Research Paper

INTRODUCTION

Anthropogenic disturbance and non-native species introductions have been implicated in the decline, extirpation, and extinction of many species throughout the world (Pyšek et al. 2017, 2020). Insular species are especially vulnerable to these threats because of small population sizes, low habitat availability, and low functional redundancy (Pimm et al. 1988, Biber 2002, Whittaker and Fernández-Palacios 2007). In Hawaiʻi, more than half the endemic avifauna have been lost, primarily because of habitat destruction, hunting, predation by introduced predators, and disease (Van Riper and Scott 2001, Paxton et al. 2018).

On Hawaiʻi Island, avifaunal communities are primarily threatened by avian pox, avian malaria, habitat loss from the fungal disease Rapid ʻŌhiʻa Death, drought, fire, and non-native ungulates (Atkinson et al. 2005, Foster et al. 2007, Pratt et al. 2009, Banko et al. 2014, Fortini et al. 2015, 2019). In response to emerging threats, various conservation efforts to monitor bird densities and trends have been implemented throughout the island (Gorresen et al. 2005, 2007, Camp et al. 2010, Paxton et al. 2013, Judge et al. 2018, Burnett et al. 2021, Kendall et al. 2023). The value of long-term monitoring to detect population trends of sensitive species has been well demonstrated (Camp et al. 2010, Banko et al. 2013).

In this study, we fill a gap in the Hawaiian forest bird dataset by reporting data collected at the US Army’s Pōhakuloa Training Area (PTA), a large region on Hawaiʻi Island where forest bird data have not yet been formally documented. Pōhakuloa Training Area harbors 20 plant species listed under the U.S. Endangered Species Act as threatened or endangered (CEMML 2019). These federally listed plant species that are primarily threatened by wildfire and browsing by two non-native ungulate species, a mouflon-domestic sheep (Ovis aries) hybrid, and feral goats (Capra hircus). In a 2003 Biological Opinion delivered by the U.S. Fish and Wildlife Service, the U.S. Army was committed to build ungulate-proof fencing around areas with federally listed threatened and endangered plant species and to eradicate ungulates inside the fence units. Prior to the fence construction, we initiated a forest bird monitoring project in three regions of the installation; two of the regions were fenced and ungulates eradicated (hereafter, N-fenced and S-fenced regions), and one region was partially fenced, but ungulates were not eradicated (hereafter, partially fenced region).

Overbrowsing of introduced ungulates negatively impacts forest bird densities on Hawaiʻi Island (Banko et al. 2013). High sheep densities have been estimated since at least 2019 in the partially fenced region (Leo 2022; Table 1). Goats are also present in the partially fenced region but at a much lower density. Negative impacts of overbrowsing in the partially fenced region are obvious, with denuded vegetation below a distinct browse line (Fig. 1). Therefore, we expected to observe bird population declines in the partially fenced region, especially for species that rely on understory vegetation as a food and cover resource.

The objective of this study was to establish a baseline dataset by estimating annual density, long-term population trends, and short-term trajectories of forest birds in the saddle region of Hawai’i. We used an 18-year (2003–2020) forest bird point count dataset to estimate forest bird density with distance sampling methods and assess long-term trend within a Bayesian framework. We also estimated short-term (2011-2020) trajectories to assess any indication of a shift in trend. Filling this data gap should be especially useful for island-wide studies such as those that focus on forest bird response to climate change, disease, and other stressors (Fortini et al. 2015, 2019, Paxton et al. 2018).

METHODS

Study area

Pōhakuloa Training Area is a 53,750 ha U.S. Army installation that occupies most of a large plain formed by the convergence of three volcanoes on Hawaiʻi Island: Mauna Kea (4205 m) lies to the northeast, Mauna Loa (4169 m) to the south, and Hualālai (2521 m) to the west (Shaw and Castillo 1997). The climate of PTA is classified as cool and tropical. Elevation ranges from 768 m to 2637 m. The 59-year average annual precipitation at Pōhakuloa Weather Station (latitude 19.7494°, longitude -155.5267°) is 354 mm. Much of the installation is situated above the thermal inversion layer and is not influenced by the trade wind-orographic rainfall regime. Most rainfall occurs during the winter months, and drought is common in the absence of winter storms.

The three study regions, N-fenced (5098 ha), S-fenced (2446 ha), and partially fenced (1554 ha) were classified into six cover types by CEMML with data collected in 2011 and 2012 using the U.S. National Vegetation Classification System to standardize vegetation classification at PTA (Cogan and Kudray 2009, Lea 2011, Block et al. 2013). The N-fenced region consists mostly of ʻōhiʻa (Metrosideros polymorpha) woodland, and the partially fenced region consists mostly of a mixed woodland of māmane (Sophora chrysophylla) and naio (Myoporum sandwicense). The S-fenced region is mostly shrubland (Fig. 2; Table 2). However, we note that shrubland species are tall in stature; most mature individuals of the two dominant plant species range from one to four meters in height (Block and Cook 2017).

The N-fenced and S-fenced regions were declared ungulate free in 2016 and 2010, respectively. In the partially fenced region, ungulates were present in similar numbers inside vs. outside the fence until 2010, when the old fence was replaced and eradication of sheep within the fence accelerated (Joe Kern and Chauncy Kalâ Asing, personal communication). In recent years, ungulate density on the outside portion of the partially fenced region has increased considerably.

Bird sampling

Forest bird point count data were collected by Colorado State University’s Center for Environmental Management of Military Lands (CEMML) and compiled in an 18-year (2003–2020) dataset. We followed point count distance sampling estimation methods that have most commonly been used to estimate forest bird population density elsewhere in Hawaiʻi (Scott et al. 1984, Camp et al. 2010, 2014, Paxton et al. 2013, Burnett et al. 2021). Preliminary analysis of the bird point count data revealed that there were sufficient data (on average ≥ 70 observations per year) to warrant analysis for two native species: ʻApapane (Himatione sanguinea) and Hawaiʻi ʻAmakihi (Chlorodrepanis virens), and four non-native species: House Finch (Haemorhous mexicanus), Eurasian Skylark (Alauda arvensis), Warbling White-eye (Zosterops japonicus), and Yellow-fronted Canary (Crithagra mozambicus). The six forest bird species considered here vary by foraging habits and preferred diet items and can be classified into three diet categories. House Finch, Eurasian Skylark, and Yellow-fronted Canary primarily feed on seeds with other plant parts, leaves, flower parts, and fruit, as well as insects (Badyaev et al. 2020, Campbell et al. 2020, Clement 2020). Hawaiʻi ʻAmakihi and Warbling White-eye feed on insects, fruit, and nectar (Lindsey 2020, Van Riper 2020). ʻApapane primarily feed on nectar of ʻōhiʻa but also consume nectar from numerous native species and insects (Fancy 2020).

Annual bird surveys were conducted in December and January 2003–2020 (CEMML 2019). Initially, these months were chosen to avoid scheduling conflicts with researchers of the critically endangered Palila (Loxioides bailleui). As a result, our surveys are not aligned with survey periods of other forest bird studies on Hawaiʻi Island. Forest bird breeding season is generally between December and May. Our surveys took place at the beginning of that period and may not be adequately timed to capture the peak activity for each species. Point transect distance sampling was used with a six-minute window, following published methodology for Hawaiian forest birds (Reynolds et al. 1980, Scott et al. 1984, Camp et al. 2010). N-fenced survey locations were arranged in two perpendicular transects consisting of 78 stations. S-fenced survey locations were arranged in seven parallel transects consisting of 126 counting stations. Partially fenced survey locations were arranged in four parallel transects consisting of 84 stations, 25 of which were located inside of the fence (Fig. 2).

We estimated densities (birds ha^-1) by fitting a detection function to distance measures, selecting among candidate detection function models using AIC methods, and performing model diagnostics of the best-fit models using the Distance package (Miller et al. 2019); all analyses in this study were completed using R Version 4.1.1 (R Core Team 2022). Bird activity can vary with time of day, and detectability can vary with observer (Johnson 2008). We recorded the start time for each six-minute point count and converted it to minutes after sunrise as a proxy for time of day. We also recorded the observer, and both variables were used as covariates in the analysis. We right truncated 10% of the distance data for each species to remove outliers and facilitate modeling (Buckland et al. 2015). Data were pooled across years, and an AIC model selection process was conducted to select a global detection function, which was used to fit three additional models: a null model and two others that included covariates (Buckland et al. 2015, Miller et al. 2019). A final AIC model selection analysis was completed for each species, and the top-ranked model was applied to estimate annual density. To assess goodness of fit, we applied a Cramér-von Mises test to each model (Anderson and Darling 1952). Variance and 95% confidence interval calculations were completed using the Distance package in R via the delta method (Buckland et al. 2004).

Trend estimation and assessment

We estimated trend within a Bayesian framework, following previously published methods used to estimate trends of Hawaiian forest bird species using point count data on Hawaiʻi Island (Camp et al. 2010). We fitted log link regression models in JAGS using the rjags package (Plummer 2019). Uninformative normal priors were used for α (intercept) and β (slope), and an uninformative gamma prior was given for τ (variance^-1), which constrained the posterior distribution to the likelihood. Long-term trends were centered on 2011 (2003–2020) and short-term trajectories were centered on 2016 (2011–2020). A burn-in period of 2000 iterations was used for three chains consisting of 50,000 iterations each, totaling 150,000 pooled samples. In a few cases, more iterations were necessary to achieve model convergence, which was evaluated via the Gelman-Rubin convergence diagnostic using the stableGR package (Knudson and Vats 2022).

The benefit of a Bayesian approach in this context is that it allows for the detection of ecologically relevant trends when variability is high (Wade 2000). We defined an ecologically relevant trend as a 50% change over the examined period. With this criterion, long-term trend (β_l) and short-term trajectories (β_s) of density were classified as the following: an ecologically meaningful decrease if β_l < -0.043 and β_s < -0.077, ecologically negligible if -0.043 < β_l < 0.024 and -0.077 < β_s < 0.045, and an ecologically meaningful increase if β_l > 0.024 and β_s > 0.045 (Camp et al. 2008).

To classify the strength of the observed trends and trajectories, βs were assigned a posterior probability (P) by integrating the posterior distribution over the composite hypothetical βs, limited at the thresholds listed above. The resulting probabilities were classified into four classes based on the ratio of the posterior odds to the prior odds, also known as the Bayes factor: very weak if P < 0.1, weak if 0.1 ≤ P < 0.7, strong if 0.7 ≤ P < 0.9, and very strong if P ≥ 0.9 (Wade 2000, Camp et al. 2010). There were insufficient data to estimate annual densities of ʻApapane in the N-fenced region and Eurasian Skylark in the N-fenced and S-fenced regions.

RESULTS

There were four native species detected during the study period that had insufficient data to reliably estimate annual density. There were five detections of Hawaiʻi ʻElepaio (Chasiempis sandwichensis) in the S-fenced region. ʻIo (Buteo solitarius) was detected once in the S-fenced region and once in the partially fenced region. ʻŌmaʻo (Myadestes obscurus) was detected once in the N-fenced region. Pueo (Asio flammeus sandwichensis) was detected 53 times, three in the N-fenced, one in the S-fenced, and 49 in the partially fenced. Short-term trajectory for ʻApapane in the partially fenced region is not reported because that model failed to converge.

The AIC selection process resulted in the hazard rate detection function for all species except for Yellow-fronted Canary (Table 3). Hawaiʻi ʻAmakihi had the highest density of the species observed. ʻApapane annual density estimates were low, all below one bird ha^-1. Warbling White-eye generally had higher density than the other non-native forest birds. We also note the first documentation of the non-native Japanese Bush Warbler (Cettia diphone) at PTA; in the S-fenced region there was one detection in 2013, 26 detections in 2018, and 23 detections in 2019. In the partially fenced region, there was one detection in 2015 and one detection in 2018.

In both fenced regions, long-term trends were mixed whereas short-term trajectories were either upward or negligible, except for ʻApapane (Figs. 3 and 4; Table 4). In the partially fenced region, long-term trends and short-term trajectories were mixed (Fig. 5; Table 4).

DISCUSSION

The forest bird density estimates, long-term population trends, and short-term trajectories presented here fill a regional gap and help extend the understanding of bird populations island wide. Estimated trends and trajectories in the partially fenced region mostly aligned with our expectations; there was very strong evidence for downward long-term trends for ʻApapane, Hawaiʻi ʻAmakihi, House Finch, and Warbling White-eye and evidence for downward short-term trajectories for species that rely on understory vegetation as a food and cover resource—House Finch, Eurasian Skylark, and Yellow-fronted Canary. There were no downward short-term trajectories in either of the fenced regions except for ʻApapane in the S-fenced region.

Fence effects and Hawaiʻi ʻAmakihi

In both fenced regions, there was evidence for upward short-term trajectories in four species: Hawaiʻi ʻAmakihi, House Finch, Warbling White-eye, and Yellow-fronted Canary. For Hawaiʻi ʻAmakihi and House Finch, there was very strong evidence for a switch from long-term downward trend to short-term increasing trajectory. In the S-fenced region, native vegetation cover increased between 2001 and 2014. This may have resulted in an increase in cover and food resources, which could in part explain positive trajectories. An increase in the number of māmane trees, a native tree species and significant food resource for Hawaiʻi ʻAmakihi, could have had a particularly positive impact on that species (Block and Cook 2017). A report produced by the Hawaiʻi Natural Heritage Program showed that among sampling sites stratified to include all the major divisions of native vegetation at PTA, vegetation in the southern portion of the S-fenced region showed both highest overall arthropod richness and highest native arthropod species richness (Oboyski 1998). Hawaiʻi ʻAmakihi are more insectivorous than some other species and might have benefited from increased arthropod availability if arthropod communities responded positively to the observed increase in native vegetation in the S-fenced region observed between 2001 and 2014 (Scott et al. 1984).

These findings provide weak support but do not confirm that ungulate browsing negatively impacts forest birds at PTA. The response of native vegetation communities to the release from browse pressure is complicated and there can be a significant lag in recovery of native species. The negative effect of introduced ungulates on forest birds has been documented elsewhere in Hawaiʻi, but the actual ecological drivers of the populations observed at PTA are unknown and likely a result of many interacting effects. Drought conditions leading up to 2010 likely contributed to the observed decrease in annual densities for some species. Regions adjacent to PTA showed similar declines for ʻApapane and Warbling White-eye over the same time periods, from 1998–2011 (Banko et al. 2013).

ʻApapane

ʻApapane had too few detections in the N-fenced region to reliably model density and trends. ʻŌhiʻa woodland and māmane-naio woodland have no doubt been impacted by ungulate overbrowsing, but to what degree is uncertain (Banko et al. 2013). Māmane, which can act as alternative food sources for ʻApapane, is impacted by overbrowsing, but ʻōhiʻa flower nectar is their primary food source and ʻōhiʻa woodlands in our study regions have been left mainly intact despite ungulate presence. In 2009, the introduction of an invasive insect, naio thrips (Klambothrips myopori) significantly reduced native naio tree cover, another important alternative food resource for ʻApapane (Block and Cook 2017, Wolfe et al. 2017).

Our finding of low ʻApapane density in these regions aligns with historical records (Berger 1972, Scott et al. 1986, Gon et al. 1993). ʻApapane are known to forage on a larger spatial scale relative to other sympatric forest birds, and they use Mauna Kea as a seasonal foraging site (Banko et al. 2013). Therefore, it follows that they might not be detected as frequently as resident species. Further, our surveys were conducted in December and January—months that fall outside of peak ʻApapane breeding season (February–June; Ralph and Fancy 1994). Indeed, historical data collected at PTA show that ʻApapane detections were lowest in the months of November and December over an annual cycle (Gon et al. 1993). Thus, we suggest that the best interpretation of our ʻApapane result may not be directional population trend, but rather support for historical findings that ʻApapane utilize PTA to a low degree, perhaps as a supplemental foraging resource or movement corridor.

Japanese Bush Warbler

Japanese Bush Warbler colonized Hawaiʻi Island by 1997 and was observed on the west slope of Mauna Kea and in the Kohala region in 2006 and 2017, respectively (Nelson and Vitz 1998, Banko et al. 2013, Burnett et al. 2021). The > 20 detections in the S-fenced region in 2018 and 2019 indicate an immigration event perhaps from the western slopes of Mauna Kea. Japanese Bush Warbler is associated with native and non-native woodlands and shrublands, and diet includes both insects and nectar (Foster 2005, Pyle and Pyle 2017, Burnett et al. 2021). These generalist attributes could facilitate further immigration and establishment at PTA; continued monitoring of this species is warranted.

Eurasian Skylark

The partially fenced region was the only region where Eurasian Skylark had sufficient detections to estimate density. Currently, the ground and vegetation cover outside the fence is overgrazed and severely reduced. Eurasian Skylark forages and nests in ground cover (Campbell et al. 2020). Although Eurasian Skylark frequent disturbed areas such as grazed paddocks, roadsides, and agricultural fields, the extensive overbrowsing may have impacted food and nesting resources and thus caused the short-term downward trajectory.

House Finch

The House Finch model failed the goodness of fit test. Small discrepancies between estimated and true probabilities are increasingly likely to cause the rejection of the hypothesis of perfect fit in larger and larger samples, and the House Finch dataset was large (n = 4673; Nattino et al. 2020). Therefore, we proceeded with trend analysis for that species. Regardless of region or period, the preponderance of data for House Finch showed mostly downward or negligible population trends and trajectories. Access to water troughs can sustain large populations of House Finch in dry regions of Hawaiʻi Island (Van Riper 1976). Twelve watering units for game birds were maintained across PTA until approximately 2000. This access to water may have supported or attracted an artificially high number of House Finch in this region. House Finch density has declined since the watering units were decommissioned, and the shift in water availability may have made the habitat less hospitable.

Warbling White-eye

Warbling White-eye showed a very strong upward short-term trajectory despite high ungulate density in the partially fenced region. Warbling White-eye are able to achieve high densities in Hawaiʻi and have been implicated in playing a strong role in depressing native bird populations because of their facultative omnivory (Mountainspring and Scott 1985). Another study noted Warbling White-eye to have a similar diet to Hawaiʻi ʻAmakihi and suggested that they compete for some habitat resources (Conant 1976). If present, competition could in part explain the difference in slope of the short-term trajectories between Warbling White-eye and Hawaiʻi ʻAmakihi in the partially fenced region, but it is likely just one of many complex interaction effects responsible for the observed patterns.

Conclusion

Fencing and removing ungulates to promote native vegetation regeneration are important management tools for Hawaiian forest birds (Banko et al. 2014). Currently in the partially fenced region, a public hunting program is in place with the goal of reducing ungulate density therein; however, the program is still in development and its effectiveness remains to be seen (Leo 2022). Contemporary records support that some extant native forest birds that move large distances in search of floral resources, such as ʻApapane and ʻIʻiwi (Vestiaria coccinea), typically use dry habitats seasonally. Loss and/or alteration and segmentation of the remaining dry forest habitat may have contributed to the decline or disappearance of other species that formerly inhabited dry forest at and adjacent to PTA such as ʻAkiapolaʻau (Hemignathus munroi), ʻAlalā (Corvus hawaiiensis), Hawaiʻi ʻElepaio, and Palila. Protecting the dry forests at PTA from ungulate impacts will help slow habitat loss and degradation. Because PTA is centrally located on the island, healthy dry forests within the installation may be important to the long-term persistence and recovery of some extant native forest bird species.

We recommend further study to understand forest bird use of ʻōhiʻa flora resources during periods of bloom. ʻApapane enhance ʻōhiʻa fruit set by transferring pollen between trees (Carpenter 1973, 1976). A reduction or absence of ʻApapane from the ʻōhiʻa forest at PTA could impact pollen transfer and fruit set. ʻŌhiʻa is a keystone species at PTA that provides habitats for most of the 20 federally listed threatened and endangered plant species. A better understanding of current pollinator services, or lack thereof, by ʻApapane for ʻōhiʻa may be another key aspect to maintaining resiliency of the ʻōhiʻa forests as the climate continues to change so that these protected, ungulate-free forests persist to support a myriad of native and federally listed threatened and endangered species.

LITERATURE CITED

Anderson, T. W., and D. A. Darling. 1952. Asymptotic theory of certain "goodness of fit" criteria based on stochastic processes. The Annals of Mathematical Statistics 23:193-212. https://doi.org/10.1214/aoms/1177729437

Atkinson, C. T., J. K. Lease, R. J. Dusek, and M. D. Samuel. 2005. Prevalence of pox-like lesions and malaria in forest bird communities on leeward Mauna Loa volcano, Hawaii. Condor 107:537-546. https://doi.org/10.1093/condor/107.3.537

Badyaev, A. V., V. Belloni, and G. E. Hill. 2020. House Finch (Haemorhous mexicanus), version 1.0. In A. F. Poole, editor. Birds of the world. Cornell Lab of Ornithology, Ithaca, New York, USA. https://doi.org/10.2173/bow.houfin.01

Banko, P. C., R. J. Camp, C. Farmer, K. W. Brinck, D. L. Leonard, and R. M. Stephens. 2013. Response of palila and other subalpine Hawaiian forest bird species to prolonged drought and habitat degradation by feral ungulates. Biological Conservation 157:70-77. https://doi.org/10.1016/j.biocon.2012.07.013

Banko, P. C., S. C. Hess, P. G. Scowcroft, C. Farmer, J. D. Jacobi, R. M. Stephens, R. J. Camp, D. L. L. Jr, K. W. Brinck, J. O. Juvik, and S. P. Juvik. 2014. Evaluating the long-term management of introduced ungulates to protect the Palila, an endangered bird, and its critical habitat in subalpine forest of Mauna Kea, Hawaiʻi. Arctic, Antarctic, and Alpine Research 46:871-889. https://doi.org/10.1657/1938-4246-46.4.871

Berger, A. J. 1972. Hawaiian Birds 1972. Wilson Bulletin 84:212-222. https://sora.unm.edu/sites/default/files/journals/wilson/v084n02/p0212-p0222.pdf

Biber, E. 2002. Patterns of endemic extinctions among island bird species. Ecography 25:661-676. https://doi.org/10.1034/j.1600-0587.2002.t01-1-250603.x

Block, P., and R. Cook. 2017. Long-term vegetation monitoring at Pōhakuloa Training Area, Island of Hawai’i, 1989–2015. Colorado State University, Center for Environmental Management of Military Lands, Fort Collins, CO.

Block, P., M. Miller, B. Woolf, and N. Hastings. 2013. PTA vegetation classification and mapping. Colorado State University, Center for Environmental Management of Military Lands, Fort Collins, Colorado, USA.

Buckland, S. T., D. R. Anderson, K. P. Burnham, J. L. Laake, D. L. Borchers, and L. Thomas, editors. 2004. Advanced distance sampling. Oxford University Press, Oxford, UK. https://doi.org/10.1093/oso/9780198507833.001.0001

Buckland, S. T., E. A. Rexstad, T. A. Marques, and C. S. Oedekoven. 2015. Distance sampling: methods and applications. Springer, New York, New York, USA. https://doi.org/10.1007/978-3-319-19219-2

Burnett, K., R. J. Camp, and P. J. Hart. 2021. Current distribution and abundance of Kohala forest birds in Hawaiʻi. Journal of Field Ornithology 92(4):377-287. https://doi.org/10.1111/jofo.12386

Camp, R. J., T. K. Pratt, P. M. Gorresen, J. J. Jeffrey, and B. L. Woodworth. 2010. Population trends of forest birds at Hakalau Forest National Wildlife Refuge, Hawai’i. Condor 112:196-212. https://doi.org/10.1525/cond.2010.080113

Camp, R. J., N. E. Seavy, P. M. Gorresen, and M. H. Reynolds. 2008. A statistical test to show negligible trend: comment. Ecology 89:1469-1472. https://doi.org/10.1890/07-0462.1

Campbell, R. W., L. M. Van Damme, S. R. Johnson, P. Donald, and F. F. J. Garcia. 2020. Eurasian Skylark (Alauda arvensis), version 1.0. In S. M. Billerman, editor. Birds of the world. Cornell Lab of Ornithology, Ithaca, New York, USA. https://birdsoftheworld.org/bow/species/skylar/cur/introduction

Carpenter, F. L. 1976. Plant-pollinator interactions in Hawaii: pollination energetics of Metrosideros collina (Myrtaceae). Ecology, 57(6):1125-1144. https://doi.org/10.2307/1935040

Carpenter, F. L., and MacMillen, R. E. 1973. Interactions between Hawaiian honeycreepers and Metrosideros collina on the island of Hawaii. U.S. International Biological Program Technical Report 33, University of California Irvine, Irvine, California, USA. https://scholarspace.manoa.hawaii.edu/server/api/core/bitstreams/124f30c5-5121-420c-9935-10a9e995afd0/content

Center for Environmental Management of Military Lands (CEMML). 2019. Army natural resources program at Pōhakuloa Training Area. Biennial Report, 01 October 2017-30 September 2019, Colorado State University, Fort Collins, Colorado, USA.

Clement, P. 2020. Yellow-fronted Canary (Crithagra mozambica), version 1.0. In J. del Hoyo, A. Elliott, J. Sargatal, D. A. Christie, and E. de Juana, editors. Birds of the world. Cornell Lab of Ornithology, Ithaca, New York, USA. https://doi.org/10.2173/bow.yefcan.01

Cogan, D., and G. M. Kudray. 2009. Vegetation classification and mapping work plan. Pacific Island Inventory and Monitoring Network. Hawaiʻi National Park, Hawaii, USA.

Conant, S., 1976. Bird distribution and abundance above 3000 feet in Hawaii Volcanoes National Park. Pages 63-74 in Proceedings of the First Conference in Natural Sciences, Hawaii Volcanoes National Park. University of Hawaii at Manoa, Honolulu, Hawaii, USA. https://scholarspace.manoa.hawaii.edu/server/api/core/bitstreams/f2be43b9-9d0e-4a6a-a3bb-e4613d738be8/content

Fancy, S. G., and C. J. Ralph (2020). Apapane (Himatione sanguinea), version 1.0. In A. F. Poole and F. B. Gill, editors. Birds of the world. Cornell Lab of Ornithology, Ithaca, New York, USA. https://doi.org/10.2173/bow.apapan.01

Fortini, L. B., L. R. Kaiser, L. M. Keith, J. Price, R. F. Hughes, J. D. Jacobi, and J. B. Friday. 2019. The evolving threat of Rapid ʻŌhiʻa Death (ROD) to Hawaiʻi’s native ecosystems and rare plant species. Forest Ecology and Management 448:376-385. https://doi.org/10.1016/j.foreco.2019.06.025

Fortini, L. B., A. E. Vorsino, F. A. Amidon, E. H. Paxton, and J. D. Jacobi. 2015. Large-scale range collapse of Hawaiian forest birds under climate change and the need 21st century conservation options. PLoS ONE 10:e0140389. https://doi.org/10.1371/journal.pone.0140389

Foster, J. T. 2005. Exotic bird invasion into forests of Hawaii: demography, competition, and seed dispersal. Dissertation, University of Illinois, Urbana-Champaign, Illinois, USA.

Foster, J. T., B. L. Woodworth, L. E. Eggert, P. J. Hart, D. Palmer, D. C. Duffy, and R. C. Fleischer. 2007. Genetic structure and evolved malaria resistance in Hawaiian honeycreepers. Molecular Ecology 16:4738-4746. https://doi.org/10.1111/j.1365-294X.2007.03550.x

Gon, S. M., L. Honigman, D. Zevin, W. Fulks, and R. E. David. 1993. Vertebrate inventory surveys at the Multipurpose Range Complex Pōhakuloa Training Area (PTA) Island of Hawai’i. US Army Corps of Engineers, Pacific Division. Honolulu, Hawaii, USA.

Gorresen, P. M., R. J. Camp, and T. K. Pratt. 2007. Forest bird distribution, density and trends in the Kaʻū region of Hawaii Island. Open-File Report 2007-1076, U.S. Geological Survey, Reston, Virginia, USA. https://pubs.usgs.gov/of/2007/1076/of2007-1076.pdf

Gorresen, P. M., R. J. Camp, T. K. Pratt, and B. L. Woodworth. 2005. Status of forest birds in the central windward region of Hawaii Island: population trends and power analysis. Open-File Report 2005-1441, U.S. Geological Survey, Reston, Virginia, USA. https://pubs.usgs.gov/of/2005/1441/report.pdf

Johnson, D. H. 2008. In defense of indices: the case of bird surveys. Journal of Wildlife Management 72:857-868. https://doi.org/10.2193/2007-294

Judge, S. W., R. J. Camp, P. J. Hart, and S. T. Kichman. 2018. Population estimates of the endangered Hawaiʻi ʻÂkepa (Loxops coccineus) in different habitats on windward Mauna Loa. Journal of Field Ornithology 89:11-21. https://doi.org/10.1111/jofo.12243

Kendall, S. J. R. A. Rounds, R. J. Camp, A. S. Genz, T. Cady, and D. L. Ball. 2023. Forest bird populations at the Big Island National Wildlife Refuge Complex, Hawaiʻi. Journal of Fish and Wildlife Management 14(2):410-432. https://doi.org/10.3996/JFWM-22-035

Knudson, C., and D. Vats. 2022. stableGR: a stable Gelman-Rubin diagnostic for Markov Chain Monte Carlo. https://cran.r-project.org/web/packages/stableGR/stableGR.pdf

Lea, C. 2011. Vegetation classification guidelines: National Park Service vegetation inventory, version 2.0. National Park Service, US Department of the Interior, Fort Collins, Colorado, USA.

Leo, B. 2022. Evaluating unmarked abundance estimators using remote cameras and aerial surveys. Wildlife Society Bulletin. 46:e1312. https://doi.org/10.1002/wsb.1312

Lindsey, G. D., VanderWerf, E. A., Baker, H. and Baker, P. E. 2020. Hawaiʻi ʻAmakihi (Chlorodrepanis virens), version 1.0. In A. F. Poole, editor. Birds of the World. Cornell Lab of Ornithology, Ithaca, New York, USA. https://birdsoftheworld.org/bow/species/hawama/cur/introduction

Miller, D. L., E. Rexstad, L. Thomas, L. Marshall, and J. L. Laake. 2019. Distance sampling in R. Journal of Statistical Software 89:1-28. https://doi.org/10.18637/jss.v089.i01

Mountainspring, S., and Scott, J. M., 1985. Interspecific competition among Hawaiian forest birds. Ecological Monographs 55(2):219-239. https://doi.org/10.2307/1942558

Nattino, G., Pennell, M. L., and Lemeshow, S. 2020. Assessing the goodness of fit of logistic regression models in large samples: a modification of the Hosmer‐Lemeshow test. Biometrics 76(2):549-560. https://doi.org/10.1111/biom.13249

Nelson, J. T., and A. Vitz. 1998. First reported sighting of Japanese Bush-warbler (Cettia diphone) on the island of Hawaii. ʻElepaio 58(1):1. https://hiaudubon.org/wp-content/uploads/2021/12/Elepaio58.1.pdf

Oboyski, P. 1998. Arthropod survey at Pohakuloa Training Area, Island of Hawaii, Hawaii. Hawaii Natural Heritage Program, report number DAHC77-96-C-0042. Honolulu, Hawaii, USA.

Paxton, E. H., P. M. Gorresen, and R. J. Camp. 2013. Abundance, distribution, and population trends of the iconic Hawaiian Honeycreeper, the ʻIʻiwi (Vestiaria coccinea) throughout the Hawaiian Islands. U.S. Geological Survey, Reston, Virginia, USA. https://pubs.usgs.gov/publication/ofr20131150

Paxton, E. H., M. Laut, J. P. Vetter, and S. J. Kendall. 2018. Research and management priorities for Hawaiian forest birds. Condor 120:557-566. https://doi.org/10.1650/CONDOR-18-25.1

Pimm, S. L., H. L. Jones, and J. Diamond. 1988. On the risk of extinction. American Naturalist 132:757-785. https://doi.org/10.1086/284889

Plummer, M. 2019. rjags: Bayesian graphical models using MCMC. https://cran.r-project.org/package=rjags

Pratt, T. K., C. T. Atkinson, P. C. Banko, B. L. Woodworth, and J. D. Jacobi. 2009. Conservation biology of Hawaiian forest birds: implications for island avifauna. Yale University Press, New Haven, Connecticut, USA.

Pyle, R. L., and P. Pyle. 2017. The birds of the Hawaiian Islands: occurrence, history, distribution, and status. Version 2. B. P. Bishop Museum, Honolulu, Hawaii, USA. http://hbs.bishopmuseum.org/birds/rlp-monograph/

Pyšek, P., T. M. Blackburn, E. García-Berthou, I. Perglová, and W. Rabitsch. 2017. Displacement and local extinction of native and endemic species. Pages 157-175 in M. Vilà and P. E. Hulme, editors. Impact of biological invasions on ecosystem services. Springer International Publishing, Switzerland. https://doi.org/10.1007/978-3-319-45121-3_10

Pyšek, P., P. E. Hulme, D. Simberloff, S. Bacher, T. M. Blackburn, J. T. Carlton, W. Dawson, F. Essl, L. C. Foxcroft, P. Genovesi, J. M. Jeschke, I. Kühn, A. M. Liebhold, N. E. Mandrak, L. A. Meyerson, A. Pauchard, J. Pergl, H. E. Roy, H. Seebens, M. van Kleunen, M. Vilà, M. J. Wingfield, and D. M. Richardson. 2020. Scientists’ warning on invasive alien species. Biological Reviews 95:1511-1534. https://doi.org/10.1111/brv.12627

R Core Team. 2022. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

Ralph, C. J., and S. G. Fancy. 1994. Timing of breeding and molting in six species of Hawaiian honeycreepers. Condor 96:151-161. https://doi.org/10.2307/1369072

Reynolds, R. T., J. M. Scott, and R. A. Nussbaum. 1980. A variable circular-plot method for estimating bird numbers. Condor 309-313. https://doi.org/10.2307/1367399

Scott, J. M., S. Mountainspring, F. Ramsey, and C. Kepler. 1986. Forest bird communities of the Hawaiian Islands: their dynamics, ecology, and conservation. Allen Press, Lawrence, Kansas, USA.

Scott, J. M., S. Mountainspring, C. V. Riper III, C. B. Kepler, J. D. Jacobi, T. A. Burr, and J. G. Giffin. 1984. Annual variation in the distribution, abundance, and habitat response of the palila (Loxioides bailleui). Auk 101:647-664. https://doi.org/10.2307/4086892

Shaw, R. B., and J. M. Castillo. 1997. Plant communities of PTA. Page 105. Colorado State University, Center for Environmental Management of Military Lands, Fort Collins, Colorado, USA.

Van Riper, C. 1976. Aspects of House Finch breeding biology in Hawaii. Condor 78:224-229. https://doi.org/10.2307/1366857

Van Riper, C., and J. M. Scott. 2001. Limiting factors affecting Hawaiian native birds. Studies in Avian Biology 22:221-233. https://sora.unm.edu/sites/default/files/SAB_022_2001%20P221-233_Limiting%20Factors%20Affecting%20Hawaiian%20Native%20Birds_Charles%20van%20Riper%2C%20III%2C%20J.%20Michael%20Scott.pdf

Van Riper, S. G., and B. van Balen (2020). Warbling White-eye (Zosterops japonicus), version 1.0. In S. M. Billerman, editor. Birds of the world. Cornell Lab of Ornithology, Ithaca, New York, USA. https://birdsoftheworld.org/bow/species/warwhe1/cur/introduction

Wade, P. R. 2000. Bayesian methods in conservation biology. Conservation Biology 14:1308-1316. https://doi.org/10.1046/j.1523-1739.2000.99415.x

Whittaker, R. J., and J. M. Fernández-Palacios. 2007. Island biogeography: ecology, evolution, and conservation. Oxford University Press, New York, New York, USA. https://doi.org/10.1093/oso/9780198566113.001.0001

Wolfe, J. D., Ralph, C. J., and Wiegardt, A. (2017). Bottom-up processes influence the demography and life-cycle phenology of Hawaiian bird communities. Ecology, 98(11):2885-2894. https://www.jstor.org/stable/26602223