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Register a new userFirst assessment of the plant phenology index (PPI) for estimating gross primary productivity in African semi-arid ecosystems
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The importance of semi-arid ecosystems in the global carbon cycle as sinks for CO2 emissions has recently been highlighted. Africa is a carbon sink and nearly half its area comprises arid and semi-arid ecosystems. However, there are uncertainties regarding CO2 fluxes for semi-arid ecosystems in Africa, particularly savannas and dry tropical woodlands. In order to improve on existing remote-sensing based methods for estimating carbon uptake across semi-arid Africa we applied and tested the recently developed plant phenology index (PPI). We developed a PPI-based model estimating gross primary productivity (GPP) that accounts for canopy water stress, and compared it against three other Earth observation-based GPP models: the temperature and greenness model, the greenness and radiation model and a light use efficiency model. The models were evaluated against in situ data from four semi-arid sites in Africa with varying tree canopy cover (3 to 65 percent). Evaluation results from the four GPP models showed reasonable agreement with in situ GPP measured from eddy covariance flux towers (EC GPP) based on coefficient of variation, root-mean-square error, and Bayesian information criterion. The PPI-based GPP model was able to capture the magnitude of EC GPP better than the other tested models. The results of this study show that a PPI-based GPP model is a promising tool for the estimation of GPP in the semi-arid ecosystems of Africa.
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Pristine coastal shallow systems are usually dominated by extensive meadows of seagrass species, which are assumed to take advantage of nutrient supply from sediment. An increasing nutrient input is thought to favour phytoplankton, epiphytic microalgae, as well as opportunistic ephemeral macroalgae that coexist with seagrasses. The primary cause of shifts and succession in the macrophyte community is the increase of nutrient load to water; however temperature plays also an important role. A competition model between rooted seagrass (Zostera marina), macroalgae (Ulva sp), and phytoplankton has been developed to analyse the succession of primary producer communities in these systems. Successions of dominance states, with different resilience characteristics, are found when modifying the input of nutrients and the seasonal temperature and light intensity forcing.
African Swine Fever (ASF) is viral infection which causes acute disease in domestic pigs and wild boar. Although the virus does not cause disease in humans, the impact it has on the economy, especially through trade and farming, is substantial. Recent rapid propagation of the (ASF) from East to West of Europe encouraged us to prepare risk assessment for Poland. The early growth estimation can be easily done by matching incidence trajectory to the exponential function, resulting in the approximation of the force of infection. With these calculations the basic reproduction rate of the epidemic, the effective outbreaks detection and elimination times could be estimated. In regression mode, 380 Polish counties (poviats) have been analysed, where 18 (located in Northeast Poland) have been affected (until August 2017) for spatial propagation (risk assessment for future). Mathematical model has been applied by taking into account: swine amount significance, disease vectors (wild boards) significance. We use pseudogravitational models of short and longrange interactions referring to the socio-migratory behavior of wild boars and the pork production chain significance. Spatial modeling in a certain range of parameters proves the existence of a natural protective barrier within boarders of the Congress Poland. The spread of the disease to the Greater Poland should result in the accelerated outbreak of ASF production chain. In the preliminary setup, we perform regression analysis, network outbreak investigation, early epidemic growth estimation and simulate landscape-based propagation.
Mountain ecosystems are sensitive indicators of climate change. Long-term studies may be extremely useful in assessing the responses of high-elevation ecosystems to climate change and other anthropogenic drivers. Mountain research sites within the LTER (Long-Term Ecosystem Research) network are representative of various types of ecosystems and span a wide bioclimatic and elevational range. Here, we present a synthesis and a review of the main results from long-term ecological studies in mountain ecosystems at 20 LTER sites in Italy, Switzerland and Austria. We analyzed a set of key climate parameters, such as temperature and snow cover duration, in relation to vascular species composition, plant traits, abundance patterns, pedoclimate, nutrient dynamics in soils and water, phenology and composition of freshwater biota. The overall results highlight the rapid response of mountain ecosystems to climate change. As temperatures increased, vegetation cover in alpine and subalpine summits increased as well. Years with limited snow cover duration caused an increase in soil temperature and microbial biomass during the growing season. Effects on freshwater ecosystems were observed, in terms of increases in solutes, decreases in nitrates and changes in plankton phenology and benthos communities. This work highlights the importance of comparing and integrating long-term ecological data collected in different ecosystems, for a more comprehensive overview of the ecological effects of climate change. Nevertheless, there is a need for i) adopting co-located monitoring site networks to improve our ability to obtain sound results from cross-site analysis, ii) carrying out further studies, with fine spatial and temporal resolutions to improve understanding of responses to extreme events, and iii) increasing comparability and standardizing protocols across networks to clarify local from global patterns.
Forty years ago, Robert May questioned a central belief in ecology by proving that sufficiently large or complex ecological networks have probability of persisting close to zero. To prove this point, he analyzed large networks in which species interact at random. However, in natural systems pairs of species have well-defined interactions (e.g., predator-prey, mutualistic or competitive). Here we extend Mays results to these relationships and find remarkable differences between predator-prey interactions, which increase stability, and mutualistic and competitive, which are destabilizing. We provide analytic stability criteria for all cases. These results have broad applicability in ecology. For example, we show that, surprisingly, the probability of stability for predator-prey networks is decreased when we impose realistic food web structure or we introduce a large preponderance of weak interactions. Similarly, stability is negatively impacted by nestedness in bipartite mutualistic networks.
Explaining biodiversity in nature is a fundamental problem in ecology. An outstanding challenge is embodied in the so-called Competitive Exclusion Principle: two species competing for one limiting resource cannot coexist at constant population densities, or more generally, the number of consumer species in steady coexistence cannot exceed that of resources. The fact that competitive exclusion is rarely observed in natural ecosystems has not been fully understood. Here we show that by forming chasing triplets among the consumers and resources in the consumption process, the Competitive Exclusion Principle can be naturally violated. The modeling framework developed here is broadly applicable and can be used to explain the biodiversity of many consumer-resource ecosystems and hence deepens our understanding of biodiversity in nature.
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