Local extinction as information: toward dynamic, reality-aware biodiversity mapping

Local extinction as information: toward dynamic, reality-aware biodiversity mapping | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 9 February 2026 V1 Latest version Share on Local extinction as information: toward dynamic, reality-aware biodiversity mapping Authors : David Livadariu 0009-0007-7747-9178 , Victor Bâcu , and Lucian Parvulescu 0000-0002-1528-1429 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177066202.20757674/v1 169 views 86 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Biodiversity databases increasingly function as the default substrate for species distribution mapping. Many processes can decouple archived occurrences from field reality, but one of the most consequential yet structurally omitted is documented local extinction—evidence that a population of a particular taxon previously recorded at a locality has since disappeared. We argue that local extinction is not simply absence but a directional, high-information transition (presence → loss) that, in database terms, requires data extirpation—something most biodiversity infrastructures cannot represent natively. The result is systematic temporal inflation: contemporary range products retain legacy presences, obscuring real-time decline and propagating error into conservation assessment, species distribution modelling, and invasion inference. To make biodiversity mapping time-aware without replacing occurrence backbones, we propose treating documented loss as a first-class record via a minimal, standardisable loss_event linked to historically occupied localities, with traceable provenance and (where available) a lightweight cause field. We anchor the proposal in an operational implementation using European crayfish extinctions, demonstrating that even fragmentary loss documentation produces measurable inflation in IUCN range metrics and revealing practical design constraints for spatial matching rules. This structural change—small in implementation but fundamental in effect—becomes essential as biodiversity informatics scales toward automated synthesis and AI-driven decision pipelines. Introduction As biodiversity loss accelerates, intensifying concerns across conservation science (Pereira et al. 2024, Sayer et al. 2025, Gonçalves-Souza et al. 2025), biodiversity databases have transformed our ability to track species distributions by aggregating millions of occurrence records into digital infrastructure (Heberling et al. 2021). This growth has been overwhelmingly enabling, expanding access, improving synthesis, and making distributional evidence far more reusable across research and decision contexts (Poisot et al. 2019). At the same time, much of the underlying infrastructure remains effectively time-blind in one specific and consequential way. Most occurrence-driven systems inherit a tacit persistence assumption: once documented, populations are treated as indefinitely present in queries and derived distribution products. Yet aggregated occurrences are routinely treated as a proxy for present-day distribution, whilst most database architectures provide no native mechanism to distinguish populations that persist from those documented as lost. This persistence bias has practical consequences because occurrence data now feed directly into widely used analytical and decision pipelines. Conservation assessments increasingly depend on digital occurrences to estimate distributions and derive range metrics (Rodrigues et al. 2006, Cardoso et al. 2011, Brooks et al. 2019). Species distribution modelling trains on pooled occurrence datasets in which historical and contemporary records become indistinguishable (Lobo et al. 2010). In invasion ecology, native population disappearances remain invisible and can be mistaken for incomplete sampling (Boakes et al. 2010). These are not generic “absence data” of uncertain meaning; they are directional, high-information events often accompanied by evidence and plausible drivers such as habitat destruction (Stuart et al. 2004), invasive displacement (Oficialdegui et al. 2024), emerging disease (Scheele et al. 2019), or climate-related loss (Wiens 2016). Yet biodiversity informatics platforms rarely preserve these transitions as structured, queryable records, relegating loss evidence to narrative scattered across publications rather than integrating it into the data substrate that increasingly shapes ecological inference. Here we argue that documented local extinction should be treated as computable information—encoded as a minimal, standardisable record type, stored as a first-class record linked to previously occupied localities, with temporal, evidence classification, and provenance metadata. Coupled with version-aware storage, this enables reproducible time-sliced products: distribution state(t) snapshots and explicit change tracking between time points. The goal is to reduce temporal inflation whilst making range dynamics transparent and quarriable. The consequences The systematic omission of documented local extinction from biodiversity databases creates a cascading bias across applications that increasingly depend on digital occurrence data. When historical presence is operationally treated as a proxy for current status, distribution products become temporally inflated: legacy localities remain “present” in outputs even where populations have been documented as lost. This matters because many downstream decisions implicitly assume that aggregated occurrences approximate contemporary distributions, while most infrastructures still lack a native, standardisable way to encode the directional transition from presence to loss. This incompleteness propagates directly into conservation assessment, where digital occurrences are routinely used to approximate contemporary distributions and to derive range metrics that influence threat categorisation and policy attention despite recognised data quality constraints (Rodrigues et al. 2006, Cardoso et al. 2011, Brooks et al. 2019, Pârvulescu 2025). If archival presences are carried forward as if they were current state, various conservation metrics can be overestimated, particularly where local disappearances are widespread. A taxon historically recorded across many sites yet locally extinct at a substantial subset can appear indistinguishable—within occurrence-only products—from one that remains present throughout its historical range. The issue is not that occurrences are “wrong”; it is that, as a data structure, they are incomplete representations of state. Where conservation metrics thresholds drive categorisation, that incompleteness can delay recognition of decline until it is severe (Ficetola et al. 2014). The same persistence bias then spills into species distribution modelling, which often draws on pooled occurrence datasets in which historical and contemporary records become indistinguishable at the point of use (Bracken et al. 2022). Absence is already epistemologically and practically challenging in SDMs (Lobo et al. 2010); the result is ’ghost presences’ persisting in training data long after populations have disappeared. Models may therefore project suitability onto landscapes where populations are known to have been lost, distorting evaluation and inference—especially when suitability is interpreted as evidence about persistence mechanisms or refugia (Araújo and Peterson 2012, Guillera-Arroita et al. 2015). Temporal filtering can mitigate this to a limited extent, but without a computable representation of documented loss it remains ad hoc and analyst-dependent, rather than a transparent property of the underlying data product. In invasion ecology the consequences are particularly acute. Reconstructing invasion trajectories requires distinguishing absence due to incomplete sampling from documented native loss following invasion (Carvajal-Endara et al. 2017), yet occurrence-only architectures cannot encode that distinction. When an invasive taxon expands and a native taxon disappears from historically occupied sites, both processes may be visible as separate presence patterns, but the transition itself—native persistence to local loss—remains unrepresented. This constrains auditable quantification of invasion impacts and can bias syntheses relying on occurrence-only products (Blackburn et al. 2019). Conversely, where natives persist despite invasion pressure (Leger and Espeland 2010), the inability to encode losses systematically hinders discovery of potential resistance mechanisms or refugia—precisely the signals that would be most actionable for conservation. These limitations also complicate attribution of range contractions under climate and other drivers. Temporal analyses require separating genuine contraction from sampling artefacts and alternative causes (Lenoir and Svenning 2015). Without structured loss records, contraction is commonly inferred indirectly (e.g., historical versus recent occurrence comparisons), but such inference is highly sensitive to uneven sampling effort and reporting bias. Documented local extinction provides a qualitatively different kind of information: a directional transition anchored to historically occupied localities, with temporal context and often supporting evidence or plausible drivers. Integrating this as structured data would not resolve attribution on its own, but it would materially strengthen the evidential basis for distinguishing “loss” from “sparsity”. Finally, the omission of local extinction as structured information reflects a broader mismatch with ecological reality. Many syntheses focus on defaunation and the erosion of ecological interactions through time (Young et al. 2016), which ultimately depend on detecting losses—of populations, interactions, and functional roles. When databases accumulate presences indefinitely but do not preserve documented local losses, they risk underrepresenting both the magnitude and the spatial patterning of decline, particularly at local scales where ecological consequences are most immediate. More fundamentally, treating loss evidence as unstructured information perpetuates a static view of biodiversity. This conflicts with ecological understanding of landscapes shaped by continuous colonisation–extinction dynamics (Hanski 1998). Recognising data extirpation as a first-class record type—capturing documented local extinction as queryable, provenance-rich information—would align biodiversity informatics with this dynamic reality and make time-aware products a property of the infrastructure rather than an undocumented downstream choice. Toward dynamic, version-aware infrastructure Reducing temporal inflation does not require replacing occurrence repositories. It requires acknowledging a missing record type: a way to encode documented local extinction as structured information, so that distributions can be represented as states through time rather than as cumulative archives. Occurrence records capture where a taxon has been observed; what most infrastructures still cannot encode cleanly is the complementary transition—where that same taxon has been documented as lost at a historically occupied locality. The result is a structural asymmetry: presences persist by default, whilst documented losses remain largely invisible. Yet these same systems increasingly supply maps, metrics, models, and assessments for decision-making. A pragmatic repair is to represent documented local extinction as a first-class record—what we term a loss_event. We distinguish local extinction (the ecological phenomenon in the field) from data extirpation (its representation as a structured database record). A loss_event links to a historically occupied locality and, where possible, to prior occurrence identifiers. Throughout, we use local extinction for the ecological phenomenon in the field, and data extirpation for its representation as a structured database record. This is not an attempt to generalise “absence data” (which remain epistemologically and practically complex), but to preserve a high-information, directional transition (presence → loss) as computable evidence. Existing standards can express presence versus absence, e.g., Darwin Core’s occurrenceStatus vocabulary (Wieczorek et al. 2012), but that alone does not provide an auditable, event-based way to archive documented loss such that it can be traced, versioned, and propagated into time-sliced products. Crucially, the data model can remain deliberately minimal to lower adoption barriers. At its core, a loss_event needs only: (i) a loss year, (ii) a locality reference sufficient for linkage, and (iii) traceable provenance (source and curator), with an optional lightweight cause field where the evidence supports it. This minimalism lowers adoption barriers: richer attribution can be layered later without changing the underlying logic. Once loss_events exist as structured records, two outputs become routine and reproducible rather than ad hoc: state(t) snapshots that distinguish extant localities from those with documented local extinction, and delta(t1,t2) ledgers that make gains and losses explicit across time windows. The key contribution is not new terminology but a structural shift: relocating temporal interpretation from undocumented analyst filtering into transparent, auditable database outputs. This matters especially as occurrence data increasingly feed automated workflows and large-scale synthesis. Once loss infrastructure is in place, version-aware outputs support two complementary modes. The default narrative view presents the most recent state: current maps, metrics, and summaries reflecting the latest ledger version, with documented losses applied as overlays. For change detection, a separate comparison mode enables temporal slicing: users select historical snapshots (e.g., version) to generate automated delta reports—gains, losses, and net change across taxa and reporting units—without regenerating spatial products, as versioned maps remain directly accessible. This dual structure makes contemporary state interpretation straightforward for most users, while supporting reproducible change analysis as an infrastructure capability rather than analyst-dependent workflow. Technical challenges and an operational anchor For documented local extinction to become usable infrastructure, it has to be made technically legible as data. The core difficulty is not detecting loss in the field, but representing it in a way that can propagate consistently into the downstream products that biodiversity databases are now expected to serve. In occurrence-driven systems, the default logic is persistence: presences accumulate, and the database has no native way to encode the directional transition that matters here (presence → loss). Treating documented local extinction as computable information requires deliberate technical conceptualisation. The ecological phenomenon remains local extinction; its database representation becomes data extirpation—a structured claim that affects interpretation without rewriting archival records. The practical challenges cluster around three issues that determine whether the approach works at scale. First, representation: the database needs a stable way to store a loss claim as a queryable, citable, auditable record type. That record must minimally state what taxon is affected, where (a historically occupied locality), when (loss year), and why we believe it (traceable provenance), with an optional lightweight cause field when evidence supports it. Second, linkage: loss is usually documented at locality scale, whilst occurrence archives contain multiple records per locality with mixed coordinate quality. If linkage rules remain implicit or ad hoc , the system simply relocates temporal interpretation from the database into analyst choices. We treat linkage as a tunable design space rather than settled standard—the ’right’ matching rule depends on system and data quality. What matters is that the rule is declared and its consequences reproducible. Third, propagation: once loss is represented, workflows must ensure that data extirpation consequences appear consistently in outputs—maps, statistics, range metrics—so users need not apply bespoke filters manually. These technical capabilities enable version-aware outputs where temporal state can be declared explicitly. Summary metrics become queryable with ledger semantics, with deltas propagating consistently across reporting units used in conservation dashboards—countries, drainage basins, biogeographic regions, freshwater ecoregions, and protected areas. The asymmetry in metric sensitivity is structurally predictable and diagnostic rather than problematic. These results are intentionally presented as an operational anchor rather than as an ecological analysis of the taxa involved. We deliberately present these as an operational demonstration rather than ecological analysis. A full interpretation would require broader, more homogeneous loss documentation across systems and time. BOX 1: Extinction encoding impact: a proof-of-concept using European crayfish. World of Crayfish ® documents 140 local extinctions (1971–2024) affecting 10 crayfish taxa across 11 countries: Astacus astacus (n=106), Austropotamobius torrentium (n=8), A. bihariensis (n=5), Pontastacus leptodactylus (n=5), A. pallipes (n=3), and five additional species (n=1-2 each). Geographically, losses concentrate in Latvia (n=66), Norway (n=24), and Belarus (n=21), with scattered records across Czechia, Romania, Italy, UK, Ukraine, France, Sweden, and Israel. Approximately 50% link to peer-reviewed sources via DOI, the remainder derive from expert monitoring reports and historical questionnaire data. Where causal attribution is possible (35% of records), documented drivers include molecularly confirmed crayfish plague ( Aphanomyces astaci , n=45), displacement by invasive species (n=2), and habitat degradation (n=1). The dominance of pathogen-driven losses reflects both the catastrophic impact of crayfish plague in northern Europe and the availability of molecular diagnostic protocols enabling confident attribution (Vrålstad et al. 2014, Svoboda et al. 2017, Ungureanu et al. 2020). We focus on A. astacus (noble crayfish), which comprises 106 documented extinctions—the largest and most geographically coherent dataset available. To quantify temporal inflation, we applied spatial buffers (0 m, 250 m, 500 m, 1000 m) around documented loss localities, removed presences within each threshold, and recalculated Area of Occupancy (AOO, using standard 2×2 km IUCN grid cells) and Extent of Occurrence (EOO, using convex hull). Baseline: che C k OVER v1.0 output (5,405 localities, no extinction encoding). Local extinction encoding removed occurrences consistently across buffer scenarios (Table 1). AOO responded proportionally; EOO remained structurally insensitive (convex hull stable when interior points suppressed). The tight clustering of ΔAOO (−4.2% to −4.4%) indicates buffer choice exerts modest influence on aggregate metrics within this radius range. Table 1. Impact comparison on range metrics for Astacus astacus using different buffer thresholds for extinction encoding. None 0 14,248 — — 4.43 — — 5,405 0 101 13,652 −596 −4.2 4.45 +0.022 +0.5 5,198 250 106 13,636 −612 −4.3 4.45 +0.022 +0.5 5,193 500 107 13,632 −616 −4.3 4.45 +0.023 +0.5 5,192 1000 109 13,624 −624 −4.4 4.45 +0.023 +0.5 5,190 Even with fragmentary local extinction documentation (106 losses among 5,405 localities), measurable inflation was detected (4.3% AOO), demonstrating that legacy presences bias IUCN metrics. Operationally, exact-match removal (0 m) risks missing extirpations under coordinate uncertainty; overly generous buffers overcorrect. Intermediate thresholds (250–500 m) provide reasonable stability, though user-defined buffers calibrated to dataset-specific coordinate quality may prove optimal. This massive under-representation likely reflects that most researchers remain unaware of the need to structure such claims. Structured extinction encoding is therefore essential infrastructure for temporal fidelity in biodiversity databases and must be thoroughly addressed methodologically and practically. ….…………………………………. end of BOX 1 …………………………………………. Conclusion and call to action Biodiversity informatics cannot become time-aware through occurrence accumulation alone. The modern biodiversity data stack is increasingly treated as a substrate for synthesis—feeding assessment pipelines, forecasting models, and automated dashboards—yet much of it still operates as if recorded presences persist by default. As environmental change accelerates, this tacit persistence rule becomes increasingly costly. It inflates contemporary range products, hides real-time decline behind archival accumulation, and makes ”temporal interpretation” an undocumented analyst decision rather than an explicit property of the infrastructure. Our proof-of-concept demonstrates this is not merely conceptual: even fragmentary extinction documentation (106 losses among 5,405 localities) produces measurable inflation (4.3% AOO) in IUCN range metrics, revealing how legacy presences bias conservation assessments when loss remains unstructured. Documented local extinction already exists as evidence in the literature and monitoring practice; what is missing is a routine way to make that evidence computable. The remedy is straightforward. Databases and standards communities do not need a comprehensive ontology of extinction causation to begin. They need a minimal, auditable representation of documented loss as a first-class record type—data extirpation as the database expression of local extinction—linked to historically occupied localities, carrying a loss year and traceable provenance, and optionally a lightweight cause field when the evidence supports it. Crucially, this record type must function as an overlay rather than a rewrite of history: archival occurrences remain intact, while time-sliced products become reproducible. Operationally, our buffer analysis reveals practical design constraints: exact-match linkage risks missing extirpations under coordinate uncertainty, while overly generous spatial buffers overcorrect; intermediate thresholds (250–500 m) provide reasonable stability, though user-defined buffers calibrated to dataset-specific coordinate quality may ultimately prove optimal. Once minimal infrastructure is in place, workflows can serve outputs currently difficult to make routine: state(t) snapshots separating extant from documented-loss localities, and delta(t1,t2) ledgers making gains and losses explicit and auditable. This shift requires coordinated expectations across stakeholder communities. Data infrastructures and aggregators must support loss_events as exchangeable records alongside occurrences—stored with stable identifiers, provenance, and version tags—so documented loss can be discovered, queried, and reused. Assessment and monitoring communities must archive site-level losses as machine-readable ledgers with provenance, rather than leaving them as prose in PDFs that cannot propagate into synthesis. The massive under-representation of documented extinctions in current databases likely reflects that most researchers remain unaware of the need to structure such claims as computable information; raising this awareness is as critical as technical infrastructure. Journals and peer review should expect structured, citable loss records (or ledger snapshots) when local extinction is used as vidence—parallel to how occurrence datasets are increasingly required for presence-based claims. Funders and programme managers should prioritise sustained curation capacity over one-off datasets: documented loss becomes valuable infrastructure only if it remains discoverable, auditable, and versioned over time. The deeper motivation is forward-looking. Ecological decision-making is moving toward automated pipelines and AI-assisted synthesis, where models will only be as reality-aware as the data foundations they consume. Our demonstration that fragmentary documentation already produces detectable bias underscores the urgency: in automated decision pipelines, the limiting factor will not be the sophistication of downstream analytics, but whether the underlying data structures can represent change. Local extinction is the most direct change signal we already have at locality scale; data extirpation is the minimal data structure that prevents that signal from being discarded. Making documented loss computable should become routine—not as added complexity, but as structural correction. This aligns biodiversity maps with ecological reality rather than archival history. References Araújo, M. B. and Peterson, A. T. 2012. Uses and misuses of bioclimatic envelope modeling. - Ecology 93: 1527–1539.Blackburn, T. M. et al. 2019. Alien versus native species as drivers of recent extinctions. - Front. Ecol. Environ. 17: 203–207.Boakes, E. H. et al. 2010. 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Keywords biodiversity informatics conservation assessment data extirpation dynamic mapping temporal bias versioning Authors Affiliations David Livadariu 0009-0007-7747-9178 West University of Timisoara View all articles by this author Victor Bâcu Technical University of Cluj-Napoca View all articles by this author Lucian Parvulescu 0000-0002-1528-1429 [email protected] West University of Timisoara View all articles by this author Metrics & Citations Metrics Article Usage 169 views 86 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation David Livadariu, Victor Bâcu, Lucian Parvulescu. Local extinction as information: toward dynamic, reality-aware biodiversity mapping. Authorea . 09 February 2026. DOI: https://doi.org/10.22541/au.177066202.20757674/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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