Abstract
Predicting habitat for rare species at landscape scales is a common goal of environmental monitoring, management, and conservation; however, the ability to meet that objective is often limited by the paucity of location records and availability of spatial predictors that effectively describe their habitat. To address this challenge, we used an adaptive, model-based iterative sampling design to direct four years of rare plant surveys within 0.25 ha plots across 602 sites in northeast Alberta, Canada. We used these location records to model and map rare plant habitats for the region using a suite of geospatial predictors including airborne light detection and ranging (LiDAR) vegetation structure metrics, land cover types, soil pH, and a terrain wetness model. Our results indicated that LiDAR-derived vegetation structural metrics and land cover were the most important individual factors, but all variables contributed to predicting the occurrence of rare plants. For LiDAR variables, rarity was negatively related to maximum canopy height, but positively related to canopy relief ratio. Rarity was therefore more likely in places with shorter canopy heights and greater structural complexity. This included fens, which overall had the highest rates of rare plant occurrence. Model-based allocation of sampling led to detections of uncommon species at nearly all sites, while the rarest species in the region were detected at an average encounter rate of 8%. Landscape predictions of rare plant habitat can improve our understanding of environmental limits in rarity, guide local management decisions and monitoring plans, and provide regional tools for assessing impacts from resource development.
Highlights
A key tool in biodiversity conservation is mapping where species are most likely to occur.This allows for the understanding of patterns in species occurrence, identification of biodiversity hotspots, and provides needed visualizations for managers
A total of 59 observations of 17 rare vascular plant species were detected within 47 of the 602 plots (~8% of sites with at least one S1 or S2 species; 39 plots when limited to the extent of available light detection and ranging (LiDAR) data) (Figure 3a)
When considering landscape predictors of rarity, we found that was the Ducks Unlimited Enhanced Wetland Classification (DU-EWC) effective in predicting the occurrence of rare species, but so were LiDAR-derived vegetation structure metrics [23], vegetation height (95th percentile) and canopy relief ratio (CRR)
Summary
A key tool in biodiversity conservation is mapping where species are most likely to occur This allows for the understanding of patterns in species occurrence, identification of biodiversity hotspots, and provides needed visualizations for managers. Creating these products and understanding broad patterns in the spatial distribution of taxa require field data that can be difficult and costly to acquire. Sampling and modeling of rare species (i.e., those of high conservation value) is complicated by the fact that they are often cryptic in nature, associated with uncommon habitats, or are largely data-deficient [2] These factors contribute to knowledge gaps on rare species including information on where they are most likely to occur, how they may respond to disturbances, and where best to allocate limited conservation resources
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