Long Distance Seed Dispersal

Recent population and community models show that the entire distribution of dispersal distances, and not only mean dispersal distances, is critical for rates of range expansion, recruitment patterns, genetic structure, metapopulation dynamics, and ultimately for community diversity at different scales. The use of dispersal mechanistic models, and especially those that are spatially explicit, is a promising tool because it provides reliable predictions of standard (local dispersal) as well as nonstandard (long-distance dispersal, LDD) events of seed dispersal, which are especially difficult to capture in nature. For wind-dispersed species, for example, the processes that affect seed dispersal distance are either atmospheric (the spatial and temporal statistics of the wind velocity field (vertical, longitudinal, and

Figure 2 Fruits of Lycium intricatum (Solanaceae) that are secondarily dispersed by the predators (Falco tinnunculus) of the primary seed disperser, the lizard endemic to the Canary Islands, Galllotia atlantica. Photos courtesy of Manuel Nogales.

Time

Figure 3 An example of a mechanistic biotic model combining the data on retention time (left axis) with the net displacement movement considered as maximum dispersal distance from the origin (right axis). For a territorial disperser as in the given case, the maximum seed dispersal distance strongly depends on its home range or territoriality, but not on the retention time. From SantamarĂ­a L, Rodriguez-Perez J, Larrinaga AR, and Pias B (2007) Predicting spatial patterns of plant recruitment using animal-displacement kernels. PLOS ONE 2(10): e1008 doi:10.137/journal.pone.0001008.

Time

Figure 3 An example of a mechanistic biotic model combining the data on retention time (left axis) with the net displacement movement considered as maximum dispersal distance from the origin (right axis). For a territorial disperser as in the given case, the maximum seed dispersal distance strongly depends on its home range or territoriality, but not on the retention time. From SantamarĂ­a L, Rodriguez-Perez J, Larrinaga AR, and Pias B (2007) Predicting spatial patterns of plant recruitment using animal-displacement kernels. PLOS ONE 2(10): e1008 doi:10.137/journal.pone.0001008.

latitudinal), their covariance structure, and their integral timescale properties) or biological (terminal velocity of the dispersal unit, release height, and timing of release) factors. The scale at which the dispersal model makes predictions will influence which factors are included in the model. Knowledge of the average wind velocities appears to be sufficient to predict local dispersal. However, in order to predict LDD, additional information on updrafts and strong gusts is needed. By contrast, seed dispersal distance in endozoochorous species is mostly a function of seed retention time in the frugi-vore's digestive tract and of frugivore's movement patterns (home range, habitat use; Figure 3). These mechanistic models are predicting that dispersal could be up to 2 orders of magnitude higher than those previously obtained by empirical methods.

Renewable Energy 101

Renewable Energy 101

Renewable energy is energy that is generated from sunlight, rain, tides, geothermal heat and wind. These sources are naturally and constantly replenished, which is why they are deemed as renewable. The usage of renewable energy sources is very important when considering the sustainability of the existing energy usage of the world. While there is currently an abundance of non-renewable energy sources, such as nuclear fuels, these energy sources are depleting. In addition to being a non-renewable supply, the non-renewable energy sources release emissions into the air, which has an adverse effect on the environment.

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