Aim The sub-escarpment coastal plains of South Africa provide remarkable opportunities to study the determinants of biome boundaries as numerous biomes are found closely juxtaposed, including the Nama-Karoo semidesert shrubland and Albany subtropical thicket. The Nama-Karoo shrubland is centred on the semi-arid and frosty high-elevation interior plateau of South Africa, whereas the Albany subtropical thicket inhabits the comparatively warmer sub-escarpment coastal plains. We examined the role of winter frosts in determining the boundaries between these two biomes on the coastal plain.Location Kaboega, Eastern Cape, South Africa.Methods We determined the relative freezing tolerance of thicket and Nama-Karoo communities by sampling dominant species from each biome in a small study site (c. 50 ha) spanning a clear vegetation and minimum temperature boundary. Freezing-induced stress on leaf photosynthesis was measured using chlorophyll fluorescence imaging across a range of subzero treatments. ResultsIn general, largely irrespective of any possible effects of temperature acclimation, freezing exposure significantly reduced photosynthetic efficiency (F v /F m ) values in thicket species relative to those from the Nama-Karoo shrubland across all treatments.Main conclusions As reduced photosynthetic efficiency is generally associated with leaf damage, and species from both these biomes are largely evergreen, we interpreted our results in terms of species-level frost resistance. Therefore, our results support the hypothesis that frost occurrence is a primary driver of the boundary between the subtropical thicket and Nama-Karoo shrubland in South Africa. This has implications for both regional-and landscape-level planning of restoration efforts and predicting boundary shifts under altered climates of the past and future.

a b s t r a c t a r t i c l e i n f o Available online xxxx Edited by WJ BondNumerous shrublands exist in areas where soil moisture should support tree growth. In South Africa, the dwarf shrublands of the Nama-Karoo biome and tree-dominated vegetation of the Albany Subtropical Thicket biome share a boundary that is often abrupt. This boundary is not associated with edaphic or rainfall transitions. Field observations and leaf-level experiments suggest that the vulnerability of thicket species to frost damage is responsible for this thicket-shrubland boundary. We tested this hypothesis by establishing cuttings of Portulacaria afra (spekboom) -a dominant thicket succulent shrub that is a keystone species in arid forms of thicket -in two separate transplant experiments. Firstly across a topographic gradient from frost-free, thicket clad slopes to frostprone, karoo shrubland dominated valley floor; and secondly, inside and outside a thicket clump in the frostprone valley floor. We quantified the effects of frost on spekboom by measuring photosynthetic efficiency (F v / F m ), leaf number, estimating the percentage of healthy stem before and after frost events in June 2013. Frost-exposed spekboom cuttings rapidly underwent declines in photosynthetic efficiency, followed by severe leaf and stem necrosis; herbivores played no role in these declines. Those planted on the frost-free valley slopes or under the frost-protecting thicket canopy on the valley floor remained largely unaffected. This supports the hypothesis that frost-exposure is likely to be the main factor determining the growth rates, and ultimately survival of spekboom. These results suggest that frost occurrence is an important factor involved in determining the boundaries between the arid, and spekboom-rich subtypes of Albany Subtropical Thicket, and the frost tolerant shrublands of the Nama-Karoo.

Restoration of subtropical thicket in South Africa using the plant Portulacaria afra (an ecosystem engineer) has been hampered, in part, by selecting sites that are frost prone—this species is intolerant of frost. Identifying parts of the landscape that are exposed to frost is often challenging. Our aim is to calibrate an existing cold-air pooling (CAP) model to predict where frost is likely to occur in the valleys along the sub-escarpment lowlands (of South Africa) where thicket is dominant. We calibrated this model using two valleys that have been monitored during frost events. To test the calibrated CAP model, model predictions of frost-occurrence for six additional valleys were assessed using a qualitative visual comparison of existing treelines in six valleys—we observe a strong visual match between the predicted frost and frost-free zones with the subtropical thicket (frost-intolerant) and Nama-Karoo shrubland (frost-tolerant) treelines. In addition, we tested the model output using previously established transplant experiments; ∼300 plots planted with P. afra (known as the Thicket-Wide Plots) were established across the landscape—without consideration of frost—to assess the potential factors influencing the survival and growth of P. afra. Here we use a filtered subset of these plots (n = 70), and find that net primary production of P. afra was significantly lower in plots that the model predicted to be within the frost zone. We suggest using this calibrated CAP model as part of the site selection process when restoring subtropical thicket in sites that lie within valleys—avoiding frost zones will greatly increase the likelihood of restoration success.

This study examines the soil bacterial diversity in the Portulacaria afra-dominated succulent thicket vegetation of the Albany Subtropical Thicket biome; this biome is endemic to South Africa. The aim of the study was to compare the soil microbiomes between intact and degraded zones in the succulent thicket and identify environmental factors which could explain the community compositions. Bacterial diversity, using 16S amplicon sequencing, and soil physicochemistry were compared across three zones: intact (undisturbed and vegetated), degraded (near complete removal of vegetation due to browsing) and restored (a previously degraded area which was replanted approximately 11 years before sampling). Amplicon Sequence Variant (ASV) richness was similar across the three zones, however, the bacterial community composition and soil physicochemistry differed across the intact and degraded zones. We identified, via correlation, the potential drivers of microbial community composition as soil density, pH and the ratio of Ca to Mg. The restored zone was intermediate between the intact and degraded zones. The differences in the microbial communities appeared to be driven by the presence of plants, with plant-associated taxa more common in the intact zone. The dominant taxa in the degraded zone were cosmopolitan organisms, that have been reported globally in a wide variety of habitats. This study provides baseline information on the changes of the soil bacterial community of a spatially restricted and threatened biome. It also provides a starting point for further studies on community composition and function concerning the restoration of degraded succulent thicket ecosystems.

<p>Accurate information on the spatial distribution of plant species and communities is in high demand for various fields of application, such as nature conservation, forestry, and agriculture. A series of studies has shown that CNNs accurately predict plant species and communities in high-resolution remote sensing data, in particular with data at the centimeter scale acquired with Unoccupied aerial vehicles (UAV). However, such tasks require ample training data to generate transferable CNN models. Reference data are commonly generated via geocoded in-situ observations or labeling of remote sensing data through visual interpretation. Both approaches are commonly laborious and can present a critical bottleneck for CNN applications. An alternative source of training data is given by using knowledge on the appearance of plants in the form of plant photographs from citizen science projects such as the iNaturalist database. Such crowd-sourced plant photos are expected to be very heterogeneous, and often show a different perspective compared to the typical bird-perspective of remote sensing data. Still, crowd-sourced plant photos could be a valuable source to overcome the challenge of limited training data and reduce the efforts for field data collection and data labeling. Here, we explore the potential of transfer learning from such a crowd-sourced data treasure to the remote sensing context. Therefore, we investigate firstly, if we can use crowd-sourced plant photos for CNN training and subsequent mapping of plant species in high-resolution remote sensing imagery. Secondly, we test if the predictive performance can be increased by a priori selecting photos that share a more similar perspective to the remote sensing data. Therefore, we used three case studies to test our proposed approach using multiple RGB orthoimages acquired from UAV for the target plant species Fallopia japonica (F. japonica), Portulacaria Afra (P. afra), and 10 different tree species, respectively. For training the CNN models, we queried the iNaturalist database for photos of the target species and the surrounding species that are expected in the areas of each case study. We trained CNN models with an EfficientNet-B07 backbone. For applying these models based on the crowd-sourced data to the remote sensing imagery, we used a sliding window approach with a 10 percent overlap. The individual sliding-window-based predictions were spatially aggregated in order to create a high-resolution classification map. Our results demonstrate that CNN models trained with heterogeneous, crowd-sourced plant photos can indeed predict the target species in UAV orthoimages with surprising accuracy. Filtering the crowd-sourced photos used for training by acquisition properties increased the predictive performance. This study demonstrates that citizen science data can effectively anticipate a common bottleneck for vegetation assessments and provides an example on how we can effectively harness the ever-increasing availability of crowd-sourced and big data for remote sensing applications.</p>