This dataset presents a fine-grained population map of Rwanda with a resolution of 100 meters for 2020, generated using the POMELO super-resolution technique that is based on deep learning. Please refer to our Nature Scientific Reports publication for more details.

Background: Traditionally, many countries, including those in sub-Saharan Africa, rely on aggregated census data over expansive spatial units, which are not always timely or accurate. The need for detailed population maps is paramount in several sectors, including urban development, environmental supervision, public health, and humanitarian initiatives. Addressing this gap, the POMELO methodology leverages coarse census data in conjunction with open geodata to produce high precision population maps.

Key Features: Resolution: The map offers a granular view with a 100m ground sampling distance, providing intricate details about population distributions in Rwanda. Data Sources: Utilizing a combination of projected admisistrative census data (UN), and supplementing it with open geodata. Reliability: In comparative experiments conducted in sub-Saharan Africa, POMELO's ability to disaggregate coarse census counts achieved R2 values of 85-89%. Furthermore, its potential to predict population numbers without any census data reached accuracy levels of 48-69%.

This dataset summarizes demographic rates, abundances and basal area across a succession of ~800 (sub) tropical tree species to explore generalities in demographic trade-offs and successional shifts in demographic strategies across four Neotropical forests that cover a large rainfall gradient. We used repeated forest inventory data from chronosequences in two wet (Costa Rica, Panama) and two dry forests (Yucatán, Oaxaca, both Mexico) to quantify demographic rates of ~800 tree species. For each forest, we explored the main demographic trade-offs and assigned tree species to five demographic groups by performing a weighted Principal Component Analysis (PCA) that accounts for differences in sample size. We aggregated the basal area and abundance across demographic groups to identify successional shifts in demographic strategies over the entire successional gradient from very young (<5 years) to old-growth forests. This dataset provides raw and transformed demographic rates, their weights in the weighted PCA, assignments to demographic groups, and forest inventory data at the species level, as well as the code for performing the weighted PCA. Methods See Rüger et al. in revision. Successional shifts in tree demographic strategies in wet and dry Neotropical forests. Global Ecology and Biogeography The file ‘weightedPCA.R’ contains an implementation of the weighted principal component analysis described in Delchambre, L. 2014. Weighted principal component analysis: a weighted covariance eigendecomposition approach. Mon. Not. R. Astron. Soc. 446(2), 3545-3555.

This dataset presents a fine-grained population map of Zambiawith a resolution of 100 meters for 2020, generated using the POMELO super-resolution technique that is based on deep learning. Please refer to our Nature Scientific Reports publication for more details.

Background: Traditionally, many countries, including those in sub-Saharan Africa, rely on aggregated census data over expansive spatial units, which are not always timely or accurate. The need for detailed population maps is paramount in several sectors, including urban development, environmental supervision, public health, and humanitarian initiatives. Addressing this gap, the POMELO methodology leverages coarse census data in conjunction with open geodata to produce high precision population maps.

Key Features: Resolution: The map offers a granular view with a 100m ground sampling distance, providing intricate details about population distributions in Zambia. Data Sources: Utilizing a combination of projected admisistrative census data (UN), and supplementing it with open geodata. Reliability: In comparative experiments conducted in sub-Saharan Africa, POMELO's ability to disaggregate coarse census counts achieved R2 values of 85-89%. Furthermore, its potential to predict population numbers without any census data reached accuracy levels of 48-69%.

Ecological drift causes species abundances to fluctuate randomly, lowering diversity within communities and increasing differences among otherwise equivalent communities. Despite broad interest in ecological drift, ecologists have little experimental evidence of its consequences in nature, where competitive forces modulate species abundances. We manipulated drift by imposing 40-fold variation in the size of experimentally assembled annual plant communities and holding their edge-to-interior ratios comparable. Drift over three generations was greater than predicted by neutral models, causing high extinction rates and fast divergence in composition among smaller communities. Competitive asymmetries drove populations of most species to small enough sizes that demographic stochasticity could markedly influence dynamics, increasing the importance of drift in communities. The strong effects of drift occurred despite stabilizing niche differences, which cause species to have greater population growth rates when at low local abundance. Overall, the importance of ecological drift appears greater in non-neutral communities than previously recognized, and varies with community size and the type and strength of density dependence.

Predicting the speed of biological invasions and native species migrations requires an understanding of the ecological and evolutionary dynamics of spreading populations. Theory predicts that evolution can accelerate species’ spread velocity, but how landscape patchiness—an important control over traits under selection—influences this process is unknown. We manipulated the response to selection in populations of a model plant species spreading through replicated experimental landscapes of varying patchiness. After six generations of change, evolving populations spread 11% farther than nonevolving populations in continuously favorable landscapes and 200% farther in the most fragmented landscapes. The greater effect of evolution on spread in patchier landscapes was consistent with the evolution of dispersal and competitive ability. Accounting for evolutionary change may be critical when predicting the velocity of range expansions.

1. Reliable predictions of population spread rates are essential to forecast biological invasions. Recent studies have shown that populations spreading through favourable habitat can rapidly evolve higher dispersal and reproductive rates at the expansion front, which accelerates spread velocity. However, spreading populations are likely to eventually encounter stressful conditions in the expanded range. How evolution during spread in favourable environments affects subsequent population growth in harsher environments is currently unknown. 2. We examined evolutionary change in performance under drought, interspecific competition and heat stress for populations of Arabidopsis thaliana that experienced six generations of spread through replicated experimental landscapes of favourable habitat. To quantify how population performance under stress differed between leading edge and founding populations, we combined individual tests of genotype performance under stress with knowledge of the genotype frequency changes that occurred over the replicate invasions. 3. After spreading through favourable environments, the average silique production of individuals exposed to drought or interspecific competition was lower in leading edge than founding populations. This change was driven by the evolution of lower intrinsic silique production, which was correlated with increased seed size, a trait that evolved as populations spread. The ability of plants to tolerate drought or interspecific competition, however, did not change markedly during spread. Heat tolerance did increase in leading edge populations, and this trait was associated with the evolution of taller plants during spread through favourable habitat. 4. Synthesis. We conclude that evolution during spread in favourable environments may affect the ability of populations to grow under stressful conditions as experienced in the expanded range, through changes in either intrinsic fecundity or stress tolerance. Thus, evolution during spread may constrain or extend the eventual range limit of non-native species invasions.

Many freshwater and marine algal species are described as having cosmopolitan distributions. Whether these widely distributed morphologically similar algae also share a similar gene pool remains often unclear. In the context of island biogeography theory, stronger spatial isolation deemed typical of freshwater lakes should restrict gene flow and lead to higher genetic differentiation among lakes. Using nine microsatellite loci, we investigate the genetic diversity of a widely distributed freshwater planktonic diatom, Asterionella formosa, across different lakes in Switzerland and the Netherlands. We applied a hierarchical spatial sampling design to determine the geographical scale at which populations are structured. A subset of the isolates was additionally analysed using amplified fragment length polymorphism (AFLP) markers. Our results revealed complex and unexpected population structure in A. formosa with evidence for both restricted and moderate to high gene flow at the same time. Different genetic markers (microsatellites and AFLPs) analysed with a variety of multivariate methods consistently revealed that genetic differentiation within lakes was much stronger than among lakes, indicating the presence of cryptic species within A. formosa. We conclude that the hidden diversity found in this study is expected to have implications for the further use of A. formosa in biogeographical, conservation and ecological studies. Further research using species-level phylogenetic markers is necessary to place the observed differentiation in an evolutionary context of speciation.