@article{MarionMcInernyPageletal.2012,
  author    = {Marion, Glenn and McInerny, Greg J. and Pagel, J{\"o}rn and Catterall, Stephen and Cook, Alex R. and Hartig, Florian and O\&rsquo, and Hara, Robert B.},
  title     = {Parameter and uncertainty estimation for process-oriented population and distribution models: data, statistics and the niche},
  series = {JOURNAL OF BIOGEOGRAPHY},
  volume    = {39},
  journal   = {JOURNAL OF BIOGEOGRAPHY},
  number    = {12},
  publisher = {WILEY-BLACKWELL},
  address   = {HOBOKEN},
  issn      = {0305-0270},
  doi       = {10.1111/j.1365-2699.2012.02772.x},
  pages     = {2225 -- 2239},
  year      = {2012},
  abstract  = {The spatial distribution of a species is determined by dynamic processes such as reproduction, mortality and dispersal. Conventional static species distribution models (SDMs) do not incorporate these processes explicitly. This limits their applicability, particularly for non-equilibrium situations such as invasions or climate change. In this paper we show how dynamic SDMs can be formulated and fitted to data within a Bayesian framework. Our focus is on discrete state-space Markov process models which provide a flexible framework to account for stochasticity in key demographic processes, including dispersal, growth and competition. We show how to construct likelihood functions for such models (both discrete and continuous time versions) and how these can be combined with suitable observation models to conduct Bayesian parameter inference using computational techniques such as Markov chain Monte Carlo. We illustrate the current state-of-the-art with three contrasting examples using both simulated and empirical data. The use of simulated data allows the robustness of the methods to be tested with respect to deficiencies in both data and model. These examples show how mechanistic understanding of the processes that determine distribution and abundance can be combined with different sources of information at a range of spatial and temporal scales. Application of such techniques will enable more reliable inference and projections, e.g. under future climate change scenarios than is possible with purely correlative approaches. Conversely, confronting such process-oriented niche models with abundance and distribution data will test current understanding and may ultimately feedback to improve underlying ecological theory.},
  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}
}