Patterns in bird and pollinator occupancy and richness in a mosaic of urban office parks across scales and seasons.

Urbanization is a leading cause of global biodiversity loss, yet cities can provide resources required by many species throughout the year. In recognition of this, cities around the world are adopting strategies to increase biodiversity. These efforts would benefit from a robust understanding of how natural and enhanced features in urbanized areas influence various taxa. We explored seasonal and spatial patterns in occupancy and taxonomic richness of birds and pollinators among office parks in Santa Clara County, California, USA, where natural features and commercial landscaping have generated variation in conditions across scales. We surveyed birds and insect pollinators, estimated multi-species occupancy and species richness, and found that spatial scale (local, neighborhood, and landscape scale), season, and urban sensitivity were all important for understanding how communities occupied sites. Features at the landscape (distance to streams or baylands) and local scale (tree canopy, shrub, or impervious cover) were the strongest predictors of avian occupancy in all seasons. Pollinator richness was influenced by local tree canopy and impervious cover in spring, and distance to baylands in early and late summer. We then predicted the relative contributions of different spatial scales to annual bird species richness by simulating "good" and "poor" quality sites based on influential covariates returned by the previous models. Shifting from poor to good quality conditions locally increased annual avian richness by up to 6.8 species with no predicted effect on the quality of the neighborhood. Conversely, sites of poor local and neighborhood scale quality in good-quality landscapes were predicted to harbor 11.5 more species than sites of good local- and neighborhood-scale quality in poor-quality landscapes. Finally, more urban-sensitive bird species were gained at good quality sites relative to urban tolerant species, suggesting that urban natural features at the local and landscape scales disproportionately benefited them.

The Biological Deserts Fallacy: Cities in Their Landscapes Contribute More than We Think to Regional Biodiversity.

Cities are both embedded within and ecologically linked to their surrounding landscapes. Although urbanization poses a substantial threat to biodiversity, cities also support many species, some of which have larger populations, faster growth rates, and higher productivity in cities than outside of them. Despite this fact, surprisingly little attention has been paid to the potentially beneficial links between cities and their surroundings. We identify five pathways by which cities can benefit regional ecosystems by releasing species from threats in the larger landscape, increasing regional habitat heterogeneity and genetic diversity, acting as migratory stopovers, preadapting species to climate change, and enhancing public engagement and environmental stewardship. Increasing recognition of these pathways could help cities identify effective strategies for supporting regional biodiversity conservation and could provide a science-based platform for incorporating biodiversity alongside other urban greening goals.

Opportunities and Constraints in Characterizing Landscape Distribution of an Invasive Grass from Very High Resolution Multi-Spectral Imagery.

Understanding spatial distributions of invasive plant species at early infestation stages is critical for assessing the dynamics and underlying factors of invasions. Recent progress in very high resolution remote sensing is facilitating this task by providing high spatial detail over whole-site extents that are prohibitive to comprehensive ground surveys. This study assessed the opportunities and constraints to characterize landscape distribution of the invasive grass medusahead (Elymus caput-medusae) in a ∼36.8 ha grassland in California, United States from 0.15m-resolution visible/near-infrared aerial imagery at the stage of late spring phenological contrast with dominant grasses. We compared several object-based unsupervised, single-run supervised and hierarchical approaches to classify medusahead using spectral, textural, and contextual variables. Fuzzy accuracy assessment indicated that 44-100% of test medusahead samples were matched by its classified extents from different methods, while 63-83% of test samples classified as medusahead had this class as an acceptable candidate. Main sources of error included spectral similarity between medusahead and other green species and mixing of medusahead with other vegetation at variable densities. Adding texture attributes to spectral variables increased the accuracy of most classification methods, corroborating the informative value of local patterns under limited spectral data. The highest accuracy across different metrics was shown by the supervised single-run support vector machine with seven vegetation classes and Bayesian algorithms with three vegetation classes; however, their medusahead allocations showed some "spillover" effects due to misclassifications with other green vegetation. This issue was addressed by more complex hierarchical approaches, though their final accuracy did not exceed the best single-run methods. However, the comparison of classified medusahead extents with field segments of its patches overlapping with survey transects indicated that most methods tended to miss and/or over-estimate the length of the smallest patches and under-estimate the largest ones due to classification errors. Overall, the study outcomes support the potential of cost-effective, very high-resolution sensing for the site-scale detection of infestation hotspots that can be customized to plant phenological schedules. However, more accurate medusahead patch delineation in mixed-cover grasslands would benefit from testing hyperspectral data and using our study's framework to inform and constrain the candidate vegetation classes in heterogeneous locations.

Hotspots of Community Change: Temporal Dynamics Are Spatially Variable in Understory Plant Composition of a California Oak Woodland.

Community response to external drivers such climate and disturbance can lead to fluctuations in community composition, or to directional change. Temporal dynamics can be influenced by a combination of drivers operating at multiple spatial scales, including external landscape scale drivers, local abiotic conditions, and local species pools. We hypothesized that spatial variation in these factors can create heterogeneity in temporal dynamics within landscapes. We used understory plant species composition from an 11 year dataset from a California oak woodland to compare plots where disturbance was experimentally manipulated with the removal of livestock grazing and a prescribed burn. We quantified three properties of temporal variation: compositional change (reflecting the appearance and disappearance of species), temporal fluctuation, and directional change. Directional change was related most strongly to disturbance type, and was highest at plots where grazing was removed during the study. Temporal fluctuations, compositional change, and directional change were all related to intrinsic abiotic factors, suggesting that some locations are more responsive to external drivers than others. Temporal fluctuations and compositional change were linked to local functional composition, indicating that environmental filters can create subsets of the local species pool that do not respond in the same way to external drivers. Temporal dynamics are often assumed to be relatively static at the landscape scale, provided disturbance and climate are continuous. This study shows that local and landscape scale factors jointly influence temporal dynamics creating hotspots that are particularly responsive to climate and disturbance. Thus, adequate predictions of response to disturbance or to changing climate will only be achieved by considering how factors at multiple spatial scales influence community resilience and recovery.