Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Towards better prediction of seed dispersal by animals

Towards better prediction of seed dispersal by animals Summary 1. Animals are the most important dispersal vector for a vast number of terrestrial plants. 2. Whereas our understanding of dispersal by wind has taken enormous conceptual strides in recent years, our ability to predict dispersal patterns by animals remains crude by comparison despite the large volume of research on this topic. 3. We review published models of dispersal by animals, discuss component processes that have been modelled in other contexts and indicate approaches that could usefully be taken in the future. 4. The current problem restricting progress is that few animal dispersal models include the processes most likely to cause dispersal to change over time or space. Thus, they are context‐specific, predicting only under the conditions from which data were collected. 5. The key to deeper understanding is to ask ‘What determines the behaviour of the vector; and what determines the influence of the vector on the trajectory of a seed’ (rather than what is the behaviour of the vector in a case study and how far does it move seeds in that study). This leads naturally to process‐based models with the ability to predict over a range of scenarios. 6. Priority processes for a more mechanistic approach include: animal movement within a landscape; linking gut throughput, defecation, feeding rate and other behaviours; and detachment from an animal’s surface. Models of gut throughput are probably already sufficient to include them in a dispersal model. Perhaps the best way to achieve progress would be to initiate an international working group of experts in modelling and the particular biological processes. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Functional Ecology Wiley

Towards better prediction of seed dispersal by animals

Download PDF
8 pages

Loading next page...
 
/lp/wiley/towards-better-prediction-of-seed-dispersal-by-animals-zG77uxn0qz

References (54)

Publisher
Wiley
Copyright
© 2010 The Authors. Journal compilation © 2010 British Ecological Society
ISSN
0269-8463
eISSN
1365-2435
DOI
10.1111/j.1365-2435.2010.01747.x
Publisher site
See Article on Publisher Site

Abstract

Summary 1. Animals are the most important dispersal vector for a vast number of terrestrial plants. 2. Whereas our understanding of dispersal by wind has taken enormous conceptual strides in recent years, our ability to predict dispersal patterns by animals remains crude by comparison despite the large volume of research on this topic. 3. We review published models of dispersal by animals, discuss component processes that have been modelled in other contexts and indicate approaches that could usefully be taken in the future. 4. The current problem restricting progress is that few animal dispersal models include the processes most likely to cause dispersal to change over time or space. Thus, they are context‐specific, predicting only under the conditions from which data were collected. 5. The key to deeper understanding is to ask ‘What determines the behaviour of the vector; and what determines the influence of the vector on the trajectory of a seed’ (rather than what is the behaviour of the vector in a case study and how far does it move seeds in that study). This leads naturally to process‐based models with the ability to predict over a range of scenarios. 6. Priority processes for a more mechanistic approach include: animal movement within a landscape; linking gut throughput, defecation, feeding rate and other behaviours; and detachment from an animal’s surface. Models of gut throughput are probably already sufficient to include them in a dispersal model. Perhaps the best way to achieve progress would be to initiate an international working group of experts in modelling and the particular biological processes.

Journal

Functional Ecology – Wiley

Published: Dec 1, 2010

There are no references for this article.