TY  - JOUR
A1  - Cabral, Juliano Sarmento
A1  - Valente, Luis
A1  - Hartig, Florian
T1  - Mechanistic simulation models in macroecology and biogeography
BT  - state-of-art and prospects
JF  - Ecography : pattern and diversity in ecology
N2  - Macroecology and biogeography are concerned with understanding biodiversity patterns across space and time. In the past, the two disciplines have addressed this question mainly with correlative approaches, despite frequent calls for more mechanistic explanations. Recent advances in computational power, theoretical understanding, and statistical tools are, however, currently facilitating the development of more system-oriented, mechanistic models. We review these models, identify different model types and theoretical frameworks, compare their processes and properties, and summarize emergent findings. We show that ecological (physiology, demographics, dispersal, biotic interactions) and evolutionary processes, as well as environmental and human-induced drivers, are increasingly modelled mechanistically; and that new insights into biodiversity dynamics emerge from these models. Yet, substantial challenges still lie ahead for this young research field. Among these, we identify scaling, calibration, validation, and balancing complexity as pressing issues. Moreover, particular process combinations are still understudied, and so far models tend to be developed for specific applications. Future work should aim at developing more flexible and modular models that not only allow different ecological theories to be expressed and contrasted, but which are also built for tight integration with all macroecological data sources. Moving the field towards such a ‘systems macroecology’ will test and improve our understanding of the causal pathways through which eco-evolutionary processes create diversity patterns across spatial and temporal scales.
Y1  - 2016
U6  - https://doi.org/10.1111/ecog.02480
SN  - 0906-7590
SN  - 1600-0587
VL  - 40
IS  - 2
SP  - 267
EP  - 280
PB  - Wiley
CY  - Hoboken
ER  - 
TY  - JOUR
A1  - Dormann, Carsten F.
A1  - Schymanski, Stanislaus J.
A1  - Cabral, Juliano Sarmento
A1  - Chuine, Isabelle
A1  - Graham, Catherine
A1  - Hartig, Florian
A1  - Kearney, Michael
A1  - Morin, Xavier
A1  - Römermann, Christine
A1  - Schröder-Esselbach, Boris
A1  - Singer, Alexander
T1  - Correlation and process in species distribution models: bridging a dichotomy
JF  - Journal of biogeography
N2  - Within the field of species distribution modelling an apparent dichotomy exists between process-based and correlative approaches, where the processes are explicit in the former and implicit in the latter. However, these intuitive distinctions can become blurred when comparing species distribution modelling approaches in more detail. In this review article, we contrast the extremes of the correlativeprocess spectrum of species distribution models with respect to core assumptions, model building and selection strategies, validation, uncertainties, common errors and the questions they are most suited to answer. The extremes of such approaches differ clearly in many aspects, such as model building approaches, parameter estimation strategies and transferability. However, they also share strengths and weaknesses. We show that claims of one approach being intrinsically superior to the other are misguided and that they ignore the processcorrelation continuum as well as the domains of questions that each approach is addressing. Nonetheless, the application of process-based approaches to species distribution modelling lags far behind more correlative (process-implicit) methods and more research is required to explore their potential benefits. Critical issues for the employment of species distribution modelling approaches are given, together with a guideline for appropriate usage. We close with challenges for future development of process-explicit species distribution models and how they may complement current approaches to study species distributions.
KW  - Hypothesis generation
KW  - mechanistic model
KW  - parameterization
KW  - process-based model
KW  - species distribution model
KW  - SDM
KW  - uncertainty
KW  - validation
Y1  - 2012
U6  - https://doi.org/10.1111/j.1365-2699.2011.02659.x
SN  - 0305-0270
VL  - 39
IS  - 12
SP  - 2119
EP  - 2131
PB  - Wiley-Blackwell
CY  - Hoboken
ER  -