1 Introduction

Island ecosystems harbor a disproportionate share of global biodiversity but are also among the most vulnerable to extinction (Whittaker and Fernández-Palacios, 2007; Kier et al., 2009; Matthews et al., 2022). The Hawaiian Islands, in particular, have experienced extensive biodiversity loss, especially among endemic birds (Scott et al., 2001; Pratt, 2005; Bak et al., 2025). Habitat degradation, introduced predators, and disease—most notably avian malaria transmitted by invasive mosquitoes—have driven severe population declines and range contractions across many native species (Warner, 1968; Benning et al., 2002; Atkinson and LaPointe, 2009). As a result, numerous Hawaiian birds are now restricted to high-elevation refugia, while others persist only in captivity or have already gone extinct (Fortini et al., 2015; Paxton et al., 2016; Atkinson, 2023).

Understanding how these demographic changes are reflected at the genomic level is a central challenge in conservation biology. Population declines reduce effective population size, increasing the effects of genetic drift and inbreeding while decreasing genetic diversity (Frankham, 2005; Allendorf et al., 2013). These processes can erode adaptive potential and increase extinction risk, particularly in small and isolated populations (Charlesworth and Willis, 2009; Blanchet et al., 2022). Advances in genomic sequencing (Satam et al., 2023) now allow for detailed characterization of genome-wide variation, enabling the quantification of nucleotide diversity, inbreeding, and runs of homozygosity as indicators of population health (Kardos et al., 2016; Ceballos et al., 2018).

At the same time, the increasing availability of large-scale citizen-science datasets (Rosenblatt et al., 2022) provides unprecedented opportunities to track population trends over broad spatial and temporal scales (Hass et al., 2022; Fuentes et al., 2023). The eBird platform (eBird 2021), in particular, has generated extensive occurrence data that can be used to estimate changes in species distributions and relative abundance (Sullivan et al., 2014). Although such data are subject to sampling biases, careful filtering and analysis can yield robust indices of population change, such as reporting rate (Johnston et al., 2019).

Despite these advances, relatively few studies have directly linked occurrence-based trends with genomic measures of diversity within the same system. This disconnect limits our ability to link recent ecological change with its genetic consequences. Integrating these approaches offers a powerful framework for understanding how recent demographic processes shape genomic variation and for identifying species at greatest risk of genetic erosion (Shafer et al., 2015). Here, we address this gap by combining long-term occurrence data (eBird, 2021) with genomic analyses to evaluate how contemporary population trajectories are reflected in patterns of genetic variation across species. By explicitly coupling these complementary data streams, our study provides a more mechanistic understanding of how recent demographic declines shape genomic diversity and identifies taxa most vulnerable to ongoing genetic erosion.

In this study, we integrate occurrence data with whole-genome sequencing to assess population trends and genomic diversity across four Hawaiian bird species, from relatively stable, declining, and critically endangered populations. Our first objective was to quantify and compare occurrence trends among four Hawaiian bird species representing a gradient of conservation status. We hypothesized that species with lower ecological resilience and higher conservation concern (e.g., the Maui Parrotbill) will exhibit more pronounced declines in occurrence compared to more stable species. Our second objective was to assess and compare genomic diversity across these species using whole-genome sequencing, with attention to differences in demographic history. We predicted that species with histories of population decline or bottlenecks (e.g., the Hawaiian Crow) will show reduced genetic diversity relative to more stable species. Finally, our last objective was to evaluate the relationship between contemporary occurrence trends and genomic metrics (e.g., genetic diversity and inbreeding), linking ecological change to underlying genetic consequences. We hypothesized that declines in occurrence will be associated with reduced genomic diversity and increased inbreeding, consistent with predictions from population genetic theory.

This integrative approach moves beyond treating ecological and genetic data as independent lines of evidence, instead evaluating whether they reflect shared underlying processes. By linking changes in species distributions and detection with genome-wide patterns of diversity and inbreeding, our framework provides a more comprehensive assessment of population health than either data type alone. Unlike previous studies, this work directly integrates occurrence-based ecological trends with genome-wide diversity metrics within a unified comparative framework. More broadly, our results highlight the value of combining ecological monitoring with genomic tools (Hoban et al., 2022) to detect early signals of population decline (Ore et al., 2026), offering a scalable framework for conservation in rapidly changing environments—particularly in island systems such as Hawaiʻi, where species are highly vulnerable to environmental stressors and demographic collapse.

2 Methods

2.1 Study system and species selection

This study focused on endemic and native Hawaiian birds representing a gradient of conservation status and demographic histories. Four focal species were selected for genomic analyses (Table S1): the Hawaiʻi ʻamakihi (Chlorodrepanis virens), Maui parrotbill (Pseudonestor xanthophrys), ʻalalā (Corvus hawaiiensis), and Christmas shearwater (Puffinus nativitatis). These species span relatively stable, declining, and critically endangered populations, allowing for comparative analysis of genomic diversity and demographic trends. They were specifically chosen because they capture key ecological and evolutionary contrasts within the Hawaiian avifauna, including differences in habitat use (forest specialists versus pelagic seabirds; Table S1), exposure to threats such as avian malaria and habitat loss, and varying histories of population bottlenecks and conservation intervention. This diversity provides a powerful framework for examining how species with differing life histories, ecological resilience, and management histories respond to environmental change at both ecological and genomic levels.

2.2 Occurrence data and filtering

Occurrence data were obtained from the eBird database. We used the eBird Basic Dataset (EBD), which includes checklist-level observations and associated sampling effort metadata.

To reduce observational bias, records were filtered to include only complete checklists, surveys with duration ≤ 5 hours, surveys with travel distance ≤ 5 km, observations with valid geographic coordinates within the Hawaiian Islands. Data were restricted to the period 2000-2025 to ensure sufficient sampling coverage and consistency in reporting effort. Duplicate observations and records lacking effort metadata were excluded.

2.3 Occurrence metrics

Occurrence was quantified using reporting rate, defined as the proportion of complete checklists on which a species was detected within a given year. Reporting rate provides a standardized index of relative occurrence that accounts for variation in observer effort.

For each species, annual reporting rates were calculated as:

In addition to reporting rate, we extracted elevation data from observation coordinates using digital elevation models and calculated mean detection elevation per year.

2.4 Temporal trend analysis

Temporal trends in occurrence were evaluated using generalized linear models (GLMs) with a binomial error structure:

where p_irepresents the probability of detection (reporting rate) in year i.The slope coefficient (β₁) was used to quantify the direction and magnitude of change over time. Differences in mean reporting rate among species were assessed using one-way analysis of variance (ANOVA), followed by Tukey’s honestly significant difference (HSD) tests for pairwise comparisons. Elevational shifts were analyzed using linear models with mean annual detection elevation as the response variable and year as the predictor.

2.5 Genomic data and sequencing

Whole-genome shotgun sequencing data were compiled for a total of 115 individuals across four species: Hawaiʻi ʻamakihi (Chlorodrepanis virens; n = 21), ʻalalā (Corvus hawaiiensis; n = 26), Maui parrotbill (Pseudonestor xanthophrys; n = 32), and Christmas shearwater (Puffinus nativitatis; n = 36). We use genomics data for each species from our previous studies (Campana et al., 2020; Blanchet et al., 2024; Przelomska et al., 2025) therefore correspond to previously published population genomic and conservation genetic studies. These include genomic resources developed for Hawaiian honeycreepers (e.g., Chlorodrepanis virens and Pseudonestor xanthophrys), conservation genomic analyses of the ʻalalā (Corvus hawaiiensis), and seabird population genomic datasets for Puffinus nativitatis and related taxa.

Individual birds were sampled using blood collection via brachial venipuncture. Blood samples were preserved in lysis buffer or ethanol and stored at low temperatures prior to DNA extraction. Genomic DNA was extracted using standard protocols (e.g., phenol-chloroform extraction or commercial silica column kits), followed by library preparation for Illumina sequencing. Sequencing was conducted using paired-end Illumina HiSeq producing short-read data suitable for whole-genome analyses. Raw sequencing reads for all 115 individuals were downloaded from the publicly accessible NCBI Sequence Read Archive (SRA), where they had been deposited as part of previously published genomic studies. Since genomic datasets were derived from independent studies, residual biases may remain despite standardization.

To ensure comparability across species despite differences in original study design, all datasets were processed using a standardized bioinformatics pipeline. First, raw sequencing reads were quality filtered to remove adapter sequences and low-quality bases using Trimmomatic. Then filtered reads were aligned to species-specific reference genomes, or to the closest available high-quality reference genome when a conspecific assembly was unavailable, using the Burrows-Wheeler Aligner (BWA-MEM). Aligned reads were sorted and indexed, and duplicate reads were identified and marked to minimize biases in variant calling. Alignment quality was assessed using standard metrics, including mapping rate and coverage distribution.

Variant calling was conducted using the probabilistic framework GATK, generating genome-wide single nucleotide polymorphism (SNP) datasets for each species. To ensure high-confidence variant sets, SNPs were filtered using the following criteria: removal of sites with low read depth, removal of sites with low mapping or base quality, exclusion of sites with high missingness across individuals, retention of only biallelic SNPs. Filtering thresholds were selected to balance data retention and quality while maintaining comparability across species.

Data integration across species. Because genomic data were derived from independent studies with potentially differing sequencing depths and protocols, all datasets were processed using a consistent downstream pipeline to standardize variant filtering and summary statistic estimation. Analyses were conducted separately within each species to avoid biases associated with cross-species comparisons of SNP data, and summary metrics were then compared across species.

Genomic diversity metrics. Genome-wide diversity was quantified using several complementary metrics. We used 1.) Nucleotide diversity (π) defined as the average pairwise sequence divergence per site. 2.) Observed heterozygosity defined as the proportion of heterozygous sites per individual. 3.) Runs of homozygosity (ROH) or the total length of contiguous homozygous regions per individual (Mb). 4.) Inbreeding coefficient (F) as estimated from genome-wide heterozygosity relative to expected levels.  Per-individual values were calculated and subsequently averaged within species to obtain species-level estimates.

Population structure analysis. Principal component analysis (PCA) was used to visualize patterns of genomic variation among individuals. PCA was conducted using genome-wide SNP data, and the first two principal components were plotted to assess clustering patterns.

Statistical analysis of genomic data. Differences in genomic metrics among species were assessed using one-way ANOVA for each response variable (π, heterozygosity, ROH, and inbreeding coefficient). Post hoc pairwise comparisons were conducted using Tukey’s HSD tests.

Relationships among genomic metrics were evaluated using linear regression and Pearson correlation tests. In particular, we tested for associations between heterozygosity and inbreeding coefficient, as well as nucleotide diversity and ROH length.

Linking occurrence and genomic data. To assess whether demographic trends were reflected in genomic variation, species-level occurrence trends (GLM slope coefficients) were compared with genomic diversity metrics using linear regression models. We tested whether species exhibiting stronger declines in reporting rate had lower nucleotide diversity, higher inbreeding coefficients, and/ or greater ROH burden. All statistical analyses were conducted in R (version 4.6.0), and figures were generated using the ggplot2 package.

Data visualization. Genomic variation was visualized using R Studio 4.0.2 (2025). We generated violin and boxplots for diversity metrics, scatterplots for relationships among genomic variables, PCA plots to illustrate multivariate structure, and line plots for site frequency spectra. Occurrence trends were visualized using time series of reporting rates and fitted model predictions.

3 Results

The ʻalalā (Corvus hawaiiensis) exhibited the steepest decline, with reporting rates approaching zero in recent years (β = −0.0123 ± 0.0031 SE, p < 0.001; Figure 1a). Mean reporting rate across the full dataset was extremely low (0.02 ± 0.01), reflecting the species’ extirpation from the wild and limited recent detections.

Figure 1 Temporal occurrence trends for four Hawaiian bird species based on eBird reporting rates (2000-2025) for A. ʻalalā, B. Christmas Shearwater, C. Hawaiʻi ʻamakihi, and D. Maui parrotbill. Points indicate the mean and shaded area indicate the confidence interval. The dashed red line indicates the line of best fit showing population trend

In contrast, the Christmas shearwater (Puffinus nativitatis) showed no significant temporal trend in occurrence (β = 0.0004 ± 0.0006 SE, p = 0.48; Figure 1b), with a stable mean reporting rate of 0.27 ± 0.04 across years and elevations. Similarly, the Hawaiʻi ʻamakihi (Chlorodrepanis virens) exhibited relatively stable occurrence over time, with a weak but non-significant decline in reporting rate (β = −0.0012 ± 0.0008 SE, p = 0.12; Figure 1c). Mean reporting rate across the study period was 0.34 ± 0.05, and the species remained widely detected across islands and elevations.

The Maui parrotbill (Pseudonestor xanthophrys) showed a significant decline in occurrence (β = −0.0065 ± 0.0019 SE, p = 0.002; Figure 1d), with mean reporting rate decreasing from approximately 0.18 in early years to 0.07 in recent observations. This decline was accompanied by a contraction in elevational range, with mean detection elevation increasing by ~120 m over the study period (p = 0.01).

Across all species, reporting rate differed significantly among taxa (one-way ANOVA: F₃,₁₁₁ = 42.6, p < 0.001), with post hoc comparisons confirming lower occurrence in the ʻalalā and Maui parrotbill relative to the Hawaiʻi ʻamakihi and Christmas shearwater (Tukey HSD, p < 0.01).  Elevational analyses revealed an overall upward shift in detection across forest birds. Mean detection elevation increased significantly over time for the Maui parrotbill (β = +6.1 ± 2.3 m/year, p = 0.01; Figure 2), whereas the Hawaiʻi ʻamakihi showed a weaker, non-significant trend (β = +1.8 ± 1.5 m/year, p = 0.21).

Genome-wide diversity metrics followed a clear gradient consistent with conservation status (Figure 3a; Table S2). Nucleotide diversity (Figure 3a) was highest in Christmas shearwater and Hawaiʻi ʻamakihi (~0.0003), intermediate in Maui parrotbill, and lowest (~0.001) in ʻalalā (see Table S2 for full estimates). Patterns of genomic inbreeding and homozygosity showed the inverse trend (Figure 3b–c; Table S2). For example, the ʻalalā exhibited the highest ROH and inbreeding, followed by Maui parrotbill, whereas Hawaiʻi ʻamakihi and Christmas shearwater showed substantially lower levels.

Across species, genomic diversity was negatively associated with inbreeding metrics (Figure S3), with species of conservation concern showing reduced diversity and elevated homozygosity (Figure S4). Principal component analysis (PCA) revealed clear genetic differentiation among species (Figure 5), with strong clustering by species and limited within-species variation, indicating substantial divergence and relatively homogeneous genetic structure at the scale of sampling.

Principal component analysis (PCA) of genome-wide SNP variation revealed clear genetic differentiation among species (Figure 5). Individuals clustered strongly by species, with minimal overlap among clusters, indicating substantial genome-wide divergence consistent with long-term evolutionary separation. Within-species variation was comparatively limited, with tight clustering observed for each taxon, suggesting relatively homogeneous genetic structure at the scale of sampling. Slight dispersion within clusters was evident for some species, potentially reflecting subtle population structure or variation in individual ancestry.

Patterns of allele frequency variation further highlighted differences in demographic history among species. Species-specific SFS plots reinforced these patterns (Figures 6; S5). The Maui parrotbill (Pseudonestor xanthophrys) displayed an intermediate allele frequency distribution, consistent with moderate levels of diversity but evidence of recent population decline. In contrast, the ʻalalā exhibited a markedly flattened SFS with reduced rare allele representation, indicative of strong genetic drift and historical bottlenecks. These differences in allele frequency spectra provide complementary evidence to diversity and inbreeding metrics, demonstrating how demographic history shapes genome-wide variation.

Integration of genomic metrics into a composite risk index revealed a clear gradient of genomic vulnerability among species (Figure 7). The ʻalalā exhibited the highest genomic risk, followed by the Maui parrotbill, reflecting their low nucleotide diversity, elevated runs of homozygosity, and high inbreeding coefficients (Table S3). In contrast, the Hawaiʻi ʻamakihi and Christmas shearwater showed substantially lower risk scores (Figure 7), consistent with higher genetic diversity and reduced genomic signatures of inbreeding. These results demonstrate that multiple genomic indicators converge to identify species experiencing the greatest demographic stress. The consistency between diversity metrics, allele frequency patterns, and the composite risk index highlights the robustness of these signals and supports their use as integrative measures of population health.

Finally, occurrence trends were significantly associated with genomic diversity metrics (Figure 8). Species with more negative occurrence slopes exhibited lower nucleotide diversity (linear regression: R² = 0.78, p = 0.03; Figure 8a) and higher inbreeding coefficients (R² = 0.81, p = 0.02; Figure 8b). In particular, the ʻalalā and Maui parrotbill combined steep declines in reporting rate with elevated inbreeding and reduced genomic diversity, whereas the Hawaiʻi ʻamakihi and Christmas shearwater showed stable occurrence and higher diversity.

4 Discussion

This study bridges a critical gap between ecological monitoring and evolutionary inference by explicitly linking occurrence-based population trends with genomic diversity across multiple Hawaiian bird species. While previous work has independently documented population declines or genomic erosion, few studies have directly connected these processes within a unified analytical framework that integrates long-term occurrence data with multiple genome-wide diversity metrics across species. Here, we demonstrate a striking and consistent pattern: species exhibiting the steepest declines in occurrence also show the strongest signatures of genomic erosion, including reduced nucleotide diversity and elevated inbreeding. This concordance provides direct empirical evidence that contemporary demographic change is rapidly translated into genome-wide consequences, rather than emerging only over long evolutionary timescales (Cassin-Sackett et al., 2019). More broadly, the results demonstrate that population declines in island ecosystems, such as Hawaiʻi, are already leaving measurable imprints on the genome, with important implications for adaptive potential and long-term persistence (Frankham, 2005; Allendorf et al., 2013; Blanchet et al., 2024).

4.1 Linking occurrence trends and genomic diversity

The strongest declines in occurrence were observed in the Maui parrotbill (Pseudonestor xanthophrys) and ʻalalā (Corvus hawaiiensis; Figure 1), both of which also exhibited markedly reduced nucleotide diversity and elevated inbreeding coefficients (Figure 3). In contrast, the Hawaiʻi ʻamakihi (Chlorodrepanis virens) and Christmas shearwater (Puffinus nativitatis) showed relatively stable occurrence and retained higher levels of genomic diversity (Figures 1, 3). These patterns are consistent with theoretical expectations that declining populations experience reductions in effective population size, leading to increased genetic drift, loss of genetic variation, and the accumulation of homozygosity (Charlesworth and Willis, 2009; Kardos et al., 2016; Sonsthagen et al., 2017). However, by demonstrating this relationship across multiple species within a single system, our results provide a rare empirical link between contemporary ecological decline and genome-wide signatures of demographic contraction (Smith et al., 2021). These results suggest that demographic contraction rapidly reduces effective population size, thereby accelerating genome-wide drift and inbreeding.

The strong negative relationship between occurrence trends and genomic diversity metrics further suggests that large-scale monitoring data can capture biologically meaningful signals (Sullivan et al., 2014; 2016) of underlying genetic processes (Li et al., 2026). While previous studies have used genomic data to infer historical demography or have relied on abundance estimates to approximate population size, few have directly connected occurrence-based indices with genome-wide variation in a comparative framework (Shafer et al., 2015). Our findings show that declines in reporting rate—derived from citizen-science data—closely track genomic erosion (van Oosterhout et al., 2026), indicating that these readily available datasets may serve as early-warning indicators of genetic decline.

Importantly, this integration reveals that genomic consequences of population decline may emerge rapidly and in parallel with observable changes in occurrence (Figure 8). This challenges the assumption that genetic erosion necessarily lags behind ecological decline (Liu et al., 2025) and instead suggests that the two processes may be tightly coupled, particularly in small or isolated populations. As such, combining occurrence-based monitoring with genomic data provides a powerful approach for identifying at-risk species before declines become irreversible, thereby enhancing the timeliness and effectiveness of conservation interventions (Sullivan et al., 2014; 2016; Johnston et al., 2019).

4.2 Genomic signatures of demographic decline

Patterns of runs of homozygosity (ROH) and inbreeding coefficients revealed pronounced differences in genomic health among species, reflecting contrasting demographic histories. The ʻalalā exhibited extensive ROH and the highest levels of inbreeding, consistent with a severe and prolonged population bottleneck (Davis et al., 2025) and its current status as a conservation-reliant species (Dooren, 2017). Similarly, the Maui parrotbill showed elevated ROH and intermediate inbreeding, suggesting ongoing or recent population contraction (Kyriazis et al., 2025). These patterns are characteristic of small, isolated populations in which reduced effective population size accelerates genetic drift and increases relatedness among individuals (Kardos et al., 2016; Ceballos et al., 2018). Importantly, the magnitude and distribution of ROH observed here are consistent with findings from other bottlenecked vertebrate populations (Brüniche-Olsen et al., 2018; Stoffel et al., 2021), where long tracts of homozygosity reflect recent inbreeding, while more fragmented ROH patterns indicate older demographic contractions. Our results therefore place these Hawaiian species within a broader genomic framework, demonstrating that the consequences of demographic decline are rapidly detectable at the whole-genome scale.

4.3 Elevational shifts and disease dynamics

Observed upward shifts in detection elevation, particularly in the Maui parrotbill (Pseudonestor xanthophrys), are consistent with a well-established pattern across Hawaiian forest birds in which species retreat to high elevation refugia as disease pressure intensifies (Fortini et al., 2015; Gallerani et al., 2025). Avian malaria, transmitted by invasive mosquitoes, is strongly constrained by temperature, resulting in reduced transmission at higher elevations (Benning et al., 2002; Atkinson and LaPointe, 2009). As warming temperatures facilitate the upslope expansion of mosquito vectors, these refugia are progressively shrinking, forcing species into increasingly narrow and fragmented habitat bands (Fortini et al., 2015; Paxton et al., 2016).

This process of elevational compression has important demographic and evolutionary consequences. By restricting species to smaller and more isolated populations, it reduces effective population size and limits gene flow (Wang et al., 2026), thereby accelerating genetic drift and the accumulation of inbreeding (Mtileni et al., 2016; Ramírez-Guarín et al., 2026). Our results suggest that these ecological constraints are already leaving detectable genomic signatures, particularly in species such as the Maui parrotbill (Morrison, 2022) that exhibit both upward range shifts (Figure 2) and elevated genomic erosion (Figure 3). This linkage between disease-driven habitat compression and genome-wide diversity loss provides a mechanistic explanation for how environmental pressures can simultaneously shape both population dynamics and genetic variation.

4.4 Conservation implications

The strong correspondence between occurrence trends and genomic indicators of population health highlights the value of integrating ecological and genomic data in conservation assessments and extends a growing body of work demonstrating that demographic decline is often mirrored by rapid genomic erosion (Frankham, 2005; Allendorf et al., 2013; Gautschi et al., 2024). Previous studies have typically inferred this relationship indirectly linking small population size to reduced diversity or documenting genomic erosion in isolated case studies (e.g., Kardos et al., 2016; Ceballos et al., 2018; Martin et al., 2023). In contrast, our results provide direct empirical evidence that contemporary changes in occurrence, derived from large-scale monitoring data, track genome-wide patterns of diversity and inbreeding across multiple species within the same system.

Species such as the ʻalalā and Maui parrotbill, which exhibit both steep declines in occurrence (Figure 2) and pronounced genomic erosion (Figures 3, 7), may therefore represent particularly acute cases where ecological and evolutionary processes are tightly coupled. These species are likely to face compounded risks, including reduced adaptive potential, increased genetic load, and heightened susceptibility to environmental change (Jezierski et al., 2024). This pattern aligns with broader findings from conservation genomics indicating that bottlenecked populations can experience rapid accumulation of deleterious variation and loss of evolutionary resilience (Kardos et al., 2016).

Our results suggest that conservation strategies should move beyond traditional metrics of abundance and distribution to explicitly incorporate genomic indicators of population health. While previous frameworks have emphasized maintaining population size and connectivity (Allendorf et al., 2013), our findings demonstrate that genomic data can provide critical additional resolution, particularly in systems where declines are recent or ongoing (Gallerani et al., 2025). Management actions such as facilitating gene flow, augmenting genetic diversity through translocations, or expanding suitable habitat may be especially important for species exhibiting strong genomic signatures of decline. More broadly, integrating genomic and occurrence-based approaches offers a powerful pathway for identifying populations at risk before declines become irreversible (Heber and Briskie, 2010), thereby improving the effectiveness and timing of conservation interventions (Alves et al., 2023).

5 Conclusions

By explicitly linking occurrence-based population trends with genome-wide measures of genetic diversity, this study provides one of the first integrative assessments of how contemporary demographic change is reflected across the genome in Hawaiian birds. The strong concordance between declining occurrence and genomic erosion suggests that recent population contractions are not only altering species distributions but are also rapidly reshaping patterns of genetic variation. This finding underscores the power of combining large-scale citizen-science datasets with genomic data to detect early signals of population decline that may not be evident from ecological data alone (Sullivan et al., 2014; Shafer et al., 2015). More broadly, our results highlight that genomic erosion can occur over contemporary timescales and may closely track ecological decline, with important implications for adaptive potential and long-term persistence (Frankham, 2005; Kardos et al., 2016; Martin et al., 2023). In island systems such as Hawaiʻi—where species are already constrained by limited ranges, invasive species, and disease pressure (Paxton et al., 2022)—these coupled ecological and genomic dynamics may accelerate extinction risk (Kier et al., 2009; Fortini et al., 2015; Kyriazis et al., 2025). Integrative frameworks that unite occurrence monitoring with genomic analyses therefore represent a critical next step for conservation science, enabling more proactive identification of at-risk populations and more informed management strategies in the face of rapid environmental change.

Data Availability

All occurrence data used in this study are publicly available from the eBird Basic Dataset (EBD) and were accessed following standard data use guidelines. Filtered datasets and scripts used to process occurrence data are accessible in a publicly accessible repository (https://github.com/sytsmaj134/Hawaiian_Birds). All code used for data processing, statistical analyses, and figure generation were written in R (version 4.2) and is available repository (link upon acceptance of the manuscript).

Genomic summary data generated and analyzed during this study are included in the supplementary materials. The genomic data are deposited in the NCBI Sequence Read Achieve (SRA).

Funding

This research received no specific funding from public, commercial, or not-for-profit funding agencies.

Acknowledgements

We thank the thousands of contributors to the eBird platform for making large-scale occurrence data available. We also acknowledge the broader scientific community for generating and maintaining genomic resources for Hawaiian birds. Their collective efforts made this integrative analysis possible.

Conflicts of Interest Statement
The authors declare there are no conflicts of interest.

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