TY  - JOUR
A1  - Eugenia Tietze, Hedwig Selma
A1  - Joshi, Jasmin Radha
A1  - Pugnaire, Francisco Ignacio
A1  - Dechoum, Michele de Sa
T1  - Seed germination and seedling establishment of an invasive tropical tree species under different climate change scenarios
JF  - Austral ecology
N2  - Increasing air temperature and atmospheric CO2 levels may affect the distribution of invasive species. Whereas there is wide knowledge on the effect of global change on temperate species, responses of tropical invasive species to these two global change drivers are largely unknown. We conducted a greenhouse experiment on Terminalia catappa L. (Combretaceae), an invasive tree species on Brazilian coastal areas, to evaluate the effects of increased air temperature and CO2 concentration on seed germination and seedling growth on the island of Santa Catarina (Florianopolis, Brazil). Seeds of the invasive tree were subjected to two temperature levels (ambient and +1.6 degrees C) and two CO2 levels (ambient and 650 ppmv) with a factorial design. Increased temperature enhanced germination rate and shortened germination time of T. catappa seeds. It also increased plant height, number of leaves and above-ground biomass. By contrast, increased atmospheric CO2 concentration had no significant effects, and the interaction between temperature and CO2 concentration did not affect any of the measured traits. Terminalia catappa adapts to a relatively broad range of environmental conditions, being able to tolerate cooler temperatures in its invasive range. As T. catappa is native to tropical areas, global warming might favour its establishment along the coast of subtropical South America, while increased CO2 levels seem not to have significant effects on seed germination or seedling growth.
KW  - CO2 concentration
KW  - coastal dunes
KW  - establishment
KW  - invasive plant
KW  - plant invasion
KW  - temperature
KW  - Terminalia catappa
Y1  - 2019
U6  - https://doi.org/10.1111/aec.12809
SN  - 1442-9985
SN  - 1442-9993
VL  - 44
IS  - 8
SP  - 1351
EP  - 1358
PB  - Wiley
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  -