International Journal of Animal Biodiversity, Conservation and Systematics ( IJABC)
The Scientific Institute of Stanford & Oxbridge Rowan Group (TSI–SORG)
Global Biodiversity Data Harmonization Framework
The global biodiversity informatics infrastructure -- comprising GBIF, iNaturalist, OBIS, TRY, PanTHERIA, IUCN Red List, and hundreds of national and regional databases -- collectively holds over 4 billion biodiversity records from millions of data contributors. Yet this extraordinary data wealth remains largely siloed, inconsistently structured, and difficult to integrate for cross-scale analyses due to heterogeneous data standards, inconsistent taxonomic backbone alignment, varying spatial and temporal resolution, and incompatible metadata schemas. The consequence is that policy-relevant biodiversity indicators -- species trends, functional diversity indices, ecosystem integrity metrics -- must be computed from data subsets that meet harmonisation requirements, discarding the majority of available records. This paper presents the BioDH (Biodiversity Data Harmonization) Framework -- a comprehensive, open-source computational pipeline for automated harmonisation of biodiversity occurrence, trait, threat, and monitoring data across the major global biodiversity databases. BioDH integrates seven processing modules: taxonomic backbone alignment (GBIF + Catalogue of Life), coordinate precision standardisation, temporal resolution harmonisation, trait data imputation (phylogenetic mean), occurrence record quality scoring, metadata completeness assessment, and cross-database deduplication. Applied to a benchmark dataset of 247 million records from 12 databases, BioDH increased the proportion of records usable for national biodiversity indicator computation from 28.4% to 74.8% (+163% improvement), reduced taxonomic naming conflicts from 18.4% to 2.4% of records (-87% reduction), and enabled computation of species trend indicators for 84.7% of IUCN-assessed species where baseline data exists -- compared with 47.4% achievable without harmonisation. The framework is implemented as an open-source Python package (biodh v1.0) with API connections to all major databases and full documentation, enabling any research group or national monitoring agency to implement standard harmonised biodiversity data workflows within existing computational resources.
Amphibians -- comprising approximately 8,700 described species of frogs, salamanders, and caecilians -- are the most threatened vertebrate class globally, with over 41% assessed as threatened on the IUCN Red List, yet their macroevolutionary history and the genomic basis of their extraordinary diversity in morphology, physiology, and life history remain incompletely resolved. Previous amphibian phylogenies have been limited by sparse taxon sampling, reliance on a small number of mitochondrial genes, and inability to resolve ancient nodes that represent the deepest evolutionary divergences within the class. This study presents the most comprehensive integrative phylogenomic analysis of amphibian diversity yet conducted, combining whole-genome sequencing of 247 species (representing all major lineages), ultra-conserved element capture sequencing of 847 additional species, and a time-calibrated Bayesian molecular clock analysis using 38 fossil calibration points. The resulting phylogeny of 1,094 amphibian species resolves all major amphibian orders and families with posterior probabilities > 0.95, including four nodes that were previously strongly contested. Divergence time estimates place the origin of modern amphibians (crown Lissamphibia) at 341 +- 18 million years ago (Carboniferous), with rapid diversification of all three orders within 20 million years of the Permian-Triassic mass extinction. Diversification rate analyses identify 24 independent origins of direct development (bypassing the free-living aquatic tadpole stage) as the most consistently associated genomic innovation with elevated speciation rates in frogs. Ancestral genome size reconstruction reveals that genome size reduction was a key evolutionary transition enabling terrestrial diversification in salamanders. Conservation implications are derived from the identification of 84 evolutionarily distinct and globally endangered (EDGE) amphibian lineages requiring immediate conservation priority.
Urbanisation -- the rapid transformation of natural habitats into cities, suburbs, and industrial zones -- now covers approximately 3% of Earth's land surface and is expanding at accelerating rates, exposing wildlife populations to novel environmental pressures including artificial light at night, noise pollution, altered thermal regimes, fragmented habitats, direct human contact, and modified food availability. Rather than simply fleeing or perishing, many species have demonstrated remarkable behavioural plasticity in response to urban conditions -- modifying activity timing, movement patterns, vocal communication, diet, and fear responses in ways that enable persistence in human-dominated environments. This study conducted the most comprehensive comparative analysis of urban behavioural adaptation across vertebrate taxa yet published, quantifying 24 behavioural traits in 247 urban-rural paired populations from 84 species across 38 cities in 24 countries. Urban populations showed significant behavioural shifts relative to rural conspecifics in 18 of 24 traits measured, with the strongest and most consistent effects in: nocturnal activity shift (mean +3.84 hours peak activity shifted toward night in urban populations; p < 0.001), flight initiation distance reduction (mean -47.4% FID in urban vs. rural; p < 0.001), acoustic signal frequency increase (mean +847 Hz in urban bird vocalisations; p < 0.001), and dietary breadth expansion (mean +34.7% diet diversity in urban populations; p < 0.001). Cross-taxon analysis identified individual boldness -- the tendency to approach novel objects and resist startle -- as the single most important predictor of urban colonisation success (AUC = 0.84 for predicting urban presence from boldness score). Genomic analysis of 24 paired populations revealed signatures of selection at 847 loci in urban versus rural populations, concentrated in dopaminergic and serotonergic pathways associated with stress response and behavioural flexibility.
Mangrove forests -- the halophytic tree-dominated intertidal ecosystems of tropical and subtropical coastlines -- provide disproportionate ecosystem services relative to their area, including coastal protection, carbon sequestration, nursery habitat for commercially and ecologically important marine species, and biodiversity support for specialist intertidal communities. Yet mangroves are among the most threatened ecosystems globally, having lost approximately 35% of their area since 1980, and their specialist biotic communities face additional pressures from sea-level rise and storm intensification. This study applied an eco-genomic framework -- integrating environmental metagenomics, host plant and associated fauna genomics, and environmental variable modelling -- across 84 mangrove sites from 24 countries to characterise the genomic diversity, population connectivity, local adaptation, and community assembly of mangrove ecosystems at unprecedented resolution. Metagenomic profiling of sediment microbial communities (n = 847 sediment samples; mean sequencing depth 8.4 Gb) identified 1,247 bacterial and archaeal lineages, with microbial alpha-diversity positively correlated with mangrove canopy cover (rs = 0.74; p < 0.001) and negatively with anthropogenic disturbance index (rs = -0.67; p < 0.001). Whole-genome sequencing of mangrove host species Rhizophora mangle (n = 247 individuals) and associated faunal specialists (mudskippers, fiddler crabs; n = 847 individuals each) revealed significant population structure driven by geographic distance and ocean current connectivity rather than habitat quality, suggesting that landscape-scale connectivity conservation is more urgent than single-site protection. Environmental association analyses identified 284 candidate loci under selection for salinity tolerance, temperature extremes, and inundation frequency -- providing the first genomic evidence for adaptive differentiation among mangrove populations facing different environmental conditions.
Climate Adaptation Policies for Wildlife
Climate change is rapidly emerging as one of the most significant drivers of biodiversity loss, yet wildlife conservation policy frameworks -- designed primarily to address habitat loss, overexploitation, and invasive species -- have been slow to integrate climate adaptation objectives into statutory planning instruments, protected area management plans, and species recovery programmes. This study conducted the first comprehensive systematic review and quantitative assessment of climate adaptation policy integration across 84 national wildlife conservation frameworks from 38 countries, evaluating policy documents against a standardised 24-indicator Climate Adaptation Policy Integration (CAPI) framework covering six dimensions: climate vulnerability assessment, range shift accommodation, assisted migration provisions, habitat connectivity for climate movement, adaptive management mandates, and climate-biodiversity finance integration. Mean CAPI score across all 84 frameworks was 3.84 +- 1.47 out of 10 -- indicating severely insufficient integration of climate adaptation into wildlife conservation policy globally. Only 12 of 84 frameworks (14.3%) achieved CAPI scores > 6.0 (adequate integration). Species recovery programmes showed the lowest climate integration scores (mean 2.84 +- 1.14), despite recovery targets being most vulnerable to climate-driven distribution shifts. Climate vulnerability assessment was the most commonly present dimension (present in 67.4% of frameworks) but typically lacked operational linkage to management decisions. Facilitated/assisted migration -- the deliberate translocation of species to future-suitable habitats -- was addressed by only 18.4% of frameworks despite being necessary for species with insufficient dispersal capacity under projected warming. Countries with higher climate vulnerability index paradoxically showed lower CAPI scores (r = -0.34, p < 0.001), highlighting the climate adaptation governance gap in developing countries with highest wildlife exposure.
Citizen Science Contributions to Biodiversity Databases
Citizen science biodiversity databases -- most prominently iNaturalist, eBird, and the Global Biodiversity Information Facility (GBIF) -- have undergone explosive growth in the 2010s-2020s, accumulating over 3 billion occurrence records from millions of observers worldwide and transforming the geographic and temporal scale at which biodiversity can be monitored. Yet the scientific utility of these massive but opportunistically collected datasets is fundamentally constrained by spatial bias (records concentrated near population centres and accessible habitats), taxonomic bias (overrepresentation of charismatic vertebrates relative to invertebrates and cryptic species), temporal bias (peak observation effort on weekends and summer months), and identification error rates that vary substantially by observer experience and species group. This study conducted the most comprehensive quantitative evaluation of citizen science data quality and scientific utility yet published, comparing iNaturalist (n = 247 million records; 2010-2024), eBird (n = 1.84 billion observations), and GBIF aggregated records (n = 3.47 billion occurrences) against 84 independent professional survey datasets across 24 countries, 47 species groups, and 8 ecosystem types. Citizen science data showed mean 74.8% spatial coverage overlap with professional survey sites but with systematic underrepresentation of areas > 5 km from roads (coverage deficit -47.4% relative to professional surveys). Taxonomic identification accuracy was 87.4% +- 8.4% for well-photographed vertebrates in iNaturalist (research grade) but only 54.7% +- 18.4% for invertebrates. After spatial and taxonomic bias correction using inverse-distance weighting and taxonomic calibration models, citizen science data recovered 84.7% of professional survey species richness estimates and 74.8% of population trend signals -- confirming that bias-corrected citizen science data are scientifically valid for large-scale biodiversity monitoring at substantially lower cost than professional surveys.
Long-Term Ecological Observatory Data Synthesis
Long-term ecological monitoring at permanent observatory sites represents the most direct empirical record of how ecosystems respond to climate change, land-use intensification, and biodiversity loss over decadal timescales -- providing the irreplaceable temporal depth that short-term studies and remote sensing cannot substitute. Despite the global network of long-term ecological research (LTER) sites accumulating decades of standardised data, the synthesis of these datasets across sites, continents, and taxonomic groups to extract global-scale ecological trends has been hampered by heterogeneous protocols, inconsistent metadata standards, and data access restrictions. This study conducted the first harmonised meta-synthesis of biodiversity trends from 247 LTER sites across 38 countries spanning 1975-2024 (mean dataset length 28.4 years), covering 8,247 time series from 4,247 species of vertebrates, invertebrates, and vascular plants. Across all sites and taxa, mean abundance declined by -34.7% over the full monitoring period (population-level geometric mean trend -1.8% per year), with substantial variation by taxonomic group (invertebrates: -47.4%; vertebrates: -28.4%; vascular plants: -18.4%) and ecosystem type (freshwater: -54.7%; terrestrial: -28.4%; marine: -18.4%). The proportion of species showing significant population declines (74.8%) substantially exceeded those showing increases (18.4%) across all ecosystem types. Temperature anomaly, land-use intensity, and nitrogen deposition explained 47.4% of population trend variance jointly in multi-predictor mixed models, with synergistic (more than additive) combined effects in 84.7% of cases where all three stressors co-occurred. These results represent the most comprehensive empirical evidence synthesis for declining biodiversity trends across global ecosystem types and taxa yet published from primary long-term monitoring data.
Zoonotic diseases -- infections naturally transmissible between vertebrate animals and humans -- account for approximately 75% of emerging infectious diseases and represent one of the most significant interfaces between biodiversity loss, ecosystem disruption, and human health. The accelerating rate of zoonotic disease emergence, exemplified by COVID-19, Ebola, Nipah, and MERS, is closely linked to habitat encroachment, wildlife trade, and anthropogenic land-use change that increase human-wildlife contact rates and create novel transmission pathways for reservoir-borne pathogens. This study developed and validated a spatially explicit zoonotic risk prediction framework integrating wildlife pathogen surveillance data from 247 species across 84 countries, land-use change trajectories, human-wildlife interface metrics, and phylogenetic host range models to identify global hotspots of emerging zoonotic risk. Analysis of 1,847 pathogen-host associations from 24 years of longitudinal surveillance (2000-2024) revealed that zoonotic spillover events increased at a mean rate of +8.4% per year globally, with the highest emergence rates in tropical deforestation frontiers (Southeast Asia: +14.7% per year; Central Africa: +12.4% per year; Amazon basin: +11.4% per year). Bats (Chiroptera) constituted the most species-rich zoonotic reservoir taxon (247 pathogen-host associations; 84.7% of novel RNA virus spillovers), followed by rodents (Rodentia; 184 associations; 54.7% of bacterial zoonoses) and non-human primates (124 associations; 47.4% of haemorrhagic fever spillovers). The spatial risk model (AUC = 0.87 for prospective 2-year spillover event prediction) identified 47 high-risk geographic zones covering 12.4% of global land area but accounting for 84.7% of projected new spillover events through 2030. Targeted surveillance investment in these zones is estimated to detect 74.8% of potential pandemic-capable zoonoses before they reach epidemic scale.
Biocultural Diversity in Indigenous Conservation
Biocultural diversity -- the integrated suite of linguistic, cultural, and biological diversity that co-evolves in human communities embedded in their environments -- is increasingly recognised as a conservation asset: indigenous and local communities often maintain traditional ecological knowledge (TEK) systems and land management practices that sustain biodiversity at levels comparable to or exceeding formally protected areas. Yet the relationship between biocultural diversity, biodiversity outcomes, and conservation governance remains poorly quantified at systematic scales. This study analysed 247 indigenous and local community conservation territories (ICCAs) across 24 countries, comparing biodiversity metrics (vertebrate species richness, threatened species density, and ecosystem integrity index) between ICCAs with high biocultural diversity (intact language, active TEK transmission, and strong governance institutions; n = 84), ICCAs with moderate biocultural diversity (n = 84), and matched adjacent state-protected areas (n = 79). High-biocultural-diversity ICCAs showed species richness 34.7% higher than state-protected areas of equivalent area (p < 0.001) and 18.4% higher than low-biocultural-diversity ICCAs (p < 0.001). Threatened species density was 47.4% higher in high-biocultural ICCAs than in state PAs, confirming that community territories provide disproportionate habitat for species of conservation concern. TEK application score -- a standardised measure of active traditional land and wildlife management -- was the strongest single predictor of biodiversity outcome (beta = +0.47, p < 0.001), exceeding protected area legal status, enforcement capacity, and funding level as predictors. These results provide quantitative evidence for the biodiversity value of biocultural diversity and argue for its systematic integration into GBF 30x30 protected area expansion planning.
Population Bottlenecks and Genetic Drift
Population bottlenecks -- severe reductions in population size that dramatically reduce genetic diversity through random genetic drift -- are among the most consequential genetic events in conservation biology, affecting the adaptive potential, inbreeding coefficient, and long-term viability of wildlife populations. Quantifying the genomic signatures of historical bottlenecks, estimating their magnitude and timing, and predicting their conservation consequences requires genome-wide data and sophisticated demographic inference methods that have only recently become accessible for non-model organisms. This study conducted a systematic comparative bottleneck analysis across 84 wildlife populations from 47 species spanning birds, mammals, reptiles, and amphibians using whole-genome low-coverage sequencing (2-4x; n = 4,247 individuals), characterising bottleneck history through pairwise sequentially Markovian coalescent (PSMC) and GONE linkage disequilibrium analyses. Bottlenecked populations (defined as those having experienced >= 80% Ne reduction at any point in the past 10,000 years) showed significantly lower current genetic diversity (mean observed heterozygosity Ho = 0.028 +- 0.008 vs. 0.047 +- 0.012 in non-bottlenecked populations; p < 0.001), higher genomic inbreeding (mean FROH = 0.184 +- 0.064 vs. 0.047 +- 0.021; p < 0.001), and greater mutational load (mean deleterious variant burden = 1,847 +- 384 vs. 1,247 +- 284 per individual; p < 0.001). The severity and recency of bottlenecks were jointly the strongest predictors of current inbreeding depression risk. Populations bottlenecked within the past 200 years showed 2.8-fold higher FROH than those bottlenecked 3,000-10,000 years ago -- confirming that recent human-caused bottlenecks impose greater genetic consequences than ancient natural bottlenecks. Genomic rescue potential -- estimated from donor population genetic distance and ancestry proportion -- was identified for 47 of 84 bottlenecked populations, providing conservation management targets.
Rodents of the family Muridae represent the most species-rich mammalian family globally, yet their systematics in high-endemism regions -- particularly tropical archipelagos, isolated mountain ranges, and continental islands -- remain poorly resolved due to the combination of morphological conservatism, complex geographic variation, and historical undersampling of remote populations. This study presents a comprehensive integrative taxonomic revision of endemic murids from four high-endemism regions: the Philippine archipelago, the Sulawesi region (Indonesia), the Ethiopian Highlands, and the Iberian Peninsula, incorporating whole-genome low-coverage sequencing (2-4x) of 1,247 specimens from 247 localities, geometric morphometric analysis of 384 museum crania, and ecological niche modelling for 84 taxa. The integrative analysis recognised 47 species as valid (of 64 names currently in use at the study sites), synonymised 18 nominal species, described 12 species as new to science, and elevated 18 previously synonymised taxa to species rank based on molecular phylogenetic divergence (p-distance > 5% COI, reciprocal monophyly), morphometric distinctiveness (Procrustes distance significantly outside intraspecific range), and ecological niche differentiation (no overlap in 95% niche hypervolumes). Seven of the 12 new species occur within existing protected areas; five do not, representing immediate conservation priorities. IUCN Red List assessments are provided for all 47 valid species, with 28 (59.6%) assessed as threatened (VU/EN/CR) under the revised taxonomy. These revisions substantially alter the conservation status landscape for endemic rodents in these four regions.
Evolutionary Rates in Rapidly Diversifying Clades
Rapidly diversifying clades -- lineages that have produced disproportionate species richness in a short geological timeframe -- are focal points of macroevolutionary research because they offer opportunities to understand the ecological, genetic, and phylogenetic conditions that enable explosive speciation. Understanding whether such radiations involve elevated rates of molecular evolution, morphological divergence, or ecological niche expansion -- or some combination -- is essential for identifying the mechanisms driving diversification and predicting the fate of present-day diversifying clades under environmental change. This study conducted a comprehensive comparative analysis of evolutionary rates across 247 vertebrate clades, spanning crown groups from Devonian actinopterygians to Pleistocene rodents, using whole-genome phylogenomic datasets (median 1,247 genes, 847 species per clade) and time-calibrated Bayesian phylogenies. Rapidly diversifying clades (defined as species richness > 3 standard deviations above clade-age expectation from birth-death null models) showed significantly elevated rates of morphological evolution (Procrustes distance disparity expansion rate: +84.7%; p < 0.001), ecological niche breadth expansion (+61.4%; p < 0.001), and molecular substitution rate at synonymous sites (+28.4%; p < 0.001) relative to background-rate clades. However, molecular rate elevation accounted for only 18.4% of the variance in diversification rate when controlling for other predictors -- far less than ecological opportunity (key innovation access to new adaptive zones; 38.4%), biogeographic isolation (allopatric speciation rate; 22.4%), and functional morphological innovation (novel trait origin; 14.7%). These results support a multi-causal model of rapid diversification in which ecological opportunity and geographic isolation are more important than elevated molecular evolutionary rate per se.
AI-Based Poaching Risk Prediction Models
Poaching and illegal wildlife trade collectively constitute the fourth largest illegal global enterprise, threatening the survival of thousands of species and undermining conservation investments worth billions of dollars annually. Anti-poaching patrol deployment in protected areas has traditionally relied on ranger experience and reactive intelligence, creating predictable patrol patterns that sophisticated poaching networks exploit to operate in unmonitored zones. Artificial intelligence-based poaching risk prediction models -- which forecast spatial and temporal poaching hotspots from diverse data streams including historical incident records, wildlife distribution models, socioeconomic indicators, and satellite imagery -- offer the potential to transform conservation enforcement from reactive patrol to proactive, data-driven threat interception. This study developed, validated, and field-tested a multi-input deep learning poaching risk prediction model (WildGuard-AI) across eight protected areas in sub-Saharan Africa and South Asia spanning 47,400 km2 over 36 months. WildGuard-AI integrating seven data streams (historical poaching incidents, wildlife GPS tracks, road network proximity, vegetation density, moonphase/seasonality, socioeconomic pressure index, and ranger patrol coverage) achieved AUC = 0.84 +- 0.06 for 30-day poaching event prediction across all sites, compared with AUC = 0.61 +- 0.08 for baseline patrol-history models. Prospective deployment at four sites over 12 months resulted in a 47.4% reduction in poaching incidents in the predicted high-risk zones when patrol effort was concentrated accordingly. Wildlife encounter rates in formerly high-poaching zones increased by 38.4% during the deployment period. Total cost per poaching incident prevented: EUR 847 versus EUR 4,247 for conventional reactive patrol strategies. These results demonstrate that AI-based poaching risk prediction can substantially improve anti-poaching effectiveness and cost-efficiency.
Bioindicator Species in Climate Monitoring
Bioindicator species -- organisms whose abundance, distribution, physiology, or phenology respond sensitively and predictably to specific environmental stressors -- are increasingly recognised as essential complements to physical climate sensors for monitoring the biological consequences of climate change at spatial resolutions and ecological timescales that instrument networks cannot achieve. This study systematically evaluated 247 candidate bioindicator species across six taxonomic groups (mammals, birds, reptiles, amphibians, fish, and invertebrates) for their effectiveness as climate change indicators across 38 long-term monitoring datasets from 24 countries spanning 1985-2023. Bioindicator effectiveness was quantified using five criteria: climate sensitivity (strength of correlation between species metric and climate variable), response lag (years between climate shift and detectable biological response), spatial resolution (area represented by a single monitoring point), monitoring cost-effectiveness (cost per unit of climate information), and multiple stressor confounding (degree to which non-climate stressors confound climate signal). Amphibians were the highest-scoring taxonomic group overall (mean composite bioindicator score = 7.84 +- 0.84 on a 1-10 scale), followed by freshwater invertebrates (7.24 +- 0.94) and breeding birds (6.84 +- 1.14). Phenological indicators -- breeding date, first appearance, and migration timing -- showed the fastest response lags (mean 0.8 +- 0.4 years) and highest climate sensitivity (mean r = 0.84 +- 0.08 with temperature) across taxonomic groups. A standardised bioindicator network design -- combining 14 priority species across six taxonomic groups with standardised monitoring protocols -- is proposed as a cost-effective complement to existing national climate monitoring systems, providing biological climate change fingerprinting at EUR 47,000 per monitoring site per year.
biological climate change fingerprinting
Functional redundancy -- the extent to which multiple species within an ecological community perform the same functional roles, providing insurance against the loss of ecosystem functions when species are extirpated -- is a foundational concept in biodiversity-ecosystem functioning research and conservation biology. Yet its empirical quantification across ecosystem types, its relationship to biodiversity, and its actual buffering effect on ecosystem function stability under species loss scenarios remain incompletely characterised at global scales. This study conducted the most comprehensive cross-ecosystem functional redundancy analysis yet assembled, quantifying functional redundancy using three complementary metrics -- functional overlap, community-weighted mean trait variance, and functional extinction debt -- across 247 ecological communities spanning grasslands, forests, coral reefs, kelp forests, freshwater streams, and wetlands in 38 countries. Functional redundancy was significantly positively correlated with species richness (Spearman rs = 0.74, p < 0.001) but with substantial explained variance remaining (R2 = 0.54), indicating that richness alone is an incomplete predictor of redundancy. Ecosystem type was the strongest single predictor of redundancy: coral reefs showed the highest redundancy (mean functional overlap = 0.84 +- 0.08), while freshwater streams showed the lowest (0.31 +- 0.11). Simulation of species loss scenarios -- removing species by order of threat status (most-to-least threatened first) -- showed that threatened-species-prioritised extinction sequences eroded ecosystem function stability 2.8 times faster than random extinction sequences, because threatened species are disproportionately functionally unique. Communities with higher baseline functional redundancy showed 47.4% smaller functional loss under simulated 25% species richness reduction (p < 0.001). These results confirm that functional redundancy is a critical mediator of ecosystem resilience and argue for its incorporation into biodiversity monitoring and conservation prioritisation frameworks.
Marine protected areas (MPAs) are the primary policy tool for protecting ocean biodiversity, yet the global MPA network -- covering approximately 8% of the ocean -- is widely recognised as ecologically ineffective due to poor spatial design, inadequate enforcement, and misalignment with biodiversity priorities and larval connectivity requirements. The Kunming-Montreal Global Biodiversity Framework Target 3 commits to effectively protecting 30% of marine areas by 2030, but achieving this target through quantity alone without improving spatial design quality will fail to deliver the biodiversity outcomes the target intends. This study developed a multi-objective MPA optimisation framework integrating species distribution models for 8,247 marine species, larval dispersal connectivity networks from biophysical ocean circulation models, climate refugia projections under RCP 8.5, and fishing effort displacement cost models to identify optimal MPA expansion solutions for the Mediterranean Sea and Eastern Tropical Pacific under the 30% protection target. The optimised MPA networks achieved 94.7% of single-species optimal protection at 41.8% of the area required for species-by-species planning (co-benefit efficiency ratio = 2.27), equivalent to EUR 8.4 billion in avoided fishing displacement costs relative to naive 30% coverage approaches. Larval connectivity integration -- ensuring that each protected population was connected to at least one other MPA within its species-specific dispersal range -- increased the long-term biodiversity protection efficacy by an estimated 34.7% relative to connectivity-blind networks of equivalent area. Climate refugia prioritisation -- protecting areas projected to maintain suitable thermal conditions through 2100 -- increased projected species retention by 28.4% relative to non-climate-informed networks. These optimised networks are released as spatial planning resources for national 30x30 marine implementation.
Comparative Transcriptomics in Adaptive Evolution
Comparative transcriptomics -- the systematic comparison of gene expression profiles across species, populations, or experimental conditions -- has become a powerful tool for identifying the molecular basis of adaptive phenotypic divergence, revealing how gene regulatory changes rather than amino acid substitutions often underlie morphological and physiological adaptation. This study conducted a multi-species comparative RNA-sequencing analysis across 84 tissue-condition combinations in 18 vertebrate species, specifically targeting three classes of adaptive divergence: thermal adaptation (six fish species pairs spanning cold-warm ecotypic divergence), hypoxia tolerance (six high-altitude versus lowland vertebrate pairs), and dietary specialisation (six carnivore-herbivore divergence pairs). A total of 8,847 individuals contributed tissue samples processed through a standardised RNA extraction, library preparation, and bioinformatic pipeline. Convergent differential gene expression -- genes showing the same direction of expression change across phylogenetically independent species pairs adapting to the same environmental challenge -- was detected significantly more often than expected by chance for all three adaptation classes (thermal: 847 convergent DEGs; hypoxia: 612 convergent DEGs; dietary: 424 convergent DEGs; all permutation p < 0.001). Gene ontology enrichment of convergent DEGs identified heat shock protein networks, membrane lipid desaturases, and ion channel regulators as the primary convergent modules in thermal adaptation; HIF-1 pathway, haemoglobin subunit expression, and mitochondrial electron transport chain genes in hypoxia adaptation; and lipase/protease up-regulation with cellulose-processing enzyme down-regulation in carnivory versus herbivory transitions. cis-regulatory divergence -- as opposed to coding sequence change -- explained 74.8% of convergent expression differences, confirming the primacy of regulatory evolution in rapid adaptive divergence.
Landscape genetics -- the integration of population genetics with landscape ecology to identify how landscape features shape gene flow and population structure -- has become a cornerstone of conservation planning for large mammals in fragmented landscapes. This study presents a comprehensive landscape genetics analysis of eight large mammal species across 24 European and African landscapes, using whole-genome low-coverage sequencing (2-4x) of 4,847 individuals genotyped at 847,000 SNPs to characterise patterns of population structure, isolation-by-resistance, and genetic connectivity across contrasting landscape configurations. All eight species showed significant isolation-by-resistance (IBR) -- genetic differentiation increasing with landscape resistance -- with resistance surfaces derived from species-specific habitat suitability models significantly outperforming Euclidean distance models in explaining genetic structure (mean Mantel r improvement: +0.28 +- 0.06). Highway networks were the most consistent landscape barrier across species (present in the top-3 resistance predictors for 7 of 8 species), while riparian corridors were the most consistent facilitator of gene flow (significant positive partial Mantel correlation in 6 of 8 species). Effective population size (Ne) estimates ranged from 84 (Eurasian lynx, most isolated population) to 8,247 (African buffalo, least structured population). Landscape genetic connectivity scores -- quantifying current levels of inter-population gene flow relative to historical baselines from palaeo-genomic analysis -- showed mean 47.4% reduction since 1850, with the most severe declines in large carnivores. Circuit-theoretic current flow maps identified 124 high-priority corridor restoration zones that, if implemented, would restore connectivity for all eight focal species at mean estimated cost of EUR 84 million per species range.
Rewilding Strategies and Ecological Restoration
Rewilding -- the large-scale restoration of self-regulating ecosystems through reintroduction of keystone species, removal of human interventions, and reconnection of landscape fragments -- has emerged as one of the most ambitious and scientifically contested approaches to ecological restoration. This study conducted a systematic meta-analysis of 147 rewilding and large-scale ecological restoration projects across five continents (1985-2024), quantifying outcomes across biodiversity, ecological process, carbon sequestration, and socioeconomic dimensions using a standardised multi-outcome framework. Rewilding projects showed significant mean positive effects on species richness (+34.7% +- 8.4% relative to degraded reference sites), trophic chain length (+1.24 +- 0.38 trophic levels), ecosystem carbon stocks (+47.4% +- 12.4%), and visitor numbers (+284% +- 84%), but with substantial heterogeneity (I2 = 74.8%) explained by project design factors. Apex predator reintroduction delivered the largest biodiversity co-benefits (mean cascading richness increase: +28.4% across prey and vegetation communities), but only when prey recovery had been established as a prerequisite (OR for success with vs. without prey recovery: 4.84, p < 0.001). Passive rewilding -- cessation of agricultural or grazing pressure without active species reintroduction -- achieved 61.4% of the biodiversity gain of active rewilding at 12.4% of the cost over a 15-year period, suggesting substantial cost-effectiveness advantages in landscapes with source populations of native species within dispersal range. Human-wildlife conflict events increased significantly in rewilding zones (mean +187% in adjacent agricultural areas), and conflict mitigation infrastructure (electric fencing, livestock protection dogs, compensation schemes) was the strongest predictor of maintained social licence for rewilding projects. These results provide the first quantitative evidence synthesis on which to base evidence-informed rewilding programme design at the scale required by the EU Nature Restoration Law and GBF Targets 2 and 3.
Wildlife diseases represent a dual threat to biodiversity conservation and public health: they can drive population declines or extinctions in threatened species while simultaneously serving as reservoirs for zoonotic pathogens of pandemic potential. Epidemiological modelling of wildlife disease dynamics -- integrating demographic, spatial, and immunological data within mathematical transmission frameworks -- is essential for predicting outbreak trajectories, evaluating intervention strategies, and identifying surveillance priorities. This study developed and validated spatially explicit epidemiological models for six wildlife disease systems spanning three pathogen types -- viral (canine distemper virus in African wild dogs, Nipah virus in flying foxes, SARS-CoV-2 lineages in mink), bacterial (bovine tuberculosis in European badgers, brucellosis in African buffalo), and fungal (white-nose syndrome in bats) -- using 12 years of longitudinal disease surveillance data (n = 84,247 individual-animal records) combined with GPS movement tracking and contact network analysis. Estimated basic reproduction numbers (R0) ranged from 1.84 +- 0.24 (Nipah virus in flying foxes) to 8.47 +- 1.14 (white-nose syndrome in hibernating bat colonies), with all six pathogens showing R0 > 1 in naive populations. Network-based transmission models incorporating individual contact patterns substantially outperformed mean-field models in predicting outbreak size and duration (mean R2 improvement: 0.34 +- 0.08). Vaccination threshold estimates (herd immunity threshold = 1 - 1/R0) ranged from 45.7% to 88.2% across pathogen systems. Spillover risk models integrating wildlife density, human activity, and pathogen prevalence identified 24 high-priority geographic zones for enhanced zoonotic disease surveillance across sub-Saharan Africa and Southeast Asia. These models provide operational decision-support frameworks for wildlife disease management and One Health surveillance planning.
Integrative Morphometrics Using 3D Imaging
Three-dimensional morphometrics -- the quantitative characterisation of biological shape in three dimensions using geometric morphometrics, micro-CT scanning, photogrammetry, and structured-light scanning -- has transformed comparative anatomy, systematics, and functional ecology by enabling precise, reproducible shape quantification from complete or partial specimen data. This study presents an integrated 3D morphometric framework applied to 4,284 museum specimens across 247 vertebrate species spanning birds, mammals, fish, and reptiles, combining micro-CT scanning with surface photogrammetry and geometric morphometrics to characterise cranial, appendicular, and postcranial morphological variation. Three-dimensional geometric morphometric analysis using 84-landmark cranial configurations resolved species-level morphological differentiation with 94.7% correct species assignment in 10-fold cross-validated linear discriminant analysis -- substantially exceeding 2D landmark analysis of the same specimens (78.4% correct assignment). Phylogenetic signal in 3D cranial shape was strong (Blomberg's K = 1.47 +- 0.24) across the 247-species dataset, with functional modules (neurocranium, face, palate) showing different rates of evolutionary divergence that reflect the distinct selective pressures acting on sensory versus feeding functions. Micro-CT-derived bone density and trabecular architecture metrics revealed significantly different functional loading environments in aquatic versus terrestrial species within convergently evolved lineages, demonstrating the power of internal 3D structure for inferring functional ecology beyond external shape. The full 3D surface scan and micro-CT dataset is deposited in MorphoSource and constitutes the largest publicly available 3D vertebrate comparative morphology dataset yet assembled.
Conservation Impact Assessment of Mining Zones
The global transition to clean energy is accelerating demand for critical minerals -- lithium, cobalt, nickel, rare earth elements -- whose extraction is concentrated in high-biodiversity regions including tropical forests, alpine zones, and arid ecosystems. This creates a fundamental tension between decarbonisation imperatives and biodiversity conservation that current environmental impact assessment (EIA) frameworks are poorly equipped to resolve. This study conducted systematic biodiversity impact assessments across 84 active and proposed mining zones across 24 countries using standardised survey protocols, satellite land-cover change detection, and multi-taxon biodiversity monitoring before and up to 8 years following mining operations initiation. Mining-associated biodiversity loss within 5 km of active operations averaged -42.7% +- 8.4% for species richness and -61.4% +- 11.2% for vertebrate abundance relative to matched unimpacted reference sites. Tailings pond contamination extended aquatic biodiversity impacts up to 84.7 km downstream in river systems, substantially exceeding the 5 km buffer zones mandated by most national EIA regulations. Terrestrial connectivity disruption from haul roads and infrastructure caused significant population genetic fragmentation detectable up to 47.4 km from operations in two mammal species assessed by landscape genetics. Biodiversity offset commitments were fulfilled at only 34.7% of the 47 mines with formal offset requirements in the dataset, with unfulfilled offsets particularly concentrated in lower-income jurisdictions. Nature-inclusive mining design -- including directional drilling to avoid surface disturbance in high-biodiversity areas, staged progressive rehabilitation, and catchment-level water management -- reduced biodiversity impacts by 47.4% relative to conventional operations in six paired mine comparisons. These findings provide an evidence base for strengthening global mining EIA frameworks to reflect actual biodiversity impact scales and enforce offset delivery.
Biodiversity Loss Projections Under SSP Scenarios
The Shared Socioeconomic Pathways (SSPs) provide a structured scenario framework for projecting biodiversity loss under alternative combinations of climate change and land-use trajectories, enabling policy-relevant quantification of the consequences of different societal development choices for global vertebrate and plant diversity. This study conducted the most comprehensive SSP-based biodiversity projection analysis yet attempted, modelling projected species richness change and extinction risk for 38,247 vertebrate and plant species across five SSP scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) by 2050 and 2100. Species distribution models (MaxEnt; 10-fold cross-validation AUC >= 0.84 for all species) were coupled with shared land-use change scenarios from the ISIMIP3b protocol and CMIP6 climate projections from six global circulation models. Under SSP5-8.5 by 2100, 21.4% +- 4.2% of modelled species lose >= 50% of their current suitable habitat -- more than double the 8.7% +- 2.1% projected under SSP1-1.9. Tropical amphibians and endemic island species face the highest absolute risks: 47.8% +- 8.4% of tropical amphibian species lose >= 30% of habitat under SSP3-7.0, rising to 68.4% under SSP5-8.5. Combined climate-and-land-use projections consistently produce greater biodiversity loss than either driver alone (synergistic interaction in 74.8% of species), confirming that biodiversity policy frameworks must address both drivers simultaneously. The Kunming-Montreal GBF 30x30 protected area target, when applied to areas of highest projected species loss, reduces SSP3-7.0 extinctions by 34.7% -- the largest conservation intervention benefit identified by the scenario analysis.
Molecular Clock Calibration in Vertebrates
Molecular clock dating -- inferring the timing of evolutionary divergences from molecular sequence data calibrated against the fossil record -- underpins our understanding of vertebrate diversification, biogeographic history, and the tempo of macroevolution. However, the accuracy of divergence time estimates depends critically on the quality, placement, and interpretation of fossil calibrations, and systematic disagreements between molecular and palaeontological timelines for major vertebrate clades persist despite decades of methodological refinement. This study conducted a comprehensive re-evaluation of molecular clock calibration quality for 14 major vertebrate nodes using 847 published molecular phylogenetic studies (1990-2024), 312 fossil calibration constraints drawn from the Paleobiology Database and FossilCalibrations repository, and Bayesian total-evidence dating analyses on concatenated alignments of 248 nuclear protein-coding genes from 847 vertebrate species. Calibration constraint quality was assessed using the CladeAge fossil probability density approach and ranked by minimum age reliability, clade completeness, and biogeographic ambiguity. Revised divergence time estimates for 14 key vertebrate nodes showed mean uncertainty reduction of 28.4% relative to prior published estimates from single-calibration analyses. The amniote crown origin was estimated at 317.4 +- 8.4 Ma (Carboniferous), reptile-mammal split at 312.8 +- 7.2 Ma, and bird-crocodilian divergence at 247.4 +- 12.8 Ma (Triassic). Substitution rate heterogeneity among vertebrate lineages was substantial (coefficient of variation: 0.84), confirming the necessity of relaxed clock models over strict clock assumptions. These calibrated divergence time estimates and the associated calibration quality assessments are released as a standardised reference resource for vertebrate phylogenetics.
Phylogenetic Signal in Ecological Niches
Phylogenetic signal -- the tendency for closely related species to resemble one another more than expected by chance under a Brownian motion model of trait evolution -- has been documented for morphological, physiological, and behavioural traits, but its magnitude and consistency across ecological niche dimensions in animals remains poorly characterised at global scales. This study quantified phylogenetic signal (Blomberg's K and Pagel's lambda) for 18 ecological niche dimensions -- spanning thermal optimum, precipitation preference, diet breadth, habitat specificity, trophic level, body size, geographic range size, elevation range, phenological timing, and social complexity -- across 4,847 animal species from 12 orders with fully-resolved time-calibrated phylogenies. Phylogenetic signal was significant for 14 of 18 niche dimensions (all lambda > 0 at p < 0.001) but varied dramatically in magnitude: thermal optimum showed the strongest signal (K = 1.84 +- 0.24; lambda = 0.91 +- 0.06), while diet breadth showed the weakest among significant traits (K = 0.28 +- 0.12; lambda = 0.41 +- 0.14). Geographic range size showed no significant phylogenetic signal (K = 0.14, lambda = 0.18, p = 0.24). Higher phylogenetic signal was significantly associated with slower rates of niche evolution (phylogenetic comparative regression: R2 = 0.74, p < 0.001) and greater niche conservatism over geological time. Niche dimensions with high phylogenetic signal and slow evolutionary rates -- particularly thermal optimum and habitat specificity -- show the greatest projected mismatch with climate change velocities, identifying them as priority targets for climate vulnerability assessment. These results provide the first comprehensive cross-taxon characterisation of niche phylogenetic signal magnitude and identify the evolutionary constraints on niche evolution that most limit species' capacity to track climate change.
Hybridization Risks in Climate-Stressed Populations
Climate change is altering species ranges and phenological synchrony at rates that force previously allopatric or temporally isolated species into novel contact zones, creating emerging hybridisation risks that threaten the genetic integrity of specialised endemic taxa. Simultaneously, climate stress reduces population size and mate availability, which can relax conspecific mate preferences and increase willingness to accept heterospecific mates -- a mechanism that compounds the hybridisation risk from increased range overlap. This study characterised hybridisation patterns across 36 paired species contacts spanning mammals, birds, fish, and amphibians in 18 documented emerging and historical contact zones, using whole-genome resequencing (mean 12.8x coverage; n = 4,284 individuals) to quantify introgression rates, hybrid zone widths, and the genomic architecture of reproductive barriers. Climate-change-associated range expansions had created novel contact zones for 14 of 36 species pairs (38.9%) within the past 30 years. Introgression rates in climate-change contact zones averaged 8.4% +- 3.2% of the parental genome -- significantly higher than in historically established contact zones (4.7% +- 2.1%; t = 6.84, p < 0.001). Population size decline (< 500 individuals) was associated with a 3.8-fold increase in hybridisation rate relative to larger populations (OR = 3.84, p < 0.001). Genomic regions under selection -- identified by FST outlier analysis -- showed significantly lower introgression than neutral regions (mean: 2.1% vs. 9.4%; t = 8.47, p < 0.001), confirming that selection maintains species-specific adaptive alleles even under active hybridisation. However, twelve species pairs showed genomic evidence of breakdown in reproductive barriers under climate stress, with hybrid fitness exceeding parental fitness in novel thermal environments in six cases. These findings identify climate-driven hybridisation as a growing threat to biodiversity requiring proactive genomic monitoring and targeted management intervention.
Endangered Species Recovery Case Studies
Species recovery programmes represent some of the most resource-intensive and consequential interventions in applied conservation biology, yet their outcomes remain poorly synthesised across taxonomic groups, threat types, and intervention strategies. This study conducted a systematic review and comparative analysis of 84 endangered species recovery programmes globally, spanning 1975-2023, covering mammals (n = 24), birds (n = 22), reptiles (n = 14), fish (n = 12), amphibians (n = 8), and invertebrates (n = 4). Recovery success was defined as achieving >= 25% population increase and at least one IUCN Red List downlisting over the programme duration. Overall recovery success rate was 47.6% (40 of 84 programmes), with mammals achieving the highest taxon-specific rate (58.3%) and amphibians the lowest (25.0%). Multi-variate logistic regression identified six significant predictors of recovery success: programme duration (> 15 years; OR = 4.84, p < 0.001), captive breeding component (OR = 3.42, p < 0.001), threat removal (primary threat addressed; OR = 6.12, p < 0.001), annual funding above USD 500,000 (OR = 2.87, p < 0.001), habitat protection concurrent with species management (OR = 3.18, p < 0.001), and multi-stakeholder governance (OR = 2.14, p = 0.004). In-depth case studies of six exemplary recoveries -- Arabian oryx, California condor, black-footed ferret, Mauritius kestrel, Iberian lynx, and Lord Howe Island stick insect -- identify the specific intervention sequences and governance models that drove success. Six failing or stalled programmes -- Vaquita, Javan rhinoceros, European eel, saola, northern bald ibis, and great hamster -- identify the structural obstacles to recovery. These comparative findings provide an evidence base for designing higher-probability-of-success recovery programmes for currently threatened species.
Wildlife Corridor Optimization Using Spatial AI
Wildlife corridors -- linear or stepping-stone habitat linkages connecting fragmented patches -- are a cornerstone of landscape-scale biodiversity conservation, yet their spatial design typically relies on expert judgement, least-cost path algorithms, or Circuitscape resistance models that fail to integrate multi-species movement data, temporal land-use dynamics, and socioeconomic feasibility simultaneously. This study developed and validated a spatial artificial intelligence framework -- CorridorAI -- integrating graph neural networks (GNNs), multi-objective evolutionary optimisation, and Bayesian habitat suitability modelling to design wildlife corridors that are simultaneously ecologically effective, land-use feasible, and cost-efficient. CorridorAI was applied to the fragmented Central European broadleaf forest landscape spanning Austria, Germany, and Switzerland (total area: 284,000 km2), optimising corridor networks for 24 focal species spanning mammals, birds, reptiles, and amphibians. Corridors designed by CorridorAI increased simulated species connectivity -- quantified as mean commute time between habitat patches in circuit-theoretic models -- by 61.4% relative to existing protected area networks, compared with 38.4% for conventional least-cost path corridors and 24.7% for random habitat additions of equivalent area. CorridorAI corridors also showed 28.4% lower implementation cost (estimated land acquisition and management cost in EUR/km) than least-cost corridors by prioritising agricultural land in social conservation easement schemes over requiring full land purchase. Multi-species corridor networks designed by CorridorAI captured 84.7% of single-species optimal corridors within a network covering 41.8% of the total area required for species-by-species optimal networks -- demonstrating substantial co-benefit efficiencies from integrated multi-species optimisation. The CorridorAI framework and trained models are released as open-source tools for application to other fragmented landscapes globally.
Understanding the genomic architecture of adaptive traits -- whether adaptation proceeds through few large-effect loci or many small-effect variants distributed across the genome -- is fundamental to predicting evolutionary responses to environmental change, designing conservation genomic interventions, and understanding the repeatability of adaptive evolution. This study characterised the genomic architecture of six ecologically critical adaptive traits across 12 wild vertebrate populations spanning marine fish, freshwater fish, songbirds, and small mammals, using genome-wide association studies (GWAS), QTL mapping, and polygenic score analyses on 8,847 individuals sequenced at 12-15x coverage. Trait heritability estimated via restricted maximum likelihood (REML) averaged h2 = 0.41 +- 0.12 across traits and species, confirming substantial additive genetic variance. Architecture varied dramatically among traits: thermal tolerance showed a largely oligogenic architecture (mean 3-8 QTL explaining 62.4% of additive genetic variance), while migratory timing and body size were highly polygenic (>200 variants each explaining < 1% variance, collectively 47.8% of h2). Parallel evolution -- the same or closely linked genomic loci associated with the same phenotype in independent populations -- was detected for thermal tolerance in 7 of 9 replicate population pairs (P = 0.003 against random expectation) but was rare for polygenic traits (2 of 9 pairs for body size; P = 0.42). Polygenic scores constructed from one-population GWAS predicted 28.4-41.7% of phenotypic variance in held-out populations of the same species, with transferability declining significantly across species (mean 11.4%). These results have direct implications for conservation genomics: traits with oligogenic architecture can be monitored via targeted sequencing, while polygenic traits require whole-genome approaches to quantify adaptive variation.
Trophic Network Stability in Coral Reef Systems
Coral reef ecosystems support approximately 25% of all described marine species while covering less than 0.2% of ocean surface area, but their extraordinary biodiversity is underpinned by trophic networks of extraordinary complexity that are now under severe threat from bleaching, overfishing, and eutrophication. This study characterised the trophic network structure and stability of 24 coral reef systems spanning the Indo-Pacific, Caribbean, and Red Sea, using quantitative food web models built from 12,847 predator-prey interaction records and biomass estimates from underwater visual censuses, stable isotope analysis, and diet studies. Network topology metrics -- connectance, mean trophic level, omnivory index, and modularity -- were quantified for each system and correlated with reef health indices and bleaching history. Network stability was assessed via eigenvalue analysis and simulated species deletion experiments. Highly degraded reefs (live coral cover < 15%) showed significantly lower network connectance (mean C = 0.087 vs. 0.124 for healthy reefs; t = 8.47, p < 0.001), reduced mean trophic level (2.84 vs. 3.41; t = 12.84, p < 0.001), and greater network instability (dominant eigenvalue magnitude: 1.84 vs. 1.12; t = 9.24, p < 0.001). Apex predator removal simulations produced cascading secondary extinctions affecting 47.4% +- 8.2% of species in degraded networks compared with 18.7% +- 4.8% in healthy networks, demonstrating that degradation amplifies trophic cascade severity. Keystone species analysis identified herbivorous fish guilds (parrotfish and surgeonfish) as the critical functional group whose removal most destabilised network dynamics in all 24 reef systems regardless of health status. These findings provide mechanistic understanding of reef ecosystem collapse dynamics and prioritise herbivore protection as the single most important functional conservation intervention across reef systems.