@book{sp:2013,
address = {NY},
author = {Bivand, Roger S and Pebesma, Edzer J and Gomez-Rubio, Virgilio},
edition = {Second edi},
publisher = {Springer},
title = {{Applied spatial data analysis with R}},
url = {https://asdar-book.org/},
year = {2013}
}
@article{Eddelbuettel:2011Rcpp,
abstract = {The Rcpp package simplifies integrating C++ code with R. It provides a consistent C++ class hierarchy that maps various types of R objects (vectors, matrices, functions, environments, ...) to dedicated C++ classes. Object interchange between R and C++ is managed by simple, flexible and extensible concepts which include broad support for C++ Standard Template Library idioms. C++ code can both be compiled, linked and loaded on the fly, or added via packages. Flexible error and exception code handling is provided. Rcpp substantially lowers the barrier for programmers wanting to combine C++ code with R.},
author = {Eddelbuettel, Dirk and Francois, Romain},
doi = {},
isbn = {9781461468677},
issn = {15487660},
journal = {Journal Of Statistical Software},
keywords = {C,R,call,foreign function interface},
number = {8},
pages = {1--18},
title = {{Rcpp : Seamless R and C++ Integration}},
url = {https://www.jstatsoft.org/htaccess.php?volume=40&type=i&issue=08},
volume = {40},
year = {2011}
}
@book{Eddelbuettel:2013Rcpp,
address = {New York, NY},
author = {Eddelbuettel, Dirk},
isbn = {978-1-4614-6867-7},
publisher = {Springer},
title = {{Seamless R and C++ Integration with Rcpp}},
year = {2013}
}
@article{Fall:2001em,
abstract = {Gaining insight into the dynamic nature of landscapes often involves the use of simulation models to explore potential changes over long time frames and extensive spatial areas. However, bridging the gap between conceptual models of landscape dynamics and their simulation on a computer can lead to many pitfalls. If implemented using a general-purpose programming language, the underlying model becomes hidden in the details of the computer code, making it difficult to compare the conceptual and implemented models, and to modify the model. Alternatively, previously built models may contain hidden assumptions and have limited adaptability. Domain-specific languages have been developed in a number of areas to facilitate the construction of models at a level closer to the conceptual model, thereby making model implementation more accessible to domain experts. Such tools to support modelling in the domain of landscape ecology can achieve a balance between the flexibility of programming and the structure and ease of using pre-built models. One of the goals of SELES (Spatially Explicit Landscape Event Simulator) has been to create a language for modelling landscape dynamics that provides ecologists and planners with an appropriate tool to address some of the problems that arise in model development. Our high-level, structured language separates the specification of model behaviour from the mechanics of its implementation, freeing landscape modellers from programming and allowing them to focus on the underlying model. This language is declarative and thus permits a clear representation of the conceptual model, which the SELES engine converts into a computer simulation of landscape change. Our structured framework guides the development of a broad class of spatio-temporal landscape models by aiding model prototyping, modification, verification, comparison, and re-use.},
author = {Fall, Andrew and Fall, Joseph},
journal = {Ecological Modelling},
pages = {1--18},
title = {{A domain-specific language for models of landscape dynamics}},
volume = {141},
year = {2001}
}
@book{Grimm:2005bk,
address = {Princeton, NJ},
author = {Grimm, Volker and Railsback, Steven F},
isbn = {9780691096667},
pages = {413},
publisher = {Princeton University Press},
title = {{Individual-based modelling and ecology}},
year = {2005}
}
@article{Grimm2005science,
abstract = {Agent-based complex systems are dynamic networks of many interacting agents; examples include ecosystems, financial markets, and cities. The search for general principles underlying the internal organization of such systems often uses bottom-up simulation models such as cellular automata and agent-based models. No general framework for designing, testing, and analyzing bottom-up models has yet been established, but recent advances in ecological modeling have come together in a general strategy we call pattern-oriented modeling. This strategy provides a unifying framework for decoding the internal organization of agent-based complex systems and may lead toward unifying algorithmic theories of the relation between adaptive behavior and system complexity.},
author = {Grimm, Volker and Revilla, Eloy and Berger, Uta and Jeltsch, Florian and Mooij, Wolf M and Railsback, Steven F and Thulke, Hans-Hermann and Weiner, Jacob and Wiegand, Thorsten and DeAngelis, Donald L},
doi = {10.1126/science.1116681},
issn = {1095-9203},
journal = {Science},
keywords = {Animal,Animals,Behavior,Biological,Decision Support Techniques,Ecology,Economic,Ecosystem,Fishes,Fishes: physiology,Models,Systems Theory,Theoretical,Trees,Uncertainty},
month = nov,
number = {5750},
pages = {987--991},
pmid = {16284171},
title = {{Pattern-oriented modeling of agent-based complex systems: lessons from ecology.}},
url = {https://www.ncbi.nlm.nih.gov/pubmed/16284171},
volume = {310},
year = {2005}
}
@Manual{raster:2015,
    title = {{raster: Geographic Data Analysis and Modeling}},
    author = {Robert J. Hijmans},
    year = {2015},
    note = {R package version 2.3-40},
    url = {https://CRAN.R-project.org/package=raster},
}
@misc{NetLogo,
address = {Evanston, IL},
author = {Wilensky, U},
publisher = {Center for Connected Learning and Computer-Based Modeling, Northwestern University},
title = {{NetLogo}},
url = {https://www.netlogo.org},
year = {1999}
}
@article{North:2013repast,
abstract = {This paper is to describe development of the features and functions of Repast Simphony, the widely used, free, and open source agent-based modeling environment that builds on the Repast 3 library. Repast Simphony was designed from the ground up with a focus on well-factored abstractions. The resulting code has a modular architecture that allows individual components such as networks, logging, and time scheduling to be replaced as needed. The Repast family of agent- based modeling software has collectively been under continuous development for more than 10 years.},
author = {North, Michael J and Collier, Nicholson T and Ozik, Jonathan and Tatara, Eric R and Macal, Charles M and Bragen, Mark and Sydelko, Pam},
doi = {10.1186/2194-3206-1-3},
issn = {2194-3206},
journal = {Complex Adaptive Systems Modeling},
keywords = {Groovy,agent-based modeling,complex adaptive systems modeling,java},
number = {1},
pages = {3},
title = {{Complex adaptive systems modeling with Repast Simphony}},
url = {http://www.casmodeling.com/content/1/1/3},
volume = {1},
year = {2013}
}
@article{sp:2005,
author = {Pebesma, Edzer J and Bivand, Roger S},
journal = {R News},
number = {2},
title = {{Classes and methods for spatial data in R}},
url = {https://cran.r-project.org/doc/Rnews/},
volume = {5},
year = {2005}
}
@article{Petchey:2015el,
abstract = {Forecasts of ecological dynamics in changing environments are increasingly important, and are available for a plethora of variables, such as species abundance and distribution, community structure and ecosystem processes. There is, however, a general absence of knowledge about how far into the future, or other dimensions (space, temperature, phylogenetic distance), useful ecological forecasts can be made, and about how features of ecological systems relate to these distances. The ecological forecast horizon is the dimensional distance for which useful forecasts can be made. Five case studies illustrate the influence of various sources of uncertainty (e.g. parameter uncertainty, environmental variation, demographic stochasticity and evolution), level of ecological organisation (e.g. population or community), and organismal properties (e.g. body size or number of trophic links) on temporal, spatial and phylogenetic forecast horizons. Insights from these case studies demonstrate that the ecological forecast horizon is a flexible and powerful tool for researching and communicating ecological predictability. It also has potential for motivating and guiding agenda setting for ecological forecasting research and development.},
author = {Petchey, Owen L and Pontarp, Mikael and Massie, Thomas M and K\'{e}fi, Sonia and Ozgul, Arpat and Weilenmann, Maja and Palamara, Gian Marco and Altermatt, Florian and Matthews, Blake and Levine, Jonathan M and Childs, Dylan Z and McGill, Brian J and Schaepman, Michael E and Schmid, Bernhard and Spaak, Piet and Beckerman, Andrew P and Pennekamp, Frank and Pearse, Ian S},
doi = {10.1111/ele.12443},
journal = {Ecology Letters},
keywords = {dynamics,ecosystems,environmental change,forecasting,futures,prediction,scenarios},
title = {{The ecological forecast horizon, and examples of its uses and determinants}},
year = {2015}
}
@article{Scheller:2007em,
abstract = {We introduce LANDIS-II, a landscape model designed to simulate forest succession and disturbances. LANDIS-II builds upon and preserves the functionality of previous LANDIS forest landscape simulation models. LANDIS-II is distinguished by the inclusion of variable time steps for different ecological processes; our use of a rigorous development and testing process used by software engineers; and an emphasis on collaborative features including a flexible, open architecture. We detail the variable time step logic and provide an overview of the system architecture. Finally, we demonstrate model behavior and sensitivity to variable time steps through application to a large boreal forest landscape. We simulated pre-industrial forest fire regimes in order to establish base-line conditions for future management. Differing model time steps substantially altered our estimates of pre-industrial forest conditions. Where disturbance frequency is relatively high or successional processes long, the variable time steps may be a critical element for successful forest landscape modeling. © 2006 Elsevier B.V. All rights reserved.},
author = {Scheller, Robert M and Domingo, James B and Sturtevant, Brian R and Williams, Jeremy S and Rudy, Arnold and Gustafson, Eric J and Mladenoff, David J},
doi = {10.1016/j.ecolmodel.2006.10.009},
isbn = {0304-3800},
issn = {03043800},
journal = {Ecological Modelling},
keywords = {Disturbance,Forest landscape simulation model,Forest succession,LANDIS-II,Manitoba,Simulation model architecture,Simulation model design},
pages = {409--419},
title = {{Design, development, and application of LANDIS-II, a spatial landscape simulation model with flexible temporal and spatial resolution}},
volume = {201},
year = {2007}
}
@article{Thiele2015,
abstract = {There are two major limitations to the potential of computational models in ecology for producing general insights: their design is path-dependent, reflecting different underlying questions, assumptions, and data, and there is too little robustness analysis exploring where the model mechanisms explaining certain observations break down. We here argue that both limitations could be overcome if modellers in ecology would more often replicate existing models, try to break the models, and explore modifications. Replication comprises the re-implementation of an existing model and the replication of its results. Breaking models means to identify under what conditions the mechanisms represented in a model can no longer explain observed phenomena. The benefits of replication include less effort being spent to enter the iterative stage of model development and having more time for systematic robustness analysis. A culture of replication would lead to increased credibility, coherence and efficiency of computational modelling and thereby facilitate theory development.},
author = {Thiele, Jan C and Grimm, Volker},
doi = {10.1111/oik.02170},
journal = {Oikos},
title = {{Replicating and breaking models: good for you and good for ecology}},
year = {2015}
}
