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Questions: Are graminoids more poorly detected than other life forms of vascular plants in surveys? How well do observer‐, species‐, and site‐specific variables explain variation in detection of Carex species across forests of different structure? Location: Northeastern Alberta, Canada. Methods: Species inventories were assessed within 50 belt transects, each 100 m in length and 2 m in width. Pseudoturnover was estimated for four life forms and all encountered species. Site‐specific factors were then compared with pseudoturnover of all vascular plants and graminoids using generalized linear regression. Carex detection probabilities were compared based on morphological groups. Detection success at a site and delays in detection within a site were assessed using logistic regression with AIC used to rank a‐priori hypotheses and standardized variables used to determine effect sizes of parameters related to plant detectability. Results: Pseudoturnover for graminoids was similar to that for other life forms and best related to ground layer cover. Morphological groups related to differences in detection, with short, small‐inflorescence Carex most poorly detected. Detection failure was best explained by species abundance and morphology, but delays were more tied to a site's vegetation structure and species abundance than to species morphology. Conclusions: Surveys targeting graminoids, including species of Carex, can achieve high detection rates with high survey effort over small areas, but should consider species‐ and site‐specific biases in detection success. Abundance is likely the most influential factor in determining detection success, and this must be accounted for when searching for low‐density species. We recommend that increased effort (time, repeat observations) be applied when searching for morphologically small graminoids. Imperfect detection is inevitable in surveys, and the degree to which this causes bias against certain taxa is unclear. Graminoids were not more overlooked than other life‐forms, but detectability varies within this group. Abundance and species morphology were the most important determinants of detection success among Carex. Our study showed clear bias toward short, small‐inflorescence Carex, even given high survey effort. [ABSTRACT FROM AUTHOR]

Environmental niche modeling is an increasingly common tool in conservation and management of non-timber species. In particular, models of species' habitats have been aided by new advances in remote sensing and it is now possible to relate forest structure variables to understory species at a relatively high resolution over large spatial scales. Here, we model landscape responses for a culturally-valued keystone shrub, velvet-leaf blueberry (Vaccinium myrtilloides Michaux), in northeast Alberta, Canada, to better understand the environmental factors promoting or limiting its occurrence, abundance, and fruit production, and to guide regional planning. Occurrence and abundance were measured at 845 and 335 sites, respectively, with both strongly related to land cover type and topo-edaphic factors. However, their influence varied widely, reflecting differences in the processes affecting occurrence and abundance. We then used airborne laser scanning (ALS) to characterize horizontal forest canopy cover for the study area, and related this and other geospatial variables to patterns in fruit production where we demonstrated a five-fold increase in fruit production from closed to open forest stands. We then simulated forest canopy thinning across the study area to identify places where gains in fruit production would be greatest following natural disturbance or directed management (e.g., thinning, prescribed burning). Finally, we suggest this approach could be used to identify sites for habitat enhancements to offset direct (land use change) or indirect (access) losses of resources in areas impacted with resource extraction activities, or simply to increase a culturally-valued resource through management. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]