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1.
Curr Biol ; 34(8): 1786-1793.e4, 2024 04 22.
Article in English | MEDLINE | ID: mdl-38614083

ABSTRACT

Soda lakes are some of the most productive aquatic ecosystems.1 Their alkaline-saline waters sustain unique phytoplankton communities2,3 and provide vital habitats for highly specialized biodiversity including invertebrates, endemic fish species, and Lesser Flamingos (Phoeniconaias minor).1,4 More than three-quarters of Lesser Flamingos inhabit the soda lakes of East Africa5; however, populations are in decline.6 Declines could be attributed to their highly specialized diet of cyanobacteria7 and dependence on a network of soda lake feeding habitats that are highly sensitive to climate fluctuations and catchment degradation.8,9,10,11,12 However, changing habitat availability has not been assessed due to a lack of in situ water quality and hydrology data and the irregular monitoring of these waterbodies.13 Here, we combine satellite Earth observations and Lesser Flamingo abundance observations to quantify spatial and temporal trends in productivity and ecosystem health over multiple decades at 22 soda lakes across East Africa. We found that Lesser Flamingo distributions are best explained by phytoplankton biomass, an indicator of food availability. However, timeseries analyses revealed significant declines in phytoplankton biomass from 1999 to 2022, most likely driven by substantial rises in lake water levels. Declining productivity has reduced the availability of healthy soda lake ecosystems, most notably in equatorial Kenya and northern Tanzania. Our results highlight the increasing vulnerability of Lesser Flamingos and other soda lake biodiversity in East Africa, particularly with increased rainfall predicted under climate change.14,15,16 Without improved lake monitoring and catchment management practices, soda lake ecosystems could be pushed beyond their environmental tolerances. VIDEO ABSTRACT.


Subject(s)
Lakes , Phytoplankton , Animals , Africa, Eastern , Biodiversity , Biomass , Climate Change , Ecosystem , Phytoplankton/physiology
2.
Ecology ; 94(5): 1123-30, 2013 May.
Article in English | MEDLINE | ID: mdl-23858652

ABSTRACT

Understanding how entire ecosystems maintain stability in the face of climatic and human disturbance is one of the most fundamental challenges in ecology. Theory suggests that a crucial factor determining the degree of ecosystem stability is simply the degree of synchrony with which different species in ecological food webs respond to environmental stochasticity. Ecosystems in which all food-web pathways are affected similarly by external disturbance should amplify variability in top carnivore abundance over time due to population interactions, whereas ecosystems in which a large fraction of pathways are nonresponsive or even inversely responsive to external disturbance will have more constant levels of abundance at upper trophic levels. To test the mechanism underlying this hypothesis, we used over half a century of demographic data for multiple species in the Serengeti (Tanzania) ecosystem to measure the degree of synchrony to variation imposed by an external environmental driver, the El Niño Southern Oscillation (ENSO). ENSO effects were mediated largely via changes in dry-season vs. wet-season rainfall and consequent changes in vegetation availability, propagating via bottom-up effects to higher levels of the Serengeti food web to influence herbivores, predators and parasites. Some species in the Serengeti food web responded to the influence of ENSO in opposite ways, whereas other species were insensitive to variation in ENSO. Although far from conclusive, our results suggest that a diffuse mixture of herbivore responses could help buffer top carnivores, such as Serengeti lions, from variability in climate. Future global climate changes that favor some pathways over others, however, could alter the effectiveness of such processes in the future.


Subject(s)
El Nino-Southern Oscillation , Food Chain , Predatory Behavior , Africa , Animals , Antelopes , Birds , Mammals , Plants , Population Dynamics , Rain , Seasons , Time Factors
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