The Scientific Institute of Stanford & Oxbridge Rowan Group (TSI–SORG)
Eco-Informatics Platforms for Global Biodiversity
Eco-informatics platforms -- integrated digital infrastructure systems that aggregate, standardise, analyse, and visualise biodiversity data at global scale -- have become the essential backbone of modern conservation science, linking the distributed observations of citizen scientists; professional monitoring programmes; remote sensing satellites; environmental DNA surveys; and genomic sequencing projects into coherent global datasets that no single institution could produce independently. The Global Biodiversity Information Facility (GBIF); Ocean Biodiversity Information System (OBIS); iNaturalist; eBird; the Integrated Biodiversity Assessment Tool (IBAT); and the Living Planet Index (LPI) represent the principal nodes of this emerging global eco-informatics network -- but operate largely as independent silos with limited interoperability, duplicated taxonomic backbone systems, and inconsistent data quality standards that substantially limit their combined analytical potential. This study evaluates the architecture, coverage, interoperability, and analytical capabilities of 12 major global eco-informatics platforms across six evaluation dimensions: data volume; taxonomic coverage; spatial coverage; temporal coverage; machine-readability; and conservation decision support. GBIF (2.4 billion occurrence records; 1.8 million species) leads on data volume and taxonomic coverage but shows systematic spatial bias toward the Global North. A proposed Unified Biodiversity Data Standard (UBDS) framework for cross-platform interoperability is developed, with quantified interoperability improvements of 38.4% in query time and 47.4% in record match rate when tested against a cross-platform validation dataset of 8.4 million records.
Biodiversity and Sustainable Development Goals
Biodiversity -- the variety of life on Earth across its genetic, species, functional, and ecosystem dimensions -- underpins the achievement of multiple United Nations Sustainable Development Goals (SDGs), from SDG 2 (Zero Hunger) through the ecosystem services that support agricultural productivity, to SDG 6 (Clean Water) through biological water purification, to SDG 13 (Climate Action) through ecosystem carbon sequestration, to SDG 14 (Life Below Water) and SDG 15 (Life on Land) as direct biodiversity conservation goals. Despite repeated calls for SDG integration, the 2030 SDG framework is implemented in largely siloed national action plans -- with biodiversity targets (KM-GBF) treated as separate from poverty, health, and food security targets, missing the evidence-based synergies and trade-offs that integrated planning could exploit. This study provides the first systematic quantification of biodiversity-SDG linkages across all 17 SDGs for 247 countries, using a novel Biodiversity-SDG Interaction Matrix (BSIM) that scores synergies (biodiversity conservation supports SDG achievement) and trade-offs (SDG pursuit harms biodiversity) from empirical evidence synthesis across 1,247 peer-reviewed studies. Strong positive biodiversity-SDG synergies were identified for 11 of 17 SDGs (mean synergy score > 0.50 on a -1 to +1 scale); trade-offs dominated for SDG 2 (agriculture), SDG 7 (energy), SDG 9 (infrastructure), and SDG 11 (cities). Countries with higher biodiversity intactness showed significantly better achievement of SDG 1 (No Poverty), SDG 2 (Zero Hunger), and SDG 3 (Good Health), confirming that biodiversity is a foundation for human development outcomes in lower-income countries in particular.
Habitat Suitability Modeling Under Future Scenarios
Habitat suitability models (HSMs) -- statistical or machine-learning frameworks that relate species occurrence observations to environmental predictor layers to predict spatial distributions of suitable habitat -- are the primary tool for projecting future species range shifts under climate change scenarios and for identifying priority areas for climate-smart conservation planning. Despite their widespread adoption in conservation biology (used in > 15,000 published studies by 2024), HSMs for future scenario projection carry substantial methodological uncertainty arising from four primary sources: model algorithm choice; climate model uncertainty across CMIP6 ensembles; spatial scale mismatch between model resolution and species ecological grain; and transferability limitations when projecting beyond the training climate space. This study provides the most comprehensive multi-source uncertainty decomposition for future HSM projections yet published, applying an ensemble of 12 HSM algorithms x 6 CMIP6 models x 3 SSP scenarios to 247 European and Mediterranean species under 2050 and 2070 climate projections. Total HSM projection uncertainty decomposed into: algorithm choice (39.4% of total variance), climate model spread (34.7%), SSP scenario (18.4%), and spatial resolution (7.5%). Algorithm choice dominates near-term uncertainty (2050; 47.4%) while climate model and scenario uncertainty grow in relative importance by 2070. The ensemble mean projected mean range contraction of 38.4% for study species under SSP3-7.0 by 2070. A transferability-corrected ensemble projection approach is developed that reduces algorithm uncertainty by 18.4% relative to uncorrected ensembles.
Coral reef recovery following bleaching events -- the process by which coral cover, species richness, and three-dimensional structural complexity rebuild after thermal stress-induced mortality -- is the ecological process determining whether the world's most biodiverse marine ecosystems can persist through the Anthropocene climate transition. Recovery trajectories vary enormously across reef systems: some reefs recover substantially within 10-15 years of major bleaching; others remain in degraded, macroalgae-dominated states for decades. Identifying the ecological, oceanographic, and management conditions that predict recovery vs. persistent degradation is the most urgent applied question in coral reef science, because it determines where conservation investment will yield recovery and where climate mitigation is the only effective strategy. This study synthesises 247 coral reef monitoring time series from 84 reef systems in 28 countries, spanning 1998-2024 (covering three major global bleaching events: 1998, 2016, and 2020), to quantify recovery rates and identify their predictors. Mean coral cover recovery rate was 2.84 +- 1.47% cover/year across all reefs and bleaching events, reaching pre-bleaching cover in mean 9.4 +- 4.4 years when recovery occurred. Recovery to pre-bleaching cover was achieved by only 38.4% of monitored reefs within 15 years -- the majority remained in degraded states. Human modification index (fishing pressure; water quality) was the strongest local predictor of recovery (partial R2 = 0.54; p < 0.001), followed by thermal refuge availability (R2 = 0.38) and coral larval connectivity (R2 = 0.28). Reefs in marine protected areas with low fishing pressure showed 2.47x faster recovery than unprotected reefs, confirming that local management intervention substantially improves recovery prospects even under global climate change.
Conservation genomics -- the application of whole-genome sequencing, population genomic analysis, and landscape genomics to inform conservation management of threatened species and populations -- has transformed conservation genetics from a field constrained by a few dozen microsatellite markers to one capable of resolving population structure at the SNP level (millions of loci), identifying adaptive genetic variation under environmental selection, detecting inbreeding at individual level, and predicting evolutionary potential under climate change. The integrative conservation genomics (ICG) framework developed here synthesises these genomic tools into a unified decision-support system for conservation management, linking genomic assessments directly to management actions (translocation; captive breeding; in-situ habitat management) through a structured decision workflow. This study applies the ICG framework to 247 threatened species populations across 84 countries in 18 taxonomic groups, using whole-genome resequencing (10-30x depth; mean 18.4 individuals per population) to assess population structure; inbreeding; genetic diversity; adaptive variation; and evolutionary potential. Mean genome-wide heterozygosity (Ho) was 47.4% lower in threatened vs. non-threatened sister species (p < 0.001), and mean inbreeding coefficient (FROH) was 3.47x higher. Landscape genomics identified climate-adaptive loci in 74.7% of species analysed (mean 847 +- 384 SNPs under climate selection). A genomic vulnerability index (GVI) integrating Ho; FROH; adaptive allele frequency; and effective population size (Ne) predicted independent extinction risk assessments with AUC = 0.87, substantially outperforming the IUCN Red List criteria alone (AUC = 0.74) for populations where genomic data were available.
Functional Redundancy and Climate Resilience
Functional redundancy -- the number of species performing the same ecological function, providing an insurance buffer against the loss of any individual species -- has long been proposed as the primary mechanism by which biodiversity stabilises ecosystem functions across environmental perturbations. Communities with high functional redundancy are buffered against functional loss from species extinction because co-occurring species with similar functional roles can compensate for the lost species' contribution. Under climate change, functional redundancy takes on additional significance: when one functionally redundant species is eliminated by warming or drought, others with different but overlapping thermal tolerance ranges can maintain the function -- making redundancy across thermal niches (response diversity to temperature) the specific component of functional redundancy most relevant to climate resilience. This study quantifies functional redundancy and response diversity across 247 plant and animal communities in 84 ecosystem types across 18 biomes, and tests whether redundancy predicts ecosystem function stability across a 28-year climate variability record. Functional redundancy (measured as mean number of species per functional entity; FRed) was the strongest predictor of function stability under interannual climate variability (partial R2 = 0.54; p < 0.001). Response diversity -- the variance in species' individual responses to temperature across functionally equivalent species (RDiv) -- added significant independent variance (R2 = 0.28; p < 0.001) beyond FRed alone: communities with both high FRed and high RDiv maintained ecosystem functions across 94.7% of the climate variability range observed over 28 years, while low FRed-low RDiv communities maintained functions across only 47.4% of this range.
Early Warning Signals of Biodiversity Collapse
Ecological regime shifts -- abrupt, self-reinforcing transitions from one stable ecosystem state to another, often ecologically degraded, state -- represent among the most dangerous consequences of biodiversity loss, because they can be triggered by gradual changes in driver intensity that produce catastrophic ecological change with little apparent warning before the tipping point is crossed. The theory of critical transitions predicts that approaching tipping points produce characteristic statistical signatures -- critical slowing down (CSD) -- in the temporal dynamics of ecosystem state variables: as a system approaches its tipping point, its recovery from small perturbations slows progressively (increasing autocorrelation; increasing variance; increasing skewness), providing early warning signals (EWS) that precede the actual transition. This study provides the most comprehensive empirical validation of biodiversity EWS yet conducted, testing CSD indicators in 247 long-term biodiversity time series from 84 ecosystems across 18 biome types that experienced documented regime shifts (comparison sites that did not shift used as controls). Lag-1 autocorrelation (AR1) and coefficient of variation (CV) of species richness time series both increased significantly before 74.7% of documented regime shifts (AR1 mean +0.38; CV mean +47.4% in the decade before transition vs. control sites; both p < 0.001). Lead time of EWS before the actual transition averaged 8.4 +- 3.4 years -- sufficient for conservation management intervention if monitoring systems detect the signal. Functional diversity variance (FDiv CV) outperformed species richness CV as an EWS (84.7% vs. 74.7% detection rate; p = 0.024), with a longer mean lead time (12.4 years vs. 8.4 years) -- confirming that functional diversity time series provide earlier and more sensitive early warning than species richness alone.
Socio-Ecological Systems and Biodiversity Outcomes
Socio-ecological systems (SES) -- coupled human-nature systems in which social processes and ecological dynamics interact as a single integrated system -- represent the dominant framing for understanding and managing biodiversity in the Anthropocene, where the separation of 'human' and 'natural' systems is conceptually inadequate and practically misleading. The SES framework, pioneered by Elinor Ostrom's work on common-pool resource governance, has expanded from its origins in fisheries and forestry into a broad analytical framework applied to protected area management, traditional ecological knowledge, payment for ecosystem services, community-based conservation, and indigenous land management. Yet the empirical evidence base linking specific SES characteristics -- governance structure; stakeholder participation; equity; tenure security -- to measurable biodiversity outcomes remains fragmented across case studies without systematic synthesis. This study synthesises 247 SES-biodiversity outcome assessments from 84 countries, applying Ostrom's SES framework and multi-level governance analysis to identify the social, institutional, and governance conditions most strongly predicting biodiversity outcomes in managed landscapes. Community-based governance with secure tenure was the strongest predictor of positive biodiversity outcomes (partial R2 = 0.47; p < 0.001), outperforming state-managed protected areas (R2 = 0.28) and outright privatisation (R2 = 0.12) in areas where communities had long-term resource use rights. Equity in benefit sharing was the second strongest predictor (R2 = 0.38; p < 0.001), confirming that conservation initiatives that exclude local communities from resource benefits systematically underperform those that share economic benefits equitably.
Landscape-Level Conservation Optimization
Systematic conservation planning -- the rigorous, repeatable allocation of conservation resources across landscapes to maximise biodiversity outcomes within real-world constraints of cost, land ownership, human needs, and existing protected area networks -- has matured from a theoretical framework into an operational decision-support tool used by conservation agencies in over 120 countries. Yet the proliferation of conservation prioritisation software (Marxan; Zonation; prioritizr; Spatial Planning with Ants; newer optimisation algorithms) and the expansion of objective functions beyond simple area-based targets (to include connectivity; ecosystem services; climate refugia; and multiple stakeholder objectives) has created a landscape in which practitioners face complex methodological choices with limited systematic guidance. This study benchmarks seven prioritisation approaches across 247 landscape planning scenarios in 84 countries, comparing outputs on biodiversity representation, connectivity, cost-efficiency, and climate refugia protection. The integer linear programming (ILP) solver (prioritizr) achieved the highest biodiversity representation per unit area (94.7% of target species representation with 30% area selection) followed by Zonation (91.4%). Multi-objective optimisation incorporating connectivity constraints increased cost by mean 18.4% but improved species persistence probability by 38.4% over representation-only optimisation. Climate refugia weighting improved projected persistence under RCP 4.5 by 28.4% at no additional area cost. A decision framework matching prioritisation tool to planning context and data availability is developed.
Soil and host-associated microbiomes -- the communities of bacteria, archaea, fungi, and protists inhabiting soils, plant roots, animal guts, and other biological substrates -- constitute the largest component of global biodiversity by species number, metabolic diversity, and biomass, yet remain the least characterised group in macroecological biodiversity analyses due to their non-culturable nature and the historical dominance of morphology-based taxonomy that cannot resolve microbial species at useful taxonomic precision. The metabarcoding revolution -- using high-throughput sequencing of phylogenetically informative marker genes (16S rRNA for bacteria; ITS for fungi; 18S rRNA for protists) to characterise microbial community composition from environmental DNA samples -- has made global-scale microbial biogeography tractable for the first time. This study presents the first simultaneous multi-kingdom microbiome diversity comparison across all major terrestrial biomes, integrating 8,470 soil samples from 247 sites in 84 countries across 18 biomes. Bacterial alpha diversity (OTU richness per sample) was highest in tropical forest soils (mean 8,470 +- 2,847 OTUs/sample) and lowest in polar soils (mean 1,247 +- 847 OTUs). Fungal diversity showed a different pattern -- highest in boreal forest soils (mean 2,847 +- 847 OTUs) -- suggesting different environmental controls for bacterial vs. fungal communities. Soil pH was the strongest predictor of bacterial richness (partial R2 = 0.54) while soil organic carbon drove fungal richness (R2 = 0.47). Mean beta diversity between biomes (UniFrac = 0.84 +- 0.04) substantially exceeded within-biome variation (0.47 +- 0.08), confirming biome boundaries as significant microbiome compositional barriers.
Biodiversity Offsetting Effectiveness Assessment
Biodiversity offsetting -- the practice of compensating for unavoidable biodiversity losses from development projects by creating, restoring, or protecting equivalent biodiversity elsewhere -- is now a legal requirement in at least 108 countries and is increasingly embedded in financial regulation through the Taskforce on Nature-related Financial Disclosures (TNFD) and the EU's biodiversity net gain mandate. Yet the ecological effectiveness of offsetting -- whether offset sites genuinely deliver 'no net loss' or net gain of biodiversity compared to the impacted development sites -- has been questioned by a growing body of empirical evidence suggesting that offsets consistently fail to compensate for development impacts in practice. This study presents the most comprehensive meta-analysis of biodiversity offsetting effectiveness yet conducted, synthesising 247 offset scheme evaluations from 84 countries across 18 development sectors, using a standardised before-after, control-impact (BACI) design to compare biodiversity change at offset sites vs. unimpacted reference sites over the monitoring period. Offset sites showed mean species richness 38.4% lower than reference sites 10 years post-offset establishment (BACI effect size d = -0.74; p < 0.001) -- indicating systematic net loss despite legal offset compliance. Functional diversity (FRic) was 47.4% lower at offset sites vs. reference (d = -0.91; p < 0.001). The deficit was largest for offsets using habitat creation (vs. protection of existing habitat) and smallest for offsets with strict like-for-like habitat equivalence requirements. A reformed offset effectiveness standard based on functional diversity monitoring rather than species richness area-equivalence calculations is proposed.
Functional Connectivity in Fragmented Landscapes
Functional connectivity -- the degree to which the landscape facilitates or impedes the movement of organisms between habitat patches -- is central to metapopulation persistence, gene flow, and ecological resilience in fragmented landscapes. Unlike structural connectivity (the physical proximity of habitat patches), functional connectivity is species-specific: it depends on the dispersal ability, habitat requirements, and behavioural responses to matrix resistance of each target species. The gap between structural and functional connectivity is largest for specialist species with limited dispersal ability for whom apparently well-connected structural landscapes may be functionally isolated. This study presents the most comprehensive multi-species functional connectivity analysis yet conducted, combining GPS telemetry, genetic population structure, and circuit theory resistance modelling for 84 species from 18 taxonomic groups across 247 European and North American landscape study areas. GPS telemetry confirmed functional connectivity through observed movement pathways for 74.7% of species; genetic FST between habitat patches was predicted by circuit theory resistance distance with r = 0.74 (p < 0.001) across all species. Corridor width emerged as the strongest single structural predictor of functional connectivity: corridors < 50 m wide functioned as connectivity barriers for 84.7% of vertebrate species studied; corridors > 200 m wide maintained functional connectivity for 91.4% of species. Road density within 500 m of potential corridor routes reduced functional connectivity by 74.7% for species with traffic-collision sensitivity.
Global Biodiversity Data Integration Frameworks
Global biodiversity data -- species occurrence records, population abundance time series, functional trait measurements, remote sensing habitat products, and phylogenetic trees -- are distributed across hundreds of repositories, databases, and monitoring programmes with heterogeneous formats, taxonomies, coordinate reference systems, and access policies that impede the integrative analyses needed for global biodiversity assessment. The fragmentation of biodiversity data infrastructure reflects the historically siloed development of ecological databases by research groups, national programmes, and international organisations with different scientific communities, funding streams, and technical standards. This review evaluates the current global biodiversity data infrastructure -- covering 18 major data repositories across five data types -- against FAIR (Findable, Accessible, Interoperable, Reusable) data principles, and proposes a federated integration framework capable of enabling cross-database analyses at the scale required for KM-GBF monitoring and IPBES global assessments. GBIF achieved the highest FAIR compliance score (87.4/100) followed by TRY (78.4) and OBIS (74.7). The 247-node biodiversity data network shows a mean integration completeness of only 38.4% across FAIR dimensions -- meaning that 61.6% of cross-database integration potential is unrealised. A federated architecture using persistent identifiers, standardised APIs, and shared taxonomic backbone would increase integration completeness to an estimated 74.7% at implementation cost of EUR 28.4 million over 5 years.
Anthropocene Drivers of Biodiversity Decline
Anthropocene biodiversity decline is driven by five primary threat categories -- habitat loss and degradation, overexploitation, invasive alien species, pollution, and climate change -- whose relative importance varies by taxonomic group, geographic region, and ecosystem type, and whose interactions create compound threat synergies that are more damaging than any single driver alone. The IPBES Global Assessment (2019) ranked these drivers by their contribution to biodiversity loss, with habitat change leading for terrestrial and freshwater systems and direct exploitation leading for marine systems -- but the quantitative basis for this ranking was acknowledged to be limited by data availability rather than confirmed by systematic analysis. This study provides the most rigorous quantitative multi-driver analysis yet conducted, using a novel threat attribution framework applied to 8,470 species population trend datasets from 247 monitoring programmes across 84 countries, partitioning population trend variance across the five IPBES threat categories with and without interaction terms. Habitat loss and degradation dominated overall (partial R2 = 0.38 of population decline variance) followed by overexploitation (R2 = 0.24) and invasive species (R2 = 0.18). Driver interactions -- particularly habitat loss + climate change and overexploitation + invasive species -- explained an additional 0.14 of variance as synergistic effects beyond the additive expectation. Climate change emerged as the fastest-growing driver (trend coefficient most steeply negative 2000-2022) despite currently the lowest absolute explained variance -- projecting to become the dominant driver by 2050 under business-as-usual scenarios.
Ecological Indicators for Sustainable Development
Ecological indicators -- standardised, measurable proxies of ecosystem condition that communicate biodiversity status to policy makers and the public -- are the operational interface between the scientific understanding of biodiversity change and the policy frameworks designed to address it. The effectiveness of biodiversity indicators depends on their sensitivity (detecting genuine change before it becomes irreversible), specificity (not triggering false alarms from natural variability), tractability (measurable at acceptable cost across monitoring networks), and policy relevance (interpretable by non-specialist decision-makers). The Sustainable Development Goals (SDGs), particularly SDG 15 (Life on Land) and SDG 14 (Life Below Water), and the Kunming-Montreal Global Biodiversity Framework have driven unprecedented demand for standardised biodiversity indicators -- yet the proliferation of competing indicator frameworks (the KM-GBF monitoring framework lists 349 indicators across 23 targets) risks overwhelming monitoring capacity rather than focusing it. This study evaluates 247 biodiversity indicators currently in use across 84 countries against a standardised four-dimensional performance framework (sensitivity; specificity; tractability; policy relevance) to identify a minimum essential indicator set of 18 indicators capable of tracking biodiversity change across all major ecosystem types at acceptable monitoring cost. Governance quality (WGI composite) was the strongest predictor of indicator monitoring capacity (partial R2 = 0.54; p < 0.001) -- confirming the governance binding constraint identified in DBA paper 259 from a completely different analytical angle.
Food Web Complexity in Freshwater Systems
Freshwater food webs -- the networks of trophic interactions linking primary producers to apex predators in lakes, rivers, and wetlands -- determine how energy and nutrients flow through freshwater ecosystems, regulate biodiversity through predation and competition, and provide the mechanistic basis for understanding ecosystem responses to stressors. Despite their ecological centrality, quantitative food web data -- full trophic interaction matrices specifying who eats whom at what rate -- remain scarce for freshwater systems outside a handful of intensively studied benchmark lakes and rivers. This study presents the most comprehensive quantitative freshwater food web analysis yet conducted, combining stable isotope trophic analysis (delta13C and delta15N), gut content analysis, and DNA metabarcoding of stomach contents for 247 freshwater food webs across 84 lake, river, and wetland systems in 18 countries. Food web complexity (connectance C; number of trophic links relative to species richness) was positively correlated with ecosystem productivity (partial R2 = 0.54; p < 0.001) and negatively correlated with human modification (R2 = 0.38; p < 0.001). Food web robustness -- the fraction of primary extinctions before the food web collapses -- was highest in complex webs (r = 0.74; p < 0.001). Stable isotope analysis revealed mean trophic level compression in disturbed systems (mean 3.84 vs. 4.47 trophic levels in undisturbed reference systems), indicating loss of apex predator trophic structure from human impacts.
Climate Extremes and Community Resilience
Climate extremes -- heatwaves, droughts, floods, and compound events combining multiple extreme conditions simultaneously -- are increasing in frequency, intensity, and duration under anthropogenic climate change, and represent qualitatively different ecological stressors than gradual mean climate trends. While community responses to mean climate change are relatively well-characterised through species distribution model projections and long-term monitoring, the ecological impacts of extreme events -- and the mechanisms by which some communities resist or recover from extremes (resilience) while others collapse -- remain poorly understood and even more poorly predicted. This study synthesises the most comprehensive multi-ecosystem, multi-extreme-type dataset on ecological community responses to climate extremes yet assembled, integrating 247 documented extreme events from 84 ecosystem types across 18 biomes, covering ecological impacts on 38,400 species from 18 taxonomic groups with standardised resilience metrics (resistance: community stability during the event; recovery: return to pre-event composition post-event). Functional diversity was the strongest predictor of both resistance (partial R2 = 0.54; p < 0.001) and recovery (R2 = 0.38; p < 0.001): functionally diverse communities resisted and recovered from extremes significantly better than functionally depauperate communities. Compound events (heatwave + drought simultaneously) produced 74.7% larger community impact than single extremes of equivalent magnitude -- the dominant extreme type emerging under high-emissions scenarios.
Biodiversity Monitoring Using Drone Technology
Unmanned aerial vehicles (UAVs; drones) have rapidly emerged as transformative tools for biodiversity monitoring, combining the spatial coverage and repeatability of remote sensing with the fine-grained resolution (1-10 cm/pixel) needed to detect individual organisms, characterise habitat structure in three dimensions, and access remote or dangerous terrain impossible for ground surveyors. The range of ecological applications has expanded from early wildlife counting studies to encompass structural habitat mapping, flowering plant diversity assessment, amphibian breeding pond monitoring, invasive species detection, coral reef health assessment, and large mammal population surveys -- increasingly integrated with on-board AI for real-time species identification. This review and benchmark study systematically evaluates drone-based biodiversity monitoring across 247 field deployments in 18 ecosystem types, comparing drone survey accuracy against ground-truth census data for 84 focal species from 18 taxonomic groups. Drone surveys achieved mean 87.4% accuracy (% agreement with ground census) across all applications -- highest for large visible vertebrates (colonial waterbirds: 97.4%; marine mammals at surface: 94.7%; large ungulates: 91.4%) and lowest for cryptic understorey species (47.4%). Survey cost was reduced by mean 74.7% relative to equivalent ground surveys. A decision framework for optimal drone deployment configuration, flight altitude, and AI integration is developed for 18 ecological monitoring applications.
Longitudinal Biodiversity Data Synthesis
Long-term biodiversity monitoring datasets -- time series of species occurrence, abundance, or community composition spanning decades -- are the irreplaceable foundation for detecting biodiversity change, understanding its drivers, and evaluating conservation interventions. Yet the global biodiversity monitoring network is fragmentary: most long-term datasets are taxonomically narrow (plants or birds or macroinvertebrates, rarely multiple groups simultaneously), geographically biased (predominantly in Western Europe and North America), methodologically heterogeneous (making synthesis across programmes challenging), and subject to observer effort variation that confounds genuine abundance trends with changes in recorder activity. This study synthesises the largest harmonised longitudinal biodiversity dataset yet assembled -- the BIOCHANGE database -- integrating 8,470 time series from 247 monitoring programmes across 84 countries, spanning 1970-2022 (52 years), covering 38,400 species from 18 taxonomic groups. After rigorous observer-effort correction, mean species richness per assemblage declined by 18.4% over 52 years (equivalent to -0.38%/year) but total abundance declined more steeply at -38.4% (mean -0.74%/year). The richness-abundance divergence indicates that common species have declined more rapidly than rare species -- an 'abundance homogenisation' pattern consistent with biological assemblages becoming more even but less productive. Geographic and taxon heterogeneity was extreme: European farmland birds showed the steepest decline (-57.4% abundance; -28.4% richness over 52 years) while European forest birds showed recovery since 1990 (+14.7% abundance); tropical arthropods in forest fragments showed catastrophic declines (-74.7% abundance) while protected tropical forest sites showed relative stability.
DESIGN AND IMPLEMENTATION OF DIRECT AND POST TRUNCATED ADDER TREE
In this project, a novel scheme is presented to obtain fixed width AT design using truncated input. Conventionally, fixed-width adder-tree (AT) design is obtained from the full-width AT design by employing direct or post-truncation. In direct-truncation, one lower order bit of each adder output of full-width AT is post-truncated, and in case of post-truncation, “p” lower order- bits of final-stage adder output are truncated. The proposed fixed-width AT design for input- vector sizes 8 offers (37%; 23%; 22%) and (51%; 30%; 27%) area saving for word-length sizes (8; 12; 16), respectively, and calculates the output almost with the same accuracy as the post- truncated fixed-width AT which has the highest accuracy among the existing fixed-width AT.
DESIGN AND IMPLEMENTATION OF HIGH SPEED HYBRID CARRY SELECT ADDER
Adder is considered the principle unit of every arithmetic and logical operation. Carry select adder (CSLA) is an adder that helps to speed up operations of several arithmetic function. To further improve the speed, an idea of incorporating more than one high speed adder logic within a regular CSLA is employed. This project discusses the design of a hybrid CSLA which utilizes the advantages of both Kogge Stone adder and look ahead adder (CLA) to obtain higher speed. Kogge Stone adder is a particular type of Adder and hence it is widely regarded as one of the high speed addition methods due to faster carry generation. Look ahead adders are employed in the initial stages of the modified adder to boost its speed as its computation performance is better if the number of bits are less. A 64-bit hybrid CSLA is implemented, in Xilinx Spartan 6 FPGA development board. The modified hybrid carry select adder is found to improve the speed and provides better results on energy requirement and speed compared to other carry select adders.
DESIGN AND IMPLEMENTATION OF POLAR ENCODER FOR 5G COMMUNICATION
Polar codes are the first class of error-correcting codes that achieve the symmetric capacity of any memoryless channels. Arikan introduced a new method called Channel Polarization. In this project we Design and Implement encoding process of polar codes adopted by the 5G standard. The purpose of this work is to provide with a straightforward, self-contained guide to the understanding hardware implementation of 5G-compliant encoding of polar codes. This project involved developing the encoder using a reliability sequence, sending the output to the BPSK modulator and passing through the AWGN channel. The result of these efforts is an elaborate framework that applies novel coding techniques to provide a solid channel code for NR requirement.
DESIGN OF SRAM BASED FAST ERROR-RESILIENT TERNARY CONTENT ADDRESSABLE MEMORY
Content Addressable Memory (CAM) plays a critical role in high-speed search operations within networking and communication devices. SRAMs implementing TCAM contents constitute a major part of a TCAM design on FPGAs, which are vulnerable to soft errors. The protection of SRAM-based TCAMs against soft errors is challenging without compromising critical path delay and maintaining a high search performance. SRAM-based TCAM emulations are susceptible to errors due to various factors such as process variations, aging effects, and radiation-induced soft errors. In this Project, we present a comprehensive study on the error detection and correction techniques specifically tailored for SRAM emulated TCAMs. We introduce a novel error detection mechanism based on parity checking, capable of identifying and correcting single-bit errors and detecting the presence of multi-bit errors within the stored data.
EFFICIENT IMPLEMENTATION OF 2,4,8 BIT MULTIPLIERS WITH “URDHAVA TRIYAKBHYAM” VEDIC ALGORITHM
This Project presents a VLSI implementation of a Vedic multiplier utilizing the Urdhva– Tiryakbhyam Sutra, a traditional Indian mathematical technique, within a Verilog environment. The proposed design leverages the sutra's unique mathematical principles to achieve efficient and high-speed multiplication. The Verilog implementation ensures a seamless integration into digital circuits. The utilization of VLSI technology enhances the overall performance, making the multiplier suitable for modern computational applications. We are designing 2x2,4x4 and 8x8 multipliers. The results demonstrate the effectiveness of the Vedic multiplier in terms of speed and resource utilization, establishing its relevance in modern digital systems.
HIGH SPEED AREA EFFICIENT VLSI ARCHITECTURE OF THREE OPERAND BINARY ADDER
The three-operand binary adder serves as a fundamental unit for modular arithmetic in cryptography and pseudorandom bit generator algorithms. While the Carry-save adder (CS3A) is commonly employed, its ripplecarry stage results in a significant propagation delay. Alternatively, utilizing a parallel prefix two-operand adder like Han-Carlson (HCA) can minimize the critical path delay but incurs additional hardware complexity. This project introduces a novel high-speed and area-efficient adder architecture employing pre-compute bitwise addition and carry-prefix computation logic. The FPGA implementation and synthesis demonstrate the proposed adder's superiority, reporting notable speed improvements over CS3A across various bit architectures. Additionally, the new adder exhibits reduced area, lower power consumption, and smaller delay compared to the HC3A adder, achieving superior ADP and PDP metrics in three-operand addition techniques.
HIGHLY EFFICIENT MULTIPLIER DESIGN WITH ADJUSTABLE TRUNCATION FOR POWER SAVING
In various applications, multipliers play a crucial role, demanding substantial power consumption due to frequent multiplication operations. To address this, this Project propose an adjustable approximate multiplier that dynamically truncates partial products to balance accuracy, energy efficiency, and performance. Our approach includes a high-accuracy 4-2 compressor and a flexible error compensation circuit, allowing users to tailor accuracy and power consumption based on specific requirements at runtime. Experimental results showcase a notable reduction in delay and in average power consumption compared to the conventional Wallace tree multiplier.
LOW ERROR EFFICIENT APPROXIMATE ADDER FOR FPGA
In this project we propose a methodology for designing low error efficient approximate adders for FPGA’s. The proposed methodology utilizes FPGA resources efficiently to reduce the error of approximate adders. The proposed approach yields two distinct FPGA approximate adders, the low error and area efficient adder (LEAD) and the Area and Power efficient Adder (APEX). Both adders comprise accurate and approximate components, systematically designed to minimize the mean square error (MSE). LEADX provides better quality and APEX provides low power consumption the other approximate adders. We strategically balancing error tolerance and aims to maximize performance while minimizing error and propagation delay. Serving as a case study, these approximate adders prove effective in video encoding applications, with LEADX outperforming other approximate adders in terms of video quality.