Hawaiian forest birds serve as an ideal group to explore the extent of climate change impacts on at-risk species. Avian malaria constrains many remaining Hawaiian forest bird species to high elevations where temperatures are too cool for malaria’s life cycle and its principal mosquito vector. The impact of climate change on Hawaiian forest birds has been a recent focus of Hawaiian conservation biology, and has centered on the links between climate and avian malaria. To elucidate the differential impacts of projected climate shifts on species with known varying niches, disease resistance and tolerance, we use a comprehensive database of species sightings, regional climate projections and ensemble distribution models to project distribution shifts for all Hawaiian forest bird species. We illustrate that, under a likely scenario of continued disease-driven distribution limitation, all 10 species with highly reliable models (mostly narrow-ranged, single-island endemics) are expected to lose >50% of their range by 2100. Of those, three are expected to lose all range and three others are expected to lose >90% of their range. Projected range loss was smaller for several of the more widespread species; however improved data and models are necessary to refine future projections. Like other at-risk species, Hawaiian forest birds have specific habitat requirements that limit the possibility of range expansion for most species, as projected expansion is frequently in areas where forest habitat is presently not available (such as recent lava flows). Given the large projected range losses for all species, protecting high elevation forest alone is not an adequate long-term strategy for many species under climate change. We describe the types of additional conservation actions practitioners will likely need to consider, while providing results to help with such considerations.

Occupation of native ecosystems by invasive plant species alters their structure and/or function. In Hawaii, a subset of introduced plants is regarded as extremely harmful due to competitive ability, ecosystem modification, and biogeochemical habitat degradation. By controlling this subset of highly invasive ecosystem modifiers, conservation managers could significantly reduce native ecosystem degradation. To assess the invasibility of vulnerable native ecosystems, we selected a proxy subset of these invasive plants and developed robust ensemble species distribution models to define their respective potential distributions. The combinations of all species models using both binary and continuous habitat suitability projections resulted in estimates of species richness and diversity that were subsequently used to define an invasibility metric. The invasibility metric was defined from species distribution models with 0.8; True Skill Statistic >0.75) as evaluated per species. Invasibility was further projected onto a 2100 Hawaii regional climate change scenario to assess the change in potential habitat degradation. The distribution defined by the invasibility metric delineates areas of known and potential invasibility under current climate conditions and, when projected into the future, estimates potential reductions in native ecosystem extent due to climate-driven invasive incursion. We have provided the code used to develop these metrics to facilitate their wider use (Code S1). This work will help determine the vulnerability of native-dominated ecosystems to the combined threats of climate change and invasive species, and thus help prioritize ecosystem and species management actions.

Aim: Citizen science is a cost‐effective potential source of invasive species occurrence data. However, data quality issues due to unstructured sampling approaches may discourage the use of these observations by science and conservation professionals. This study explored the utility of low‐structure iNaturalist citizen science data in invasive plant monitoring. We first examined the prevalence of invasive taxa in iNaturalist plant observations and sampling biases associated with these data. Using four invasive species as examples, we then compared iNaturalist and professional agency observations and used the two datasets to model suitable habitat for each species.Location: Hawai'i, USA.Methods: To estimate the prevalence of invasive plant data, we compared the number of species and observations recorded in iNaturalist to botanical checklists for Hawai'i. Sampling bias was quantified along gradients of site accessibility, protective status and vegetation disturbance using a bias index. Habitat suitability for four invasive species was modelled in Maxent, using observations from iNaturalist, professional agencies and stratified subsets of iNaturalist data.Results: iNaturalist plant observations were biased towards invasive species, which were frequently recorded in areas with higher road/trail density and vegetation disturbance. Professional observations of four example invasive species tended to occur in less accessible, native‐dominated sites. Habitat suitability models based on iNaturalist versus professional data showed moderate overlap and different distributions of suitable habitat across vegetation disturbance classes. Stratifying iNaturalist observations had little effect on how suitable habitat was distributed for the species modelled in this study.Main Conclusions: Opportunistic iNaturalist observations have the potential to complement and expand professional invasive plant monitoring, which we found was often affected by inverse sampling biases. Invasive species represented a high proportion of iNaturalist plant observations, and were recorded in environments that were not captured by professional surveys. Combining the datasets thus led to more comprehensive estimates of suitable habitat.

As the impacts of global climate change on species are increasingly evident, there is a clear need to adapt conservation efforts worldwide. Species vulnerability assessments (VAs) are increasingly used to summarize all relevant information to determine a species’ potential vulnerability to climate change and are frequently the first step in informing climate adaptation efforts. VAs commonly integrate multiple sources of information by utilizing a framework that distinguishes factors relevant to species exposure, sensitivity, and adaptive capacity. However, this framework was originally developed for human systems, and its use to evaluate species vulnerability has serious practical and theoretical limitations. By instead defining vulnerability as the degree to which a species is unable to exhibit any of the responses necessary for persistence under climate change (i.e., toleration of projected changes, migration to new climate-compatible areas, enduring in microrefugia, and evolutionary adaptation), we can bring VAs into the realm of ecological science without applying borrowed abstract concepts that have consistently challenged species-centric research and management. This response-based framework to assess species vulnerability to climate change allows better integration of relevant ecological data and past research, yielding results with much clearer implications for conservation and research prioritization.