Feeding ecology of birds in a Mist Belt forest in South Africa

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, in fulfilment of the requirements for the degree of Master of Science October 2012 Johannesburg, South AfricaFood is not always readily available and therefore an important limiting resource to birds. South African forests have a similar fruiting and flowering phenology to tropical forests in that food availability fluctuates over space and time. South African indigenous forest is naturally fragmented in a non-forest matrix and therefore differs from tropical forests. Anthropogenic landuse change has contributed to the increased fragmentation of indigenous forest. The isolated nature of patches provides a suitable platform from which to assess changes in a forest bird community between seasons because they are structurally and functionally distinct from the surrounding vegetation. Therefore, the aim of the study was to assess food as a driver of community dynamics and dietary patterns of birds in a Mist Belt Mixed Podocarpus forest patch between two distinct seasons, winter and summer. This was assessed through a combination of field techniques and stable isotope analysis of carbon and nitrogen. Further, birds were categorised as forest specialists, forest generalists, and forest visitors based on published information to provide extra insight into community changes. Species richness and abundance differed between seasons due to the local movements and turnover of birds and due to the influx of migrants into the forest. In addition, nectarivores and frugivores increased in abundance and biomass in the forest in winter when flowers and fruit were readily available from canopy trees, such as Halleria lucida and Podocarpus latifolius. However, insect-eating guilds increased in summer when there was a greater diversity of invertebrates. Nectarivores, granivores, and omnivores vertically tracked food within forest height strat to where it was most abundant, demonstrating a close association with the location of their food resources. Further, the niche of several species, particularly insectivores and nectarivores, broadened in winter when food resources were limited. However, there was niche contraction in several bird species in summer when resources were more readily available. Overall, the forest acted as a refuge for guilds, particularly frugivores and nectarivores, in winter. Furthermore, this study suggested that food limitation is species-specific and does not apply to all species. Understanding the drivers of community change has important implications for forest management and conservation of forest flora and fauna

Bias and precision of crowdsourced recreational activity data from Strava

Recreational activity is the single most valuable ecosystem service in many developed countries with a range of benefits for public health. Crowdsourced recreational activity data is increasingly being adopted in management and monitoring of urban landscapes, however inherent biases in the data make it difficult to generalize patterns to the total population. We used in-situ observations and questionnaires to quantify accuracy in Strava data - a widely used outdoor activity monitoring app – in Oslo, Norway. The precision with which Strava data captured the spatial (R2 = 0.9) and temporal variation (R2 = 0.51) in observed recreational activity (cyclist and pedestrian) was relatively high for monthly time series during summer, although precision degraded at weekly and daily resolutions and during winter. Despite the precision, Strava exhibits significant biases relative to the total recreationist population. Strava activities represented 2.5 % of total recreationist activity in 2016, a proportion that increased steadily to 5.7 % in 2020 due to a growing usership. Strava users are biased toward cyclists (8 % higher than observed), males (15.7 % higher) and middle-aged people (20.4 % higher for ages 35–54). Strava pedestrians that were able to complete a questionnaire survey (>19 years) were biased to higher income brackets and education levels. Future studies using Strava data need to consider these biases – particularly the underrepresentation of vulnerable age (children/elderly) and socio-economic (poor/uneducated) groups. The implementation of Strava data in urban planning processes will depend on accuracy requirements of the application purpose and the extent to which biases can be corrected for. Accuracy Mobility GPS tracking Physical activity Green spacepublishedVersio

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Application of Landsat-derived vegetation trends over South Africa: Potential for monitoring land degradation and restoration

Monitoring vegetation change is important because the nature, extent and rate of change in key measures, such as plant biomass, cover and species composition, provides critical insight into broader environmental and land use drivers and leads to the development of appropriate policy. We used Landsat data between 1984 and 2018 to produce a map of Enhanced Vegetation Index (EVI) change over South Africa at 30 m resolution and an interactive web application to make the analysis both globally applicable and locally meaningful. We found an increase in EVI of 0.37 ± 0.59% yr−1 (mean ± standard deviation), confirming global vegetation greening trends observed with lower-resolution satellites. Mesic, productive biomes including the Albany Thicket and Savanna, exhibited the largest greening trends while browning trends were dominant in more arid biomes, such as the Succulent Karoo and Desert. Although overall EVI trends correspond to vegetation index trends derived from the Advanced Very-High-Resolution Radiometer (8 km resolution), the relative scarcity of Landsat data availability during the 1980 s is a potential source of error. Using repeat very-high-resolution satellite (ca. 3 m resolution) imagery and ground-based photography as reference, we found good correspondence with EVI trends, revealing patterns of degradation (e.g. woody plant encroachment, desertification), and restoration (e.g. increased rangeland productivity, alien clearing) over selected landscapes. The utility of the EVI trend layer to government and industry for monitoring ecosystem changes will be enhanced by the ability to distinguish climatic from anthropogenic drivers of change. This may be partially achieved though interactive exploration of the EVI trends using the application found here: http://evitrend.zsv.co.zaacceptedVersio

Application of Landsat-derived vegetation trends over South Africa: Potential for monitoring land degradation and restoration

Monitoring vegetation change is important because the nature, extent and rate of change in key measures, such as plant biomass, cover and species composition, provides critical insight into broader environmental and land use drivers and leads to the development of appropriate policy. We used Landsat data between 1984 and 2018 to produce a map of Enhanced Vegetation Index (EVI) change over South Africa at 30 m resolution and an interactive web application to make the analysis both globally applicable and locally meaningful. We found an increase in EVI of 0.37 ± 0.59% yr−1 (mean ± standard deviation), confirming global vegetation greening trends observed with lower-resolution satellites. Mesic, productive biomes including the Albany Thicket and Savanna, exhibited the largest greening trends while browning trends were dominant in more arid biomes, such as the Succulent Karoo and Desert. Although overall EVI trends correspond to vegetation index trends derived from the Advanced Very-High-Resolution Radiometer (8 km resolution), the relative scarcity of Landsat data availability during the 1980 s is a potential source of error. Using repeat very-high-resolution satellite (ca. 3 m resolution) imagery and ground-based photography as reference, we found good correspondence with EVI trends, revealing patterns of degradation (e.g. woody plant encroachment, desertification), and restoration (e.g. increased rangeland productivity, alien clearing) over selected landscapes. The utility of the EVI trend layer to government and industry for monitoring ecosystem changes will be enhanced by the ability to distinguish climatic from anthropogenic drivers of change. This may be partially achieved though interactive exploration of the EVI trends using the application found here: http://evitrend.zsv.co.z