Plant Strategies

Product information

€60.89

Stock: In Stock Online

Our USPs

free delivery icon
Free Delivery
Extended Range: Delivery 3-4 working days
dubray rewards icon
Dubray Rewards
Earn 244 Reward Points on this title

Plant Strategies

Product information

Author: Daniel C. Laughlin

Type: Paperback

ISBN: 9780192867957

Date: 27th July, 2023

Publisher: Oxford University Press

  1. Categories

  2. Botany
  3. Conservation
  4. Applied Ecology

Description

How do plants make a living? Some plants are gamblers, others are swindlers. Some plants are habitual spenders while others are strugglers and miserly savers. Plants have evolved a spectacular array of solutions to the existential problems of survival and reproduction in a world where resources are scarce, disturbances can be deadly, and competition is cut-throat. Few topics have both captured the imagination and furrowed the brows of plant ecologists, yet no topic is more important for understanding the assembly of plant communities, predicting plant responses to global change, and enhancing the restoration of our rapidly degrading biosphere. The vast array of plant strategy models that characterize the discipline now require synthesis. These models tend to emphasize either life history strategies based on demography, or functional strategies based on ecophysiology. Indeed, this disciplinary divide between demography and physiology runs deep and continues to this today. The goal of this accessible book is to articulate a coherent framework that unifies life history theory with comparative functional ecology to advance prediction in plant ecology. Armed with a deeper understanding of the dimensionality of life history and functional traits, we are now equipped to quantitively link phenotypes to population growth rates across gradients of resource availability and disturbance regimes. Predicting how species respond to global change is perhaps the most important challenge of our time. A robust framework for plant strategy theory will advance this research agenda by testing the generality of traits for predicting population dynamics.

Additional details