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Background: Antananarivo, the capital city of Madagascar, is experiencing a steady increase in population growth. Due to the abundance of mosquito vectors in this locality, the population exposed to mosquito-borne diseases is therefore also increasing, as is the risk of epidemic episodes. The aim of the present study was to assess, in a resource-limited setting, the information on mosquito population dynamics and disease transmission risk that can be provided through a longitudinal entomological study carried out in a multi-host single site. Methods: Mosquitoes were collected every 15 days over 16 months (from January 2017 to April 2018) using six CDC-light traps in a peri-urban area of Antananarivo. Multivariable generalised linear models were developed using indoor and outdoor densities of the predominant mosquito species as response variables and moon illumination, environmental data and climatic data as the explanatory variables. Results: Overall, 46,737 mosquitoes belonging to at least 20 species were collected, of which Culex antennatus (68.9%), Culex quinquefasciatus (19.8%), Culex poicilipes (3.7%) and Anopheles gambiae sensu lato (2.3%) were the most abundant species. Mosquito densities were observed to be driven by moon illumination and climatic factors interacting at different lag periods. The outdoor models demonstrated biweekly and seasonal patterns of mosquito densities, while the indoor models demonstrated only a seasonal pattern. Conclusions: An important diversity of mosquitoes exists in the peri-urban area of Antananarivo. Some well-known vector species, such as Cx. antennatus, a major vector of West Nile virus (WNV) and Rift-Valley fever virus (RVFV), Cx. quinquefasciatus, a major vector of WNV, Cx. poicilipes, a candidate vector of RVFV and An. gambiae sensu lato, a major vector of Plasmodium spp., are abundant. Importantly, these four mosquito species are present all year round, even though their abundance declines during the cold dry season, wi

Theobroma cacao, a tropical cauliflorous fruit tree, typically produces flowers and fruits twice a year and exhibits alternate harvesting patterns over consecutive six-monthly seasons in regions with bi-modal rainfall distribution, such as Côte d'Ivoire. This study investigated seasonal variations in flowering and fruiting among trees in populations of mixed cacao genotypes. The intensities of crown and trunk flowering and pod production were monitored for eight consecutive six-monthly seasons on 114 adult cacao trees grown from seedlings. We investigated distributions of seasonal flowering and fruiting values, relationships between flowering and pod production, and the effects of seasonal cumulative rainfall. The patterns of seasonal flowering and fruiting series were analyzed using two descriptors: the first distinguishing between regular and variable patterns, and the second analyzing the structure of such variability, classifying it as either irregular or alternating. Despite being subjected to similar climate and agronomic management, individual trees exhibited highly variable flowering and fruiting behaviors within each season, as well as variable patterns of flowering and fruiting across seasons. Seasonal alternate fruiting on a population scale masked highly variable patterns among trees, and only 19 % of the trees exhibited marked alternate fruiting patterns. Variations in pod production on a tree scale were mainly related to variations in trunk flowering. Endogenous factors seemed to control seasonal variations in flowering and fruiting, even though exogenous factors, both climatic and agronomic, could structure flowering and fruiting patterns at the orchard scale.

Wood density is a fundamental property related to tree biomechanics and hydraulic function while playing a crucial role in assessing vegetation carbon stocks by linking volumetric retrieval and a mass estimate. This study provides a high-resolution map of the global distribution of tree wood density at the 0.01° (~1 km) spatial resolution, derived from four decision trees machine learning models using a global database of 28,822 tree-level wood density measurements. An ensemble of four top-performing models combined with eight cross-validation strategies shows great consistency, providing wood density patterns with pronounced spatial heterogeneity. The global pattern shows lower wood density values in northern and northwestern Europe, Canadian forest regions and slightly higher values in Siberia forests, western United States, and southern China. In contrast, tropical regions, especially wet tropical areas, exhibit high wood density. Climatic predictors explain 49%–63% of spatial variations, followed by vegetation characteristics (25%–31%) and edaphic properties (11%–16%). Notably, leaf type (evergreen vs. deciduous) and leaf habit type (broadleaved vs. needleleaved) are the most dominant individual features among all selected predictive covariates. Wood density tends to be higher for angiosperm broadleaf trees compared to gymnosperm needleleaf trees, particularly for evergreen species. The distributions of wood density categorized by leaf types and leaf habit types have good agreement with the features observed in wood density measurements. This global map quantifying wood density distribution can help improve accurate predictions of forest carbon stocks, providing deeper insights into ecosystem functioning and carbon cycling such as forest vulnerability to hydraulic and thermal stresses in the context of future climate change.

Short-term adaptive evolution represents one of the primary mechanisms allowing species to persist in the face of global change. Predicting the adaptive response at the species level requires reliable estimates of the evolutionary potential of traits involved in adaptive responses, as well as understanding how evolutionary potential varies across a species' range. Theory suggests that spatial variation in the fitness landscape due to environmental variation will directly impact the evolutionary potential of traits. However, empirical evidence on the link between environmental variation and evolutionary potential across a species range in the wild is lacking. In this study, we estimate multivariate evolutionary potential (via the genetic variance–covariance matrix, or G-matrix) for six morphological and life history traits in 10 wild populations of great tits (Parus major) distributed across Europe. The G-matrix significantly varies in size, shape, and orientation across populations for both types of traits. For life history traits, the differences in G-matrix are larger when populations are more distant in their climatic niche. This suggests that local climates contribute to shaping the evolutionary potential of phenotypic traits that are strongly related to fitness. However, we found no difference in the overall evolutionary potential (i.e., G-matrix size) between populations closer to the core or the edge of the distribution area. This large-scale comparison of G-matrices across wild populations emphasizes that integrating variation in multivariate evolutionary potential is important to understand and predict species' adaptive responses to new selective pressures.

For countries' emission-reduction efforts under the Paris Agreement to be effective, baseline emission/removals levels and reporting must be as transparent and accurate as possible. For Indonesia, which holds among the largest area of tropical peatlands and mangrove forest in the world, it is particularly important for these high-carbon ecosystems to produce high-accuracy greenhouse gas inventory and to improve national forest reference emissions level/forest reference level. Here, we highlight the opportunity for refining greenhouse gas emission factors (EF) of peatlands and mangroves and describe scientific challenges to support climate policy processes in Indonesia, where 55 to 59% of national emission reduction targets by 2030 depend on mitigation in Forestry and Other Land Use. Based on the stock-difference and flux change approaches, we examine higher-tier EF for drained and rewetted peatland, peatland fires, mangrove conversions, and mangrove on peatland to improve future greenhouse gas flux reporting in Indonesia. We suggest that these refinements will be essential to support Indonesia in achieving Forest and Other Land Use net sink by 2030 and net zero emissions targets by 2060 or earlier.