector-borne blood pathogens remain poorly understood. Pathogen occurrence is driven by a combination of environmental, ecological, and phylogenetic factors, yet how these drivers shape blood pathogen communities in tropical amphibians is largely unknown. In this study, we used molecular screening and phylogenetic linear models (PGLMMs) to evaluate how climate and ecomorphology influence the incidence of three blood pathogen groups (Trypanosomatidae, Hepatozoon, and Rickettsia) in wild anurans from a protected area in the Brazilian Atlantic Forest. Among 93 individuals sampled, over 93% were infected with at least one pathogen. Trypanosomatidae was the most common (76.3%), followed by Rickettsia (69.9%) and Hepatozoon (16.1%). Pathogen responses to temperature were contrasting: Hepatozoon occurrence increased in warmer periods, while Trypanosomatidae declined. Furthermore, rheophilic species showed a lower probability of Rickettsia infection, providing the first evidence that microhabitat use influences blood pathogen dynamics in amphibians. Our findings demonstrate that hemoparasites prevalence is driven by a multifaceted interplay of variables, highlighting that conservation strategies must account for these pathogen-specific responses to habitat use and environmental change, even within protected areas.

Climate change is expected to alter plant populations not only through direct environmental shifts but also via changes in biotic interactions, such as with herbivores and pathogens. As plant species are also expected to differ in their responses to both climate and antagonists, plant responses to both factors are expected to be variable and species-specific. To assess whether interactive effects of climate and antagonists on plant population dynamics are common and whether the strength and direction of plant responses vary across species, we conducted a multi-year field experiment that manipulated realistic climate change and experimentally reduced insect herbivores and fungal pathogens. We measured responses of plant vital rates, such as survivorship, growth, and reproduction across six grassland species. Using Integral Projection Models (IPMs) and Life Table Response Experiments (LTREs), we quantified changes in population growth rate across experimental treatments and the contribution of each vital rate to that observed change. Two of the study species declined so drastically over the course of the experiment that demographic quantification of population growth rates was not possible. From the remaining species, Bromus erectus and Plantago lanceolata show significant interactive responses of climate and antagonist reduction on population growth rates. In contrast, Dianthus carthusianorum and Tragopogon orientalis showed limited responses to experimental treatments. Notably, our results indicate that in some species biotic interactions may amplify the effects of climate change: the presence of plant antagonists exacerbates the negative effects of the future climate treatment on plant population dynamics. Our findings highlight the complexity in predicting plant population responses to climate change and provide insights for grassland management under future environmental conditions.

Nepal's rangelands provide multiple benefits, including support for pastoral livelihoods and alpine biodiversity, regulation of water and soil nutrients, and sequestering carbon. Climate change and anthropogenic pressures are altering these rangelands, leading to vegetation and biodiversity change. However, national-scale assessments of rangeland change are limited in Nepal. This study quantified rangeland changes at multiple spatial scales and assessed the climatic and non-climatic drivers of rangeland change. About 80.7% of Nepal's high-altitude rangeland (> 2,000m) outside protected areas showed no significant change. Among areas exhibiting significant annual maximum NDVI trends, 383,281 ha (18.6%) showed positive and 14,702 ha (0.7%) showed negative trends, corresponding the ratio of increase in vegetation greenness and decline in vegetation greenness to 26:1. Climate predicted positive trends covered 627,184 ha (30.5%), whereas residual trends caused by non-climatic drivers covered 94,656 ha (4.6%). Climate induced negative trends covered 47,609 ha (2.3%) while residual trends were observed in 6,260 ha (0.3%). Negative trend pixels were concentrated mainly within the 3,000 to 5,000 m elevation band, with Karnali Province recording the highest proportional climate predicted decline in vegetation greenness (3.4%). At the municipality scale, rangeland change showed no significant relationship with grazing pressure derived from gridded livestock data, suggesting that grazing pressure alone did not explain the non-climatic vegetation signal. These spatially explicit, nationally consistent results identify where rangeland change is occurring and help distinguish climatic and non-climatic drivers of rangeland vegetation change, providing evidence to support targeted rangeland management under Nepal's federal governance structure.

The coast redwood (Sequoia sempervirens) is a long-lived, hexaploid conifer of high ecological, cultural, and economic value whose range has been greatly reduced by historical logging. Effective restoration and conservation depend on understanding patterns of genetic differentiation across the redwood range to delineate populations for management prioritization. Yet, past range-wide studies provided only a partial picture of population structure in coast redwood as they relied on a limited set of genetic markers or limited sampling, as sequencing was done on the same range-wide provenance collection. Here, we analyze 334,029 SNPs from a new range-wide set of 224 individuals using a dosage-based approach that accounts for polyploidy. Principal coordinates and neighbor-joining analyses reveal clear latitudinal genetic differentiation, with a distinct break south of San Francisco Bay. Outlier SNP analysis indicates new candidate loci involved in salinity tolerance, climate stress response, and nutrient uptake, suggesting potential local adaptation. These results point to the central role of geography in shaping genetic variation in coast redwood and give scientific basis for designing new conservation strategies and future experiments, including assisted migration, provenance trials, and restoration planning aimed at preserving the species into the future.

Global climate change has increased the frequency and intensity of drought events, making it urgent to understand how native species respond to water deficit (WD). In biodiverse environments such as tropical forests, simple methods are needed to study multiple species simultaneously. This can help predict how natural environments will respond to climate change and guide the strategic selection of drought-resistant species for reforestation. This study aimed to: (1) adapt an existing simple and inexpensive method to apply a controlled WD on tree seedlings from tropical species commonly produced in nurseries for restoration projects, suitable for greenhouse experiments; and (2) evaluate the effectiveness of this method in generating ecophysiological responses to WD that allow the estimation of species' drought resistance. Ten native tree species from the Semideciduous Seasonal Forest (SSF), a phytophysiognomy of the Atlantic Forest, were selected. An existing method was adapted to implement capillary irrigation, in which the bases of the seedling tubes were placed in floral foam blocks positioned inside 15 L plastic containers filled with water. A gradual and severe WD was applied to five seedlings of each species by removing all water from the containers, leaving only the water retained in the saturated floral foam available for plant uptake. The remaining seedlings were maintained well-watered (containers full and foam saturated) as the control group. Stomatal conductance (gs) was measured daily for all seedlings until they reached 50% or less of their initial gs (igs); at this point, stem water potential ({Psi}w) was measured. Both gs and {Psi}w differed significantly among treatments and species (p < 0.01). Ficus guaranitica and Heliocarpus popayanensis were the only species that did not show significant {Psi}w differences between treatments, indicating higher drought resistance. In contrast, Campomanesia xanthocarpa and Eugenia uniflora had the lowest {Psi}w values under WD, suggesting lower drought resistance. The remaining species were distributed along a gradient of responses to WD. Additionally, no correlation was found between {Psi}w and gs at 50% igs in the WD group (rho = 0.16, p = 0.26). The method proved effective in inducing controlled WD and generating measurable ecophysiological responses, offering a useful tool for screening native species for drought resistance.

Tropical dry evergreen forests (TDEFs) are a unique and highly threatened forest type of the dry tropics. Their restoration could be strengthened if native species demonstrate carbon sequestration comparable to widely used non-native trees. We assessed biodiversity and carbon sequestration in a restored TDEF in India, developed over 50 years from a largely barren landscape. The site now supports high woody-plant diversity, with 91 native species across 34 families. Aboveground biomass (AGB) averaged 66.91 +/- 41.2 Mg/ha comparable to seasonally dry tropical forests globally. Although native species were planted more recently and are shorter than non-natives, they contributed 23.86 +/- 23.4 Mg/ha to AGB and show potential for future increases in basal area. Given their comparable wood densities and capacity to attain similar heights, native species are predicted to sequester carbon at levels similar to non-natives in the long term. AGB was unrelated to species diversity. Overall, native TDEF species can achieve carbon storage while maintaining ecological integrity.

Circuit theory has been successfully applied to ecological connectivity modelling, notably via the Circuitscape software, which is typically run locally on a laptop or via a server. For downstream geospatial web applications relying on connectivity analysis, backend infrastructure is required, which can be costly and require advanced data governance. Recent developments in WebAssembly now allow fast C++ or Rust code to be run directly in a sandboxed browser environment for edge computing. We present a WebAssembly/Rust toolset with a geospatial data pipeline and efficient edge-computing implementation of connectivity analysis. This approach may be useful for geospatial modelling software where rasters and memory footprint are small enough for the browser context. Our results show that as expected, Circuitscape solves 1000x1000 raster networks 1-2x faster, but requires further file writes. Accounting for total program runtime, our web implementation can be faster for the given context.

Aim: To forecast near-future arrivals of non-native terrestrial and freshwater vertebrates at the regional level. Location: Global (geopolitical regions worldwide, including countries and main administrative divisions). Methods: We compiled first regional record data and assembled functional and macroecological variables for 1,931 non-native vertebrate species. For each region, we identified recently arrived non-native species using retrospective windows of thirty and twenty years ending in 2015 (1986-2015; 1996-2015). We then fitted region-specific random-forest models classifying recently arrived species versus those not yet arrived using as predictors: (i) harmonised species traits (e.g., habitat, diet, body size and native-range attributes) and (ii) spread history, capturing time since first record elsewhere. Predictive performance was evaluated using leave-one-out cross-validation, comparing full models with trait-only and spread-only variants. We also assessed relationships between predictive accuracy, predictor importance, and the geographic positioning and trade connectedness of regions. Finally, we predicted region-specific probabilities of arrival for species not yet recorded. Results: Forecasting accuracy was consistently high across regions and taxa, with AUC values above 0.9 in more than half of the focal regions. Full models substantially outperformed models using either predictor set alone, and spread-history-only models typically exceeded trait-only models. Relative importance of spread-history predictors declined with geographic distance to the focal region, whereas predictability was lower in highly trade-connected regions. Predicted near-future high-risk arrivals were dominated by birds and freshwater fishes and showed strong regional structuring. A small set of species ranked highly across many regions (e.g., birds: Phasianus colchicus, Acridotheres tristis, Amandava amandava, Colinus virginianus, Corvus splendens and Lonchura malacca; fishes: Coregonus peled and Oreochromis mossambicus; mammal: Oryctolagus cuniculus), suggesting substantial unrealised spread potential. Main conclusions: Near-future regional arrivals of non-native vertebrates are predictable from spread history and species traits. This enables scalable, updateable regional watchlists to support prevention, early detection and horizon scanning.

Object representations in the human ventral occipitotemporal cortex (VOTC) are organized along multiple dimensions, including shape (rectilinear vs. curvilinear), real-world size (large vs. small), and mobility (stationary vs. mobile). However, these dimensions are strongly correlated in naturalistic vision, making their separate contributions to VOTC organization unclear. For example, large objects (e.g., a wardrobe, a house) are typically rectilinear and stationary, while small objects (e.g., a ball, a cup) are more curvilinear and mobile. Here, we used fMRI, together with a new stimulus set that orthogonally manipulates shape, size, and mobility, to investigate the separate influences of these dimensions on VOTC organization. Example stimuli include air balloon (large, curvilinear, mobile), radar dish (large, curvilinear, stationary), and mailbox (small, rectilinear, stationary). Contrasts revealed that large (vs. small), rectilinear (vs. curvilinear), and stationary (vs. mobile) dimensions all independently evoked strong and overlapping activity in medio-anterior VOTC. This overlapping activity was at the intersection of the parahippocampal place area (PPA) and the ventral place-memory area (VPMA). Similar results were found at the intersection of the scene-selective occipital place area and the lateral place-memory area (LPMA). Finally, large (vs. small), but not rectilinear (vs. curvilinear) or stationary (vs. mobile) activity, was found in additional posterior ventral scene-selective regions, as well as in early visual cortex. Overall, these results indicate that object shape, real-world size, and mobility dimensions all independently activate scene-selective PPA and OPA, showing joint selectivity for distinct low- and high-level object properties that are highly correlated in naturalistic vision.

We showed before that the endogenous peptide Nociceptin blocks the premature differentiation of oligodendrocytes (OLGs), preventing untimely precocious myelination in the developing brain. Consistent with this early function, Nociceptin brain expression is developmentally regulated, sharply decreasing with the initiation and progression of myelination. However, we now found that at difference with controls and relapsing-remitting multiple sclerosis (RRMS), Nociceptin levels are highly elevated in cerebrospinal fluid from patients with the most severe progressive MS (PMS) forms. This questioned whether Nociceptin early developmental effects could be latter recapitulated, interfering with remyelination in PMS. This possibility was tested by inducing experimental autoimmune encephalomyelitis in older mice, at an age equivalent to that with increased risk of RRMS transition into PMS. Older animals develop persistently highly debilitating clinical symptoms, and display both brain and spinal cord demyelination. Importantly, these mice exhibit elevated brain Nociceptin levels, and their treatment with an antagonist of the Nociceptin receptor (NOR) elicits a regression of clinical scoring that is accompanied by higher ratios of OLGs/OLG progenitor cells, increased myelination, and reduction of reactive astrocytes. These findings suggest that Nociceptin may be a crucial player in the age-related progression of MS; interfering with OLG maturation and remyelination, and perhaps further exacerbating neurological dysfunction by targeting astrocyte populations. The upregulation of Nociceptin secretion by human astrocytes in response to proinflammatory cytokines, also points to this peptide as a mediator of microglia-astrocyte interactions supporting MS progression with aging. NOR may offer a novel pharmacological target for ameliorating the devastating effects of MS progression.

Extrahepatic delivery of lipid nanoparticles (LNPs) to non-phagocytic cells is a major challenge, with the leading strategy involving surface functionalization with target-specific monoclonal antibody (mAb) ligands. We investigate the stability of mAb-conjugated LNPs using two anchoring systems: the commonly used DSPE-PEG2kDa-maleimide and a block copolymer, PCL5kDa-b-PEG2kDa -maleimide, with the hypothesis that conjugation to a 150,000 Da antibody could overwhelm the relatively small ~600 Da aliphatic anchor on the PEG-lipid in vivo. Shedding of the mAB would compromise targeting. Conjugation integrity following IV injection was assessed by tagging LNPs and mAbs with metal ion tracers that could be quantified by ICP-MS. Results show that DSPE-PEG-mAb rapidly (within 1h) dissociates from LNPs in blood, leading to accelerated LNP clearance. In contrast, mAbs conjugated using PCL-b-PEG remained stably associated with the LNP over the 24h circulation and clearance of the construct. Results are connected to a thermodynamic model that reproduces experimental findings for PEG-anchor(-mAb) shedding in vitro and in vivo. This study identifies anchoring strength as a critical, unconsidered parameter for in vivo performance when conjugating mAbs to LNPs for extrahepatic delivery.

Blockade by sedimentary particles, such as mineral crystals, is a continuous risk the kidney tubule faces. To prevent that, kidney resident macrophages form transepithelial protrusions and remove intratubular sedimentary particles, a behavior particularly prevailing in the medulla over the cortex. However, the molecular mechanisms underlying this characteristic behavior of medulla macrophages are incompletely understood. In this study, we identified that the medulla had higher mechanical stiffness than the cortex in steady state, which was further elevated when kidney stone formed. Increased tissue rigidity was sensed by medulla macrophages via mechanoreceptor Piezo1, which promoted macrophage protrusion formation and their ability to clean the tubules. Loss of Piezo1 expression in kidney macrophages predisposed mice to intratubular accumulation of mineral crystal in steady state and accelerated kidney stone formation during oxalate intake challenge. Signaling via Piezo1 mobilized molecules involved in cell adhesion and protrusion assembly, including Talin2 and focal adhesion kinase (FAK). Finally, we developed a first-of-its-kind cell-based therapy for the treatment of experimental nephrolithiasis by exploiting macrophage Piezo1 activity, and this strategy shows great promise for future translational research.

Single-nucleus transcriptomic atlases offer an unprecedented opportunity to connect cellular molecular states with Alzheimer's disease (AD) neuropathology, but whether these profiles encode reproducible, predictive information about pathological burden remains unclear. We present the SEA-AD DREAM Challenge, an open, international, model-to-data competition built on the Seattle Alzheimer's Disease Brain Cell Atlas to predict Alzheimer's disease neuropathological severity from single-nucleus RNA-sequencing data. Participants developed containerized models to predict categorical neuropathological staging, including overall Alzheimer's disease neuropathologic change, Braak stage, Thal phase, and CERAD score, as well as quantitative amyloid-{beta} and phospho-tau burden measured by 6E10 and AT8 immunohistochemistry. Across 17 eligible teams from 15 countries, the crowdsourcing framework enabled systematic comparison of diverse computational approaches and surfaced a broad landscape of modeling strategies and candidate predictive features. Top-performing methods achieved near-perfect prediction of categorical staging, with the best submission reaching a quadratic weighted kappa of 1.0 for the Overall AD Neuropathological Change score (ADNC), and competitive prediction of quantitative pathological burden in held-out data, with a best concordance correlation coefficient of 0.48. Post hoc perturbation analyses revealed that top categorical-stage predictions relied heavily on donor-level metadata-driven signals rather than transcriptomic features, whereas quantitative pathology prediction was more robust and supported by transcriptomic and cell-type-associated features with potential biological relevance to AD progression. The challenge also introduced the first AI Agent Track in a DREAM Challenge, providing an early benchmark for autonomous and human-guided agentic model development in single-cell neuroscience. This work demonstrates that single-nucleus transcriptomes encode substantial information about Alzheimer's disease pathology, establishes a reproducible benchmark for molecular neuropathology prediction, and highlights critical principles for designing privacy-preserving, leakage-aware community challenges using deeply phenotyped human brain data.