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Abstract
Openly available species observation records on various online platforms achieve good coverage in urban areas. Thus, such digital data could provide a basis for biodiversity assessments in cities. Here, we investigated suitability of open digital species occurrence data, compared with systematically field surveyed data, in Freiburg, Germany (a Western city) and Dhaka, Bangladesh (a global-South city). We focused on resident bird species richness as an indicator of local biodiversity. We collected avian records for urban areas from 'ornitho.de' in Freiburg and 'gbif.org' in Dhaka. Additionally, we conducted point count surveys at several urban locations in both cities. Using these records, we prepared three grid (cell size 250 m × 250 m) based datasets—open digital dataset (i.e. records compiled from well-surveyed grid cells), field surveyed dataset (i.e. records of systematic bird surveys) and combined dataset (i.e. digital data and field data combined). We compared the relationship of resident bird richness with different habitat factors by applying linear regression models, separately using each of the three datasets. We assessed suitability of data from online platforms by comparing the variables retained after model selection based on digital data versus field surveyed data. We found that field surveyed data and combined data did not alter general understanding of the key driving factors of bird richness patterns we obtained from open digital data. This held for both case examples, Freiburg vs. Dhaka, respectively. This suggests that open digital data from well-surveyed urban locations can provide a suitable basis to assess drivers of biodiversity patterns within cities.

Due to legislative protection, many species, including large carnivores, are currently recolonizing Europe. To address the impending human-wildlife conflicts in advance, predictive habitat models can be used to determine potentially suitable habitat and areas likely to be recolonized. As field data are often limited, quantitative rule based models or the extrapolation of results from other studies are often the techniques of choice. Using the wolf (Canis lupus) in Germany as a model for habitat generalists, we developed a habitat model based on the location and extent of twelve existing wolf home ranges in Eastern Germany, current knowledge on wolf biology, different habitat modeling techniques and various input data to analyze ten different input parameter sets and address the following questions: (1) How do a priori assumptions and different input data or habitat modeling techniques affect the abundance and distribution of potentially suitable wolf habitat and the number of wolf packs in Germany? (2) In a synthesis across input parameter sets, what areas are predicted to be most suitable? (3) Are existing wolf pack home ranges in Eastern Germany consistent with current knowledge on wolf biology and habitat relationships? Our results indicate that depending on which assumptions on habitat relationships are applied in the model and which modeling techniques are chosen, the amount of potentially suitable habitat estimated varies greatly. Depending on a priori assumptions, Germany could accommodate between 154 and 1769 wolf packs. The locations of the existing wolf pack home ranges in Eastern Germany indicate that wolves are able to adapt to areas densely populated by humans, but are limited to areas with low road densities. Our analysis suggests that predictive habitat maps in general, should be interpreted with caution and illustrates the risk for habitat modelers to concentrate on only one selection of habitat factors or modeling technique.

Abstract: Bird species richness has often been used as an indicator of urbanisation effects on biodiversity in North America and Europe, but similar studies are rare in rapidly urbanising regions in the tropics. Here we assess the effect of different urban environmental factors on local resident bird species richness at different spatial extents in South Asian cities. Bird data at 57 urban locations distributed across 11 cities were retrieved from the available literature. Different variables assessing the degree of urbanisation and urban habitat factors were measured, at 1000 m and 5000 m radius scale extent, for each urban location. We investigated how resident bird species richness was affected by urban environmental predictors by fitting linear regression models in a Bayesian framework. Our model suggested strong positive influence of the interaction between habitat Shannon metric (a proxy of habitat heterogeneity) and proportion of impervious surface (a proxy of human built-up and settlement extent) on local resident bird richness at both spatial scales. Increasing values of habitat Shannon metric positively related to increasing bird richness, but only when the proportion of impervious surface was very high. Our results suggest that areas with a high degree of urbanisation necessitate an increase of habitat heterogeneity to maintain high local bird diversity. Increasing the quality and the compositional variability of remaining bird habitat patches in highly built-up areas should be a major conservation concern within cities of South Asia