Single-and multi-brooded species of birds differ in their seasonal patterns of clutch size. Single-brooded species start with a maximum clutch size that declines continuously as the season progresses, whereas the clutch sizes of multi-brooded species usually increase to a mid-season maximum peak and then decrease progressively until the end of the breeding season. Previous studies have shown that multi-brooded migrant species present seasonal patterns that are similar to single-brooded species at high latitudes but similar to multibrooded non-migratory species at lower latitudes. We studied the Greenfinch Carduelis chloris and Goldfinch C. carduelis populations in eastern Spain (Sagunto, Valencia) between 1975 and to compare seasonal variations in clutch sizes between years with early and late starts to the breeding season. The period over which clutch sizes increase was longer when the breeding season started earlier. The Goldfinch population showed no pattern of initial increase in clutch size when there was a late start to the breeding season: a late start shortens the season giving them less time to breed, and may also coincide with maximum food availability. Thus, the pattern of single-brooded species was observed. In the Greenfinch population, a trend toward the seasonal pattern of single-brooded species was also observed when the following indices were compared: clutch size increase, modal timing, initial slope and timing of maximum clutch size. We have also compared the seasonal patterns of clutch size of both species in eastern Spain with the patterns observed in Britain. Our results show that for both Goldfinches and Greenfinches, the non-migrant southern populations of Sagunto in eastern Spain do not tend towards a more multi-brooded seasonal pattern of clutch size than the migrant Goldfinches of Britain.Passerine birds use two tactics to maximize their number of offspring per breeding season. In singlebrooded species, the clutch size is at its maximum at the start of the breeding season and declines as the season progresses. Multi-brooded species, however, present a different pattern. Their breeding season is longer and females lay several clutches with a midseason peak of clutch size (Lack 1954, Klomp 1970. Therefore, in multi-brooded species, seasonal reproductive success is determined not only by the productivity of each brood but also by the number of broods raised (Bryant 1979, Desrochers & Magrath 1993. As a consequence, Crick et al. (1993) suggested that multi-brooded species start laying before the optimal moment, which accounts for the increase in clutch sizes until the optimal time, when clutch sizes are the largest. In addition, a tendency to start breeding earlier would be normal as it would allow a greater number of clutches per season. Furthermore, the same species might present both patterns across its distribution range if it is made up of non-migrant as well as migrant populations. The Eastern Bluebird Sialia sialis shows an initial increase in the size of clutches in the southern p...
<p>On average, more than 21 million forced human displacements were reported as result of weather-related events between 2008 and 2020 worldwide. This is a major concern due to the increment trend in intensity and frequency of weather hazards. Breaking down the figures, the impact is more severe in low-middle income countries, where most of the natural hazards take place and adaptation strategies are lacking. Implementing efficient and operational policy responses requires a quantitative analysis of the nexus between climate-induced displacement. So far the study of this phenomenon has been often limited to qualitative assessments or to correlation measures from regression linear models, not accounting for the inherent complexity of the problem. The multicausal nature of human mobility and data availability present significant research challenges. We apply two methodological approaches that use machine-learning to close these gaps, namely addressing both rapid-onset (e.g. floods) and slow-onset (e.g. droughts) disaster types. The former uses the Internal Displacement Monitoring Centre (IDMC) global database of displacements triggered by floods and storms at disaster level, socioeconomic (RWI Meta Data4Good, Global Human Modification Layer, Education Expenditure), and Earth-Observation variables: meteorological (CHIRPS, ERA5) and environmental (NASA ASTER SRTM DEM, MODIS NDVI vegetation index). Explainable AI techniques enable to open the black box of random forest models and were applied at the global scale: Shapley values are used to investigate the contributions of the main drivers thereby quantitatively addressing the climate-displacement nexus. Results are consistent with the hazard, exposure and vulnerability concept discussed in literature and findings reveal that socioeconomic factors greatly mediate displacement magnitudes. The slow-onset study is being explored at the local scale at district level, currently focused on the effects of droughts on displaced populations in Somalia using UNCHR PRMN displacement dataset, remote sensing variables (CHIRPS, MODIS LST), conflict (ACLED) and market prices time-series (FSNAU, WFP VAM Unit). Beyond correlations analysis, causation alongside time-lag effects for the drivers of drought-induced displacement are assessed using the PCMCI algorithm. Results in specific districts indicate that decreases in vegetation in conjunction with cattle price drops are driving drought displacement, revealing these factors are in need for targeted intervention. Albeit the same method applied to other districts in Somalia returns no causal link among considered variables, taking these findings into account, we are able to propose district-wise recommendations on how to improve the quality of the data: eg. field data collection guidelines, what other data input is required, and where sampling efforts should be directed.&#160;</p>
<p>Compound heat waves and drought events draw our particular attention as they become more frequent. Co-occurring extreme events often exacerbate impacts on ecosystems and can induce a cascade of detrimental consequences. However, the research to understand these events is still in its infancy. DeepExtremes is a project funded by the European Space Agency (https://rsc4earth.de/project/deepextremes/) aiming at using deep learning to gain insight into Earth surface under extreme climate conditions. Specifically, the goal is to forecast and explain extreme, multi-hazard, and compound events. To this end, the project leverages the existing Earth observation archive to help us better understand and represent different types of hazards and their effects on society and vegetation. The project implementation involves a multi-stage process consisting of 1) global event detection; 2) intelligent subsampling and creation of mini-data-cubes; 3) forecasting methods development, interpretation, and testing; and 4) cloud deployment and upscaling. The data products will be made available to the community following the reproducibility and FAIR data principles. By effectively combining Earth system science with explainable AI, the project contributes knowledge to advancing the sustainable management of consequences of extreme events. This presentation will show the progress made so far and specifically introduce how to participate in the challenges about spatio-temporal extreme event prediction in DeepExtremes.</p>
This thesis is based on the following chapters, which are referred to in the text by their numerals: 1. Development of a dynamic growth model for Pinus radiata D. Don plantations in El Bierzo 2. Silvicultural alternatives for Pinus radiata D. Don plantations: applications of the growth model developed in El Bierzo 3. Influence of ecological parameters on site index of Pinus radiata D. Don plantations in El Bierzo 4. Aboveground biomass modelling and carbon pools estimation of Pinus radiata D. Don stands combining inventory and RS data in Northwestern Spain 5. Management guidelines for Pinus radiata D. Don stands in El Bierzo: general review of radiata pine experiences and practical analysis of a Nelder trial in New Zealand
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.