TY  - JOUR
A1  - Sarmento, Juliano Sarmento
A1  - Jeltsch, Florian
A1  - Thuiller, Wilfried
A1  - Higgins, Steven
A1  - Midgley, Guy F.
A1  - Rebelo, Anthony G.
A1  - Rouget, Mathieu
A1  - Schurr, Frank Martin
T1  - Impacts of past habitat loss and future climate change on the range dynamics of South African Proteaceae
JF  - Diversity & distributions : a journal of biological invasions and biodiversity
N2  - 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.
KW  - biodiversity refugia
KW  - CFR Proteaceae
KW  - climate change
KW  - demographic properties
KW  - habitat loss
KW  - local abundances
KW  - process-based range models
KW  - range filling
KW  - range size
KW  - species distribution models
Y1  - 2013
U6  - https://doi.org/10.1111/ddi.12011
SN  - 1366-9516
VL  - 19
IS  - 4
SP  - 363
EP  - 376
PB  - Wiley-Blackwell
CY  - Hoboken
ER  - 
TY  - JOUR
A1  - Marion, Glenn
A1  - McInerny, Greg J.
A1  - Pagel, Jörn
A1  - Catterall, Stephen
A1  - Cook, Alex R.
A1  - Hartig, Florian
A1  - O&rsquo, 
A1  - Hara, Robert B.
T1  - Parameter and uncertainty estimation for process-oriented population and
 distribution models: data, statistics and the niche
JF  - JOURNAL OF BIOGEOGRAPHY
N2  - 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.
KW  - Bayesian inference
KW  - demography
KW  - dispersal
KW  - dynamic models
KW  - dynamic range models
KW  - establishment
KW  - global change
KW  - niche models
KW  - species distribution models
Y1  - 2012
U6  - https://doi.org/10.1111/j.1365-2699.2012.02772.x
SN  - 0305-0270
SN  - 1365-2699
VL  - 39
IS  - 12
SP  - 2225
EP  - 2239
PB  - WILEY-BLACKWELL
CY  - HOBOKEN
ER  -