@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} } @article{ThuillerAlbertAraujoetal.2008, author = {Thuiller, Wilfried and Albert, C{\´e}cile H. and Ara{\´u}jo, Miguel B. and Berry, Pam M. and Cabeza, Mar and Guisan, Antoine and Hickler, Thomas and Midgley, Guy F. and Paterson, James and Schurr, Frank Martin and Sykes, Martin T. and Zimmermann, Niklaus E.}, title = {Predicting global change impacts on plant species' distributions : future challenges}, issn = {1433-8319}, doi = {10.1016/j.ppees.2007.09.004}, year = {2008}, language = {en} } @article{YatesElithLatimeretal.2010, author = {Yates, Colin J. and Elith, Jane and Latimer, Andrew M. and Le Maitre, David and Midgley, Guy F. and Schurr, Frank Martin and West, Adam G.}, title = {Projecting climate change impacts on species distributions in megadiverse South African Cape and Southwest Australian Floristic Regions : Opportunities and challenges}, issn = {1442-9985}, doi = {10.1111/j.1442-9993.2009.02044.x}, year = {2010}, abstract = {Increasing evidence shows that anthropogenic climate change is affecting biodiversity. Reducing or stabilizing greenhouse gas emissions may slow global warming, but past emissions will continue to contribute to further unavoidable warming for more than a century. With obvious signs of difficulties in achieving effective mitigation worldwide in the short term at least, sound scientific predictions of future impacts on biodiversity will be required to guide conservation planning and adaptation. This is especially true in Mediterranean type ecosystems that are projected to be among the most significantly affected by anthropogenic climate change, and show the highest levels of confidence in rainfall projections. Multiple methods are available for projecting the consequences of climate change on the main unit of interest - the species - with each method having strengths and weaknesses. Species distribution models (SDMs) are increasingly applied for forecasting climate change impacts on species geographic ranges. Aggregation of models for different species allows inferences of impacts on biodiversity, though excluding the effects of species interactions. The modelling approach is based on several further assumptions and projections and should be treated cautiously. In the absence of comparable approaches that address large numbers of species, SDMs remain valuable in estimating the vulnerability of species. In this review we discuss the application of SDMs in predicting the impacts of climate change on biodiversity with special reference to the species-rich South West Australian Floristic Region and South African Cape Floristic Region. We discuss the advantages and challenges in applying SDMs in biodiverse regions with high levels of endemicity, and how a similar biogeographical history in both regions may assist us in understanding their vulnerability to climate change. We suggest how the process of predicting the impacts of climate change on biodiversity with SDMs can be improved and emphasize the role of field monitoring and experiments in validating the predictions of SDMs.}, language = {en} } @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{SarmentoJeltschThuilleretal.2013, author = {Sarmento, Juliano Sarmento and Jeltsch, Florian and Thuiller, Wilfried and Higgins, Steven and Midgley, Guy F. and Rebelo, Anthony G. and Rouget, Mathieu and Schurr, Frank Martin}, title = {Impacts of past habitat loss and future climate change on the range dynamics of South African Proteaceae}, series = {Diversity \& distributions : a journal of biological invasions and biodiversity}, volume = {19}, journal = {Diversity \& distributions : a journal of biological invasions and biodiversity}, number = {4}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {1366-9516}, doi = {10.1111/ddi.12011}, pages = {363 -- 376}, year = {2013}, abstract = {Aim To assess how habitat loss and climate change interact in affecting the range dynamics of species and to quantify how predicted range dynamics depend on demographic properties of species and the severity of environmental change. Location South African Cape Floristic Region. Methods We use data-driven demographic models to assess the impacts of past habitat loss and future climate change on range size, range filing and abundances of eight species of woody plants (Proteaceae). The species-specific models employ a hybrid approach that simulates population dynamics and long-distance dispersal on top of expected spatio-temporal dynamics of suitable habitat. Results Climate change was mainly predicted to reduce range size and range filling (because of a combination of strong habitat shifts with low migration ability). In contrast, habitat loss mostly decreased mean local abundance. For most species and response measures, the combination of habitat loss and climate change had the most severe effect. Yet, this combined effect was mostly smaller than expected from adding or multiplying effects of the individual environmental drivers. This seems to be because climate change shifts suitable habitats to regions less affected by habitat loss. Interspecific variation in range size responses depended mostly on the severity of environmental change, whereas responses in range filling and local abundance depended mostly on demographic properties of species. While most surviving populations concentrated in areas that remain climatically suitable, refugia for multiple species were overestimated by simply overlying habitat models and ignoring demography. Main conclusions Demographic models of range dynamics can simultaneously predict the response of range size, abundance and range filling to multiple drivers of environmental change. Demographic knowledge is particularly needed to predict abundance responses and to identify areas that can serve as biodiversity refugia under climate change. These findings highlight the need for data-driven, demographic assessments in conservation biogeography.}, language = {en} }