@article{SarmentoBondMidgleyetal.2011,
  author    = {Sarmento, Juliano Sarmento and Bond, William J. and Midgley, Guy F. and Rebelo, Anthony G. and Thuiller, Wilfried and Schurr, Frank Martin},
  title     = {Effects of harvesting flowers from shrubs on the persistence and abundance of wild shrub populations at multiple spatial extents},
  series = {Conservation biology : the journal of the Society for Conservation Biology},
  volume    = {25},
  journal   = {Conservation biology : the journal of the Society for Conservation Biology},
  number    = {1},
  publisher = {Wiley-Blackwell},
  address   = {Malden},
  issn      = {0888-8892},
  doi       = {10.1111/j.1523-1739.2010.01628.x},
  pages     = {73 -- 84},
  year      = {2011},
  abstract  = {Wildflower harvesting is an economically important activity of which the ecological effects are poorly understood. We assessed how harvesting of flowers affects shrub persistence and abundance at multiple spatial extents. To this end, we built a process-based model to examine the mean persistence and abundance of wild shrubs whose flowers are subject to harvest (serotinous Proteaceae in the South African Cape Floristic Region). First, we conducted a general sensitivity analysis of how harvesting affects persistence and abundance at nested spatial extents. For most spatial extents and combinations of demographic parameters, persistence and abundance of flowering shrubs decreased abruptly once harvesting rate exceeded a certain threshold. At larger extents, metapopulations supported higher harvesting rates before their persistence and abundance decreased, but persistence and abundance also decreased more abruptly due to harvesting than at smaller extents. This threshold rate of harvest varied with species' dispersal ability, maximum reproductive rate, adult mortality, probability of extirpation or local extinction, strength of Allee effects, and carrying capacity. Moreover, spatial extent interacted with Allee effects and probability of extirpation because both these demographic properties affected the response of local populations to harvesting more strongly than they affected the response of metapopulations. Subsequently, we simulated the effects of harvesting on three Cape Floristic Region Proteaceae species and found that these species reacted differently to harvesting, but their persistence and abundance decreased at low rates of harvest. Our estimates of harvesting rates at maximum sustainable yield differed from those of previous investigations, perhaps because researchers used different estimates of demographic parameters, models of population dynamics, and spatial extent than we did. Good demographic knowledge and careful identification of the spatial extent of interest increases confidence in assessments and monitoring of the effects of harvesting. Our general sensitivity analysis improved understanding of harvesting effects on metapopulation dynamics and allowed qualitative assessment of the probability of extirpation of poorly studied species.},
  language  = {en}
}
@article{SchurrBondMidgleyetal.2005,
  author    = {Schurr, Frank Martin and Bond, William J. and Midgley, Guy F. and Higgins, Steven I.},
  title     = {A mechanistic model for secondary seed dispersal by wind and its experimental validation},
  issn      = {0022-0477},
  year      = {2005},
  abstract  = {1 Secondary seed dispersal by wind, the wind-driven movement of seeds along the ground surface, is an important dispersal mechanism for plant species in a range of environments. 2 We formulate a mechanistic model that describes how secondary dispersal by wind is affected by seed traits, wind conditions and obstacles to seed movement. The model simulates the movement paths of individual seeds and can be fully specified using independently measured parameters. 3 We develop an explicit version of the model that uses a spatially explicit representation of obstacle patterns, and also an aggregated version that uses probability distributions to model seed retention at obstacles and seed movement between obstacles. The aggregated version is computationally efficient and therefore suited to large-scale simulations. It provides a very good approximation of the explicit version (R-2 > 0.99) if initial seed positions vary randomly relative to the obstacle pattern. 4 To validate the model, we conducted a field experiment in which we released seeds of seven South African Proteaceae species that differ in seed size and morphology into an arena in which we systematically varied obstacle patterns. When parameterized with maximum likelihood estimates obtained from independent measurements, the explicit model version explained 70-77\% of the observed variation in the proportion of seeds dispersed over 25 m and 67- 69\% of the observed variation in the direction of seed dispersal. 5 The model tended to underestimate dispersal rates, possibly due to the omission of turbulence from the model, although this could also be explained by imprecise estimation of one model parameter (the aerodynamic roughness length). 6 Our analysis of the aggregated model predicts a unimodal relationship between the distance of secondary dispersal by wind and seed size. The model can also be used to identify species with the potential for long-distance seed transport by secondary wind dispersal. 7 The validated model expands the domain of mechanistic dispersal models, contributes to a functional understanding of seed dispersal, and provides a tool for predicting the distances that seeds move},
  language  = {en}
}