Background: Trees play crucial roles in the biosphere and societies worldwide, with a total of 60,065 tree species currently identified. Increasingly, a large amount of data on tree species occurrences is being generated worldwide: from inventories to pressed plants. While many of these data are currently available in big databases, several challenges hamper their use, notably geolocation problems and taxonomic uncertainty. Further, we lack a complete picture of the data coverage and quality assessment for open/public databases of tree occurrences. Methods: We combined data from five major aggregators of occurrence data (e.g. Global Biodiversity Information Facility, Botanical Information and Ecological Network v.3, DRYFLOR, RAINBIO and Atlas of Living Australia) by creating a workflow to integrate, assess and control data quality of tree species occurrences for species distribution modeling. We further assessed the coverage - the extent of geographical data - of five economically important tree families (Arecaceae, Dipterocarpaceae, Fagaceae, Myrtaceae, Pinaceae). Results: Globally, we identified 49,206 tree species (84.69% of total tree species pool) with occurrence records. The total number of occurrence records was 36.69 M, among which 6.40 M could be considered high quality records for species distribution modeling. The results show that Europe, North America and Australia have a considerable spatial coverage of tree occurrence data. Conversely, key biodiverse regions such as South-East Asia and central Africa and parts of the Amazon are still characterized by geographical open-public data gaps. Such gaps are also found even for economically important families of trees, although their overall ranges are covered. Only 15,140 species (26.05%) had at least 20 records of high quality. Conclusions: Our geographical coverage analysis shows that a wealth of easily accessible data exist on tree species occurrences worldwide, but regional gaps and coordinate errors are abundant. Thus, assessment of tree distributions will need accurate occurrence quality control protocols and key collaborations and data aggregation, especially from national forest inventory programs, to improve the current publicly available data.

Landscape change and biodiversity decline is a global problem and has sparked world-wide initiatives promoting biological conservation techniques such as reintroductions, protected area networks, and both preservation and restoration of landscape connectivity. Despite the increasing abundance of such working landscapes (i.e. “human-modified” landscapes), we know relatively little about their ecological mechanics; these landscapes can be vast, encompassing areas too large to obtain high resolution ecological data to test ecological process. To investigate the ecological mechanics of working landscapes, I use a small, tractable, landscape mesocosm situated in east-central Alberta, Canada, The Cooking Lake Moraine (a.k.a. the Beaver Hills Biosphere). The chapters within this dissertation quantify biodiversity across a hierarchy of measurements (from genes to communities) and investigate consistencies in ecological processes generating patterns in these biodiversity measurements across spatial scales. As a result, I investigate both a depth, and breadth, of spatial ecological processes underlying the efficacy of biodiversity conservation techniques in heterogeneous working landscapes. In Chapter I, I explore between-landscape functional connectivity by investigating the genetic contribution of reintroduced individuals to an ostensibly successfully reintroduced population within the mesocosm. I find that contemporary animals are the result of recolonization from adjacent sources rather than putative reintroduction founding individuals, indicating greater mesocosm functional connectivity to adjacent landscapes than previously thought. In Chapter II, I probe within-landscape functional connectivity by quantifying the contribution of protected areas, natural, and anthropogenic landscape features to animal movement across the mesocosm. I find that natural features had the largest effect on animal movements, despite the presence of protected areas. Chapter III investigates protected area network efficacy on biodiversity conservation by quantifying the contribution of protected areas, natural, and anthropogenic landscape features to mammalian functional diversity across multiple spatial scales within the mesocosm. I find that protected areas rarely predict functional diversity across spatial scales; instead natural features positively predict functional diversity at small spatial scales while anthropogenic features are negatively associated with biodiversity at large spatial scales. Finally, Chapter IV ties the previous three chapters together by testing implicit assumptions of the species occurrence data collected in each. I compare GPS collar data (Chapter II) to species occurrence data collected on wildlife cameras (Chapter III) to demonstrate that the magnitude of animal movements better predict species occurrence than the commonly assumed proximity of animal space use. Across chapters, two central themes emerge from this dissertation. First, the importance of natural features at small spatial scales, and anthropogenic features at large spatial scales, within the landscape matrix is predominant in predicting multiple measures of biodiversity. And second, we cannot assume predictable efficacy of conservation strategies or even the ecological process inferred from the data collected to test these strategies. / Graduate