Is cellular automata algorithm able to predict the future dynamical shifts of tree species in Italy under climate change scenarios? A methodological approach

Interactions between growth, demography and biotic interactions in determining species range limits in a warming world: The case of Pinus sylvestris

“…This conclusion is in concordance with the results of several modelling studies, which predict a strong reduction of the distribution of conditions suitable for Scots pine in Spain by 2100 with only some remnant populations persisting in the northern mountain ranges (Benito Garzón et al, 2008a,b;Keenan et al, 2011). Similarly to the Iberian Peninsula, a strong reduction in area is expected in Italy, where Scots pine is only expected to survive in mountain regions of the Alps Di Traglia et al, 2011). However, even in many areas of the Alps a reduction in growth and survival has also been predicted under future simulated conditions due to drought stress (Richter et al, 2012).…”

Habitat restoration will help some functional plant types persist under climate change in fragmented landscapes

“…Several studies have used a cellular automata approach that represents population dynamics within a cell in a relatively abstract way (e.g. Iverson et al ., ; Engler & Guisan, ; Di Traglia et al ., ) to focus on questions related to dispersal and rate of climate change, while our approach based on a more detailed age‐structured density‐based population model allowed us to achieve our aim of examining the role of functional and life cycle plant traits. Other approaches combine detailed age‐structured population models with detailed and specific landscape models and SDMs (Keith et al ., ; Midgley et al ., ), which can provide realistic predictions for particular cases and help understand the role of other factors, such as disturbance, but the results from these studies are perhaps less generalizable.…”

“…A strength of this approach is that it allows results about the relative importance of different factors, particularly plant functional traits, to be generalized more widely than if we had focused on a particular species, landscape, or the localized details of a particular climate change scenario. To achieve particular aims, previous work on integrating dynamic factors into models predicting PPunCC has focused more on other factors, such as dispersal , rate of climate change ), invasibility (Dullinger et al, 2004), landscape structure (Pearson & Dawson, 2005) or disturbance or focused on particular landscapes or species (Dullinger et al, 2004;Keith et al, 2008;Di Traglia et al, 2011). Several studies have used a cellular automata approach that represents population dynamics within a cell in a relatively abstract way (e.g.…”

“…Therefore, in this study we aim to build on recent work integrating dynamic processes, such as dispersal, colonization, population dynamics and growth, and disturbance regimes (Dullinger et al ., ; Iverson et al ., ; Keith et al ., ; Engler & Guisan, ; Midgley et al ., ; Di Traglia et al ., ) with a new spatially explicit model that focuses on the role of species' traits in migration, colonization, and persistence in a shifting climate. In this study, we illustrate the model with an example of decreasing rainfall in the South Western Australia Floristic Region, although the model and the results of this study are not limited to any one scenario, region, or climatic variable.…”

Spatial neighborhood effect and scale issues in the calibration and validation of a dynamic model of Phragmites australis distribution – A cellular automata and machine learning approach

“…A plethora of dynamic spatial models have been introduced and applied for a wide range of ecological and environmental applications (Parker et al, 2003). The cellular automata (CA) approach is widely applied in dynamic modeling (Hogeweg, 1988;Balzter et al, 1998;SoaresFilho et al, 2002;Parker et al, 2003;Molofsky and Bever, 2004;Colasanti et al, 2007;Silva et al, 2008;Di Traglia et al, 2011). In CA, transition rules capturing local interactions are used to replicate the landscape processes in a bottom-up approach (Fonstad, 2006).…”