Methods in Ecology and Evolution 2014
doi: 10.1111/2041-210X.12315
Modelling range dynamics under global change: which framework and why? de rik Saltre and Damien A. Fordham Miguel Lurgi*, Barry W. Brook, Fre The Environment Institute and School of Earth and Environmental Sciences, University of Adelaide, Adelaide, South Australia 5005, Australia
Summary 1. To conserve future biodiversity, a better understanding of the likely effects of climate and land-use change on the geographical distributions of species and the persistence of ecological communities is needed. Recent advances have integrated population dynamic processes into species distribution models (SDMs), to reduce potential biases in predictions and to better reflect the demographic nuances of incremental range shifts. However, there is no clear framework for selecting the most appropriate demographic-based model for a given data set or scientific question. 2. We review the computer-based modelling platforms currently used for the development of either populationor individual-based species range dynamics models. We describe the features and requirements of 20 software platforms commonly used to generate simulations of species ranges and abundances. We classify the platforms according to particular capabilities or features that account for user requirements and constraints, such as (i) ability to simulate simple to complex population dynamics, (ii) organism specificity or (iii) their computational capacities. 3. Using this classification, we develop a protocol for choosing the most appropriate framework for modelling species range dynamics based in data availability and research requirements. We find that the main differences between modelling platforms are related to the way in which they simulate population dynamics, the type of organisms they are able to model and the ecological processes they incorporate. We show that some platforms can be used as generic modelling software to investigate a broad range of ecological questions related to the range dynamics of most species, and how these are likely to change in the future in response to forecast climate and land-use change. We argue that model predictions will be improved by reducing usage to a smaller number of highly flexible freeware platforms. 4. Our approach provides ecologists and conservation biologists with a clear method for selecting the most appropriate software platform that meets their needs when developing SDMs coupled with population-dynamic processes. We argue that informed tool choice will translate to better predictions of species responses to climate and land-use change and improved conservation management.
Key-words: biogeography, climate change, conservation management, dispersal, global change, landscape dynamics, mechanistic model, metapopulation, population viability analysis, species distribution models
Introduction Climate and land-use change are affecting the geographical distributions of species world-wide (Fahrig 2003; Parmesan & Yohe 2003), with important consequences not only for the persistence of species but also the ecological communities they are embedded in (Lurgi, L opez & Montoya 2012). These changes and their consequences are likely to increase in the future, making predictions of species’ range dynamics and the rearrangement of biodiversity across the Earth a central focus of applied research in ecology and conservation biology (Dawson et al.
2011). This challenge requires an interdisciplinary approach, drawing on expertise from different areas of biology such as biogeography, community ecology and conservation biology. Stochastic demographic-based modelling frameworks have traditionally been used as a tool by conservation biologists to assess population viability (Beissinger & McCullough 2002). In parallel to these biogeographers and conservation biologists have commonly used, statistical species distribution models (SDMs), which use correlations between climatic and species occurrence data to create a static picture of the current and potential distribution of species (Elith & Leathwick 2009; Schurr et al. 2012). More recently, process-based models that link physiological traits with environmental conditions are
*Correspondence author. E-mail:
[email protected] © 2014 The Authors. Methods in Ecology and Evolution © 2014 British Ecological Society
2 M. Lurgi et al. providing a functionally realistic method for predicting species potential distributions (Kearney & Porter 2009). Reliable predictions of species range changes require a mechanistic understanding of range dynamics in relation to environmental variation and biotic interactions (Altwegg, Wheeler & Erni 2008; Urban, Zarnetske & Skelly 2013). Research done over the last decade suggests that if we are going to have any hope of predicting biodiversity changes due to climate change, we need to understand and reliably forecast species range shifts (Keith et al. 2008). In most real-world cases, this can only be achieved through integrative approaches that synthesize relevant information on a species’ demography, landscape dynamics, habitat suitability, dispersal and evolution (past and future) into spatiotemporal simulations of population dynamics (Fordham et al. 2014a). The recent trend towards the inclusion of demographic data and processes into range dynamics prediction has provided an ecologically realistic and less biased way of modelling species distributions and the dynamics of species ranges (Fordham et al. 2013b). This approach is based on leveraging the well-developed methods of population viability analyses (PVA) for single species and applying them to metapopulations. This integration has permitted the development of new quantitative ways for predicting the effects of spatiotemporal climatic variation on species distributions, range dynamics and extinction risk (Fordham et al. 2013c), providing a necessary step towards better understanding and forecasting novel communities due to climate change (Lurgi, L opez & Montoya 2012) and other environmental stressors. To this end, many researchers have developed software platforms and tools independently to allow demographic and landscape processes to be integrated into models of species range dynamics. Yet, there is a large amount of overlap between the functions, capabilities and goals of many of these software applications. In this study, we show that a range of software applications are well suited to allow demographic and autecological traits to be accounted for in predictions of species’ range dynamics. Since demographic-population models are being used with increasing frequency to simulate species’ responses to climate and land-use change, there is now an obvious need to know which platform is most suitable for a given data set and/or ecological question. Here, we address this need by providing an easy-to-use guide for identifying the modelling platform, from among the many available, that might best suit the requirements of different studies.
Modelling range dynamics in a changing world Population-based range dynamics models incorporate demographic information relevant for simulating range dynamics and understanding species distributions (Keith et al. 2008; Anderson et al. 2009). Examples include: survival and fecundity rates, population growth or carrying capacity. These methods typically take into account the suitability of the habitat occupied by the species, which in turn affects the demographic parameters. This is especially relevant in spatiotemporally changing settings, where demographic rates are affected
differentially across the entire species’ range (Fordham et al. 2013c). Furthermore, models of species range dynamics need to account for metapopulation structure, including migration and, in some cases, heterogeneity in dispersal behaviour (Fordham et al. 2014b). These processes directly affect a species’ ability to occupy habitable conditions and are hence key drivers of the dynamics of its distribution boundaries, especially in heterogeneous landscapes impacted by habitat loss or fragmentation (Wiegand, Revilla & Moloney 2005) and likely climate change (Travis et al. 2013). In the following sections, we thus focus on modelling platforms that allow for incorporation of ecological aspects of species’ range and population dynamics. EXISTING MODELLING FRAMEWORKS
On the basis of both the systematic review of the ecological modelling literature and the expertise of the authors; we identified 20 software frameworks (Table 1) that simulate population dynamics and landscape/spatial structure and have been applied to the study of species’ range dynamics. Using Thomson Reuters’ Web of Science search engine on 12th of July 2014, we searched for the following string: (range or dispersal), (dynamics), (spatially explicit), (model or software platform) and (species or population*). From the research papers resulting from this search (n = 1057), we selected those that specifically describe simulation modelling platforms for the study of species range dynamics or employ them to address particular questions related to global change (specifically: climate and land-use change and invasions) and range dynamics. We further added other articles that were referenced within those, and we considered relevant for this work. This approach resulted in us reviewing 47 research articles in detail. Our review uncovered a variety of approaches that have been used to model species range dynamics. These studies used a wide range of software tools (Table 1) or developed their own models using different programing environments. Some of the articles cited software tools that were either outdated (e.g. ALEX, Possingham & Davies 1995) or that were replaced by newer technologies (e.g. PATCH, Schumaker 1998). These tools were not included in our review. Judging by the speed at which platforms for modelling range dynamics have been developed (most of them were developed in the 2000s), we anticipate that it is likely that many more platforms will be added to this literature in coming years. This does not mean that the platforms reviewed here will not be used in the future. In fact, there is evidence that many of these have been around for a long time [e.g. SPOMSIM (Moilanen 2004) and VORTEX (Lacy 1993)]. Some of the platforms reviewed are highly flexible in terms of the possible model implementations that could be achieved using them. Yet, some others are detail driven and specific to particular case-study species and environments. We formulated a series of simple questions to help users to choose among existing platforms for their project/problem. These questions were based on the type of input parameters for the 20 platforms that we reviewed, the data available to develop demographic-based range dynamics models, and other consid-
© 2014 The Authors. Methods in Ecology and Evolution © 2014 British Ecological Society, Methods in Ecology and Evolution
GIS
Stochastic discretetime transitions between stages and classes in a complex life cycle structure
Scalar (based on difference equations) Stage-/agestructured transition matrices (Caswell 2000)
© 2014 The Authors. Methods in Ecology and Evolution © 2014 British Ecological Society, Methods in Ecology and Evolution
No
Yes/Trees (Forests)
No
Yes
No
No
Yes
Yes/Plants (Prunus spp.)
No
No
No
Yes/Plants
Yes
No
Population-based platforms No dynamics Yes
Local population dynamics
Tailored for particular organism?/Type of organism
TREEMIG
Low
Low PRUNUS
DYMEX
SPATIAL
BIOMOVE
Medium
Medium
LANDIS-II
GMBI
VORTEX
DEMONICHE
RAMAS
MDIG
SPOMSIM
META-X
MIGCLIM-R
High
Medium
Medium
Medium/ High
High
Low
Low
Flexibility
Platform name
Geographical stochastic occupancy model Stochastic patch occupancy model Stochastic patch occupancy model based on the incidence function model (Hanski 1994) Integrated to the GRASS GIS (GRASS Development Team 2012) platform. Allows for specific dispersal processes Spatially structured metapopulation. Demographics can be linked to niche (habitat suitability) values Demographic projection in populations is linked to spatially explicit niche values Certain aspects of the model (e.g. dispersal and genetics) are individual based; high flexibility offered by scripted functions Demographic parameters at the individual level, landscape heterogeneity. Tailored for biological invasions Flexible temporal and spatial resolution. Detailed representation of the effect of disturbances (e.g. fire regimes) Support for several plant functional types. Carrying capacity constrained by light availability and vegetation structure Multi-species, height-structured forest model. Useful for models in which competition for light is important Ability to incorporate very detailed information about the biology of the species Specifically designed for plant life cycles, particularly for Prunus species
Specific features
Table 1. Commonly used software tools for the development of species’ range dynamics models
Freeware
Licensed/1665 US$
Freeware with restrictions
Freeware
10 000 sites (potential populations)
5000 sites (potential populations) NA
10 000 000 sites (potential populations) 10 000 000 populations
1 000 000 sites (potential populations)
Freeware
Freeware
50 populations; 100 000 individuals
NA
100 000 sites (potential populations) 7000 populations
100 million populations NA 10 000 populations
Freeware
Freeware
Licensed/5400 US$
Freeware
Licensed/85 US$ Freeware
Freeware
Licence/cost
Computational capacity
No
Yes (Griffiths et al. 2010)
Yes (Meier et al. 2012)
Yes (Duveneck, Scheller & White 2014) Yes (Midgley et al. 2010)
No
Yes (Vargas et al. 2007)
No
Yes (Keith et al. 2008)
Yes (Pitt, Worner & Suarez 2009)
Yes (Engler et al. 2009) No Yes (Ozgul et al. 2006)
Used for climate change-related studies?
Sebert-Cuvillier et al. (2010)
Parry, Aurambout & Kriticos (2011)
Lischke et al. (2006)
Midgley et al. (2010)
Scheller et al. (2007)
Savage & Renton (2014)
Lacy (1993)
Nenzen et al. (2012)
Akcßakaya & Root (2005)
Pitt, Worner & Suarez (2009)
Engler, Hordijk & Guisan (2012) Grimm et al. (2004) Moilanen (2004)
References
Modelling species range dynamics 3
GIS
No
No
No
Yes/Plants
Medium
Medium
High
Medium
High
Low
Yes/Mosquitoes
No
Flexibility
Tailored for particular organism?/Type of organism
KERNELPOP
ALADYN
LPJ-DISP
LPJ-GUESS,
SHIFTER
RANGE
ECO-SPACE
HEXSIM
ANOSPEX
BUSTER;
SKEETER
Platform name
Freeware
Freeware
Freeware with restrictions
Freeware
NA
Freeware
Freeware
Interpatch heterogeneity incorporating the effects of local climate on demographics Individuals can incorporate behavioural, genetic, and demographic processes completely defined by the user Allows for the incorporation for biotic interactions and autoecological features (e.g. ecotoxicological) Detailed dispersal process. Includes plastic and evolutionary processes in a constrained way. Parameters are specified at the population level Incorporates land–atmosphere carbon exchange, large-scale plant distributions, and water and carbon flows Individuals’ locations are resolved in continuous space. Incorporates genetics of populations. Demographic parameters at the population level Allows for the study of genetic processes and dynamics at the population level
Licence/cost
Specific features
100 000 individuals
1 000 000 individuals
5 000 000 individuals
2 000 000 individuals
100 000 individuals
1 000 000 individuals
400 sites (potential populations)
Computational capacity
No
Yes (Schiffers et al. 2013)
Yes (Koca, Smith & Sykes 2006)
Yes (Bocedi et al. 2014b)
No
No
No
Used for climate change-related studies?
Strand & Niehaus (2007)
Schiffers & Travis (2014)
Smith, Prentice & Sykes (2001) and Snell (2014)
Bocedi et al. (2014b)
Loos et al. (2010)
Schumaker (2013)
Magori et al. (2009) and Oluwagbemi et al. (2013)
References
Tools are grouped by whether they are population (top) or individual (bottom) based. Local population dynamics specifies the way in which the dynamics of populations at the local community level are simulated. The geographical information systems (GIS) column classifies platforms by whether they are able to incorporate information of the landscape derived from GIS layers (e.g. rasters). The field ‘Tailored for particular organism?/Type of organism’ specifies whether the given tool is tailored to a particular type of organism. Platforms are classified according to their ‘Flexibility’ based on whether they are: (i) able to incorporate only a small fraction of information from the species being modelled (e.g. limited number of parameters) (Low), (ii) able to represent more complex phenomena by presenting user with more options to configure and provide input data for more processes than tools with ‘Low’ flexibility (Medium) and (iii) highly flexible platforms in terms of the possibilities provided to configure desired aspects of the biology, demographics or behaviour of the subject species (High). Some ‘specific features’ and the type of ‘Licence’ (with associated estimated cost – RPP in 2014) for each modelling platform are also given in addition to an estimation of their ‘computational capacity’. This measure was obtained by contacting the authors of each platform and asking for an estimation of how many populations/individuals would the platform be able to simulate in a standard desktop computer, c2014. Many of the platforms reviewed have been used for addressing climate change-related questions. This is specified in the table by the field ‘Used for climate change-related studies?’. NA values in the table mean that the information has not been possible to obtain from the authors.
Mix between stage/age-structured transition matrices and individual-level processes
No
Individual-based platforms They are a Yes consequence of mechanisms operating at the individual level
Local population dynamics
Table 1. (continued)
4 M. Lurgi et al.
© 2014 The Authors. Methods in Ecology and Evolution © 2014 British Ecological Society, Methods in Ecology and Evolution
Modelling species range dynamics
7
Box 1. Case study examples Example 1: Managed relocations and climate change (from Fordham et al. 2012a). Research question: Are managed relocation strategies useful to mitigate the threat of climate change for an endangered species of lizard? Goal: To construct a spatially explicit, metapopulation model with age-class structure in which climate-driven spatiotemporal variation in habitat availability influences population demographics. Data available: Demographic data at the population level for lizards in different age classes. GIS layers for habitat and environmental suitability. Choosing the appropriate model: Using Fig. 1, RAMAS would be chosen as the suitable modelling platform because the authors: (i) have demographic data available, (ii) need to simulate population dynamics, (iii) possess population-level demographic data, (iv) need a platform that is generic in terms of the organisms it can model and (v) require dynamic incorporation of GIS-type data and (vi) need to simulate an intermediate level of life cycle complexity. Platform used in the study: RAMAS. Example 2: Evolution of dispersal (from Bocedi et al. 2014b). Research question: How does an evolutionary response to environmental change differ when considering one versus two dispersal traits? Goal: To construct a spatially explicit, individual-based model with genetic representation of dispersal traits affected by environmental factors. Data available: Since this is a theoretical study, no data were used in it. Choosing the appropriate model: The decision tree proposed in this study would recommend either HEXSIM or RANGE SHIFTER as the appropriate modelling platform for this study because: (i) population dynamics at the individual level must be simulated, (ii) a generic type of organism has to be modelled and (iii) the platform must be capable of modelling genetics and either (iv) specific processes related to dispersal (RANGE SHIFTER) or (v) complex individual behaviour (HEXSIM), whereby the user can specify the dispersal behaviour of individuals as needed. Platform used in the study: RANGE SHIFTER.
encompasses the tools’ abilities to incorporate different aspects of the biology and ecology of the studied species (e.g. behaviour). For example, MDIG is able to simulate species dispersal and metapopulation structure across several different levels of complexity, from single population, limited-dispersal communities, to metapopulation structures with dispersal based on complex kernel functions. Additionally, some of the platforms shown in Table 1 are highly flexible in terms of their capabilities to implement different aspects of the biology, demographics or behaviour of species. An example of this is the complexity in the species life cycle (Fig. 1), which in some cases can include several details of each of the species life stages (e.g. SPATIAL DYMEX). In spite of an openly defined concept, ‘flexibility’ is meant to guide users with complex requirements to platforms that are most able to incorporate these, as well as users looking for platforms able to develop models with different levels of complexity. In Box 1, we illustrate the decision process associated with Fig. 1 by providing two examples from the literature where platforms presented here have been used to address specific research questions. Figure 1 allows users to make informed decisions on what modelling platform (among those in Table 1) will be better suited for their data and ecological question. Aside from the platforms mentioned in Table 1, other statistical approaches exist that incorporate demographic information into range dynamics analyses (Schurr et al. 2012). For example, Bayesian-oriented methods developed in generic software like OPENBUGS (openbugs.net) can be used to incorporate demographic as well as ecological processes (e.g. population growth rate, dispersal) into range dynamics estimation at an arbitrary level of detail. Like other simulation models such as stochastic patch occupancy models (Table 1), the power of these statistical techniques also lies in the simplicity of their parameterization. This could be advantageous if, for example,
detailed data on a broad range of life-history traits are not available. CONSERVATION MANAGEMENT
In the face of the rapid pace of change experienced by the Earth’s climate and ongoing habitat modification due to economic development, it is crucial that scientists have readily available and easy-to-use tools for addressing important questions regarding potential biodiversity loss from global change (Dawson et al. 2011). In particular, the ability to test different conservation strategies at appropriate spatial scales and to analyse the synergistic effects of multiple human-induced environmental changes is of key importance for both understanding and predicting future changes in species distributions and extinction risk and guiding policymakers and conservation managers on the use of scientific tools to properly weight different scenarios and options (Fordham et al. 2013c). Demographic-based models, which account for important processes such as source–sink dynamics (Bonnot et al. 2013; Fordham et al. 2013c) have provided scientifically grounded ways of testing and comparing different management strategies, especially under changing climate scenarios (Wintle et al. 2011; Regan et al. 2012; Fordham et al. 2013a). Here, we have identified that many tools are now capable of fulfilling these requirements, proposing ways of selecting the most appropriate conservation-orientated model based on the specific questions to be asked, the type of empirical data available and the processes to be incorporated into the models. Example 1 in Box 1 presents an example from the literature in which one of the platforms reviewed here has been used to address a specific conservation management question and illustrates the selection process for the appropriate platform (proposed in Fig. 1) for this case.
© 2014 The Authors. Methods in Ecology and Evolution © 2014 British Ecological Society, Methods in Ecology and Evolution
6 M. Lurgi et al. to choose among platforms where budget might constrain their decision. [For licensed platforms, estimated cost (RPP as of 2014) is given in Table 1.] The majority of the graphical user interface (GUI)-based platforms are designed to run only on MS Windows operating systems (e.g. HEXSIM, RAMAS, VORTEX, RANGE SHIFTER and META-X) rendering them inflexible in terms of system requirements, which not only affects their usability but also the extent to which their users can share their models. Some of the platforms are flexible in this respect and can be run on Linux, Mac OS and MS Windows environments (e.g. LPJ-GUESS, ALADYN and GMBI). Two of them are dependent on other platforms to be able to run: MIGCLIM-R requires the R statistical software (freeware: www.r-project.org) and libraries within it, whereas MDIG runs over the GRASS GIS platform (freeware: www.grass.osgeo.org). This makes both tools essentially multiplatform, since both R and GRASS can be used in any operating system. Our review revealed that several platforms simulate similar processes and phenomena (e.g. RAMAS, VORTEX, DEMONICHE and MDIG for population-based approaches; and HEXSIM, ECOSPACE and RANGE SHIFTER for individual-based models), suggesting that these platforms have been developed in spite of similar solutions being readily available for the same ultimate purpose. Even though some of these platforms are meant to simulate specific processes, the model implementation they use could potentially be achieved using existing, more generic tools. For example, the model-specific advantages acclaimed for RANGE SHIFTER, ECO-SPACE or SKEETER BUSTER, could, at least in principle, have been achieved using generic tools such as HEXSIM or MDIG, albeit less conveniently. We support the use of generic tools (such as HEXSIM, MDIG, GMBI or LPJ) that are flexible in the way they can be used to implement different ecological processes. This will encourage researchers to use a single (or a small set of) modelling platform(s) rather than having a large set of user- or group-specific tools; thus limiting the duplication of effort in software development, reducing modelbased errors through improved model testing and facilitating research collaboration. Several platforms can be used to address specific research questions regarding a species’ population viability or range dynamics [e.g. RAMAS (Akcßakaya 2000), VORTEX (Vargas et al. 2007), HEXSIM (Schumaker et al. 2014), MDIG (Pitt, Worner & Suarez 2009) and RANGE SHIFTER (Bocedi et al. 2014a)]. In some cases, however, researchers still prefer to address their questions without the help of existing platforms. They opt for the development of their own models using different programming languages such as C (Tyre, Possingham & Lindenmayer 1999), Visual Basic (Rizkalla & Swihart 2012) and SELES (Brotons et al. 2012), among others (e.g. the Python programming language: www.python.org). This is likely to reflect the importance of the competence that the user/researcher has in programming ecological models when it comes to choosing among different platforms. Users with programming experience will generally favour platforms that are more flexible in terms of execution, can be driven by repeatable scripts, and have the possibility of third-party enhancement. Conversely, researchers without an established experience in code
development will be more inclined to sacrifice flexibility by using self-contained tools (sometimes referred to as ‘canned packages’) that will allow them to configure and execute models without dedicating time to learning a programming language. There might be cases in which it could be desirable to use a software platform with a standard GUI if it makes the parameterization and execution of different model runs simpler and helps avoid erroneous results. However, if a standardized GUI is provided, this should ideally not come at the cost of a tool that can run on several operating systems. The proliferation of demographic-based range dynamics platforms during the last decade (many of the platforms shown in Table 1 were developed after the year 2000) could be a result of the availability of more powerful computers. This technological advance has facilitated the incorporation of more complex phenomena that could not previously be modelled into ecological analyses of species range dynamics, such as evolutionary processes (Thuiller et al. 2013). Another reason for the development of new platforms could be related to the difficulty in adopting existing technologies for particular case studies or applied problems. It is also sometimes difficult for users to operate existing tools because of the learning process involved or simply because it might not be apparent how to adapt or tune them to meet the needs of the current modelling exercise. This can prompt researchers to devote a great deal of time to developing their own models from scratch, despite this leaving them open to erroneous results due to limited model testing (Lindenmayer et al. 1995). Plants often differ in their life history compared to mobile organisms mainly due to their dispersal mode and their interactions with neighbouring individuals. Plants are also involved in carbon sequestration and other biogeochemical processes that deserve particular attention because of the feedback loops they create, which can affect these organisms’ range dynamics. This has justified the development of modelling platforms specifically tailored for the simulation of plant-specific processes (Fig. 1). Models for plants are thus designed to account for many of these phenomena (e.g. TREEMIG, LPJ-GUESS and LANDIS-II). Other organisms with complex life cycles that transmit epizootics have also demanded the development of tailored range dynamics platforms (e.g. ANOSPEX and SKEETER BUSTER). Dispersal is one of the main drivers of range dynamics, particularly under climate change scenarios (Travis et al. 2013). Therefore, models of species’ range dynamics (demographic based or not) must account for this process explicitly. This realization has prompted the creation of simple dispersal models used to simulate species distributions (e.g. MIGCLIM-R) through to sophisticated demographic-based range dynamics tools that incorporate ways of simulating dispersal heterogeneity at each stage of the invasion pathway (Fig. 1 – e.g. RANGE SHIFTER). However, many of the other platforms reviewed here can accommodate different levels of detail for the dispersal process, provided that the user is capable of implementing it. We define flexibility as a measure of a tool’s capability to simulate key demographic processes (e.g. dispersal and metapopulation dynamics) using simple to complex approaches, depending on the available data and/or research question. It also
© 2014 The Authors. Methods in Ecology and Evolution © 2014 British Ecological Society, Methods in Ecology and Evolution
Modelling species range dynamics
7
Box 1. Case study examples Example 1: Managed relocations and climate change (from Fordham et al. 2012a). Research question: Are managed relocation strategies useful to mitigate the threat of climate change for an endangered species of lizard? Goal: To construct a spatially explicit, metapopulation model with age-class structure in which climate-driven spatiotemporal variation in habitat availability influences population demographics. Data available: Demographic data at the population level for lizards in different age classes. GIS layers for habitat and environmental suitability. Choosing the appropriate model: Using Fig. 1, RAMAS would be chosen as the suitable modelling platform because the authors: (i) have demographic data available, (ii) need to simulate population dynamics, (iii) possess population-level demographic data, (iv) need a platform that is generic in terms of the organisms it can model and (v) require dynamic incorporation of GIS-type data and (vi) need to simulate an intermediate level of life cycle complexity. Platform used in the study: RAMAS. Example 2: Evolution of dispersal (from Bocedi et al. 2014b). Research question: How does an evolutionary response to environmental change differ when considering one versus two dispersal traits? Goal: To construct a spatially explicit, individual-based model with genetic representation of dispersal traits affected by environmental factors. Data available: Since this is a theoretical study, no data were used in it. Choosing the appropriate model: The decision tree proposed in this study would recommend either HEXSIM or RANGE SHIFTER as the appropriate modelling platform for this study because: (i) population dynamics at the individual level must be simulated, (ii) a generic type of organism has to be modelled and (iii) the platform must be capable of modelling genetics and either (iv) specific processes related to dispersal (RANGE SHIFTER) or (v) complex individual behaviour (HEXSIM), whereby the user can specify the dispersal behaviour of individuals as needed. Platform used in the study: RANGE SHIFTER.
encompasses the tools’ abilities to incorporate different aspects of the biology and ecology of the studied species (e.g. behaviour). For example, MDIG is able to simulate species dispersal and metapopulation structure across several different levels of complexity, from single population, limited-dispersal communities, to metapopulation structures with dispersal based on complex kernel functions. Additionally, some of the platforms shown in Table 1 are highly flexible in terms of their capabilities to implement different aspects of the biology, demographics or behaviour of species. An example of this is the complexity in the species life cycle (Fig. 1), which in some cases can include several details of each of the species life stages (e.g. SPATIAL DYMEX). In spite of an openly defined concept, ‘flexibility’ is meant to guide users with complex requirements to platforms that are most able to incorporate these, as well as users looking for platforms able to develop models with different levels of complexity. In Box 1, we illustrate the decision process associated with Fig. 1 by providing two examples from the literature where platforms presented here have been used to address specific research questions. Figure 1 allows users to make informed decisions on what modelling platform (among those in Table 1) will be better suited for their data and ecological question. Aside from the platforms mentioned in Table 1, other statistical approaches exist that incorporate demographic information into range dynamics analyses (Schurr et al. 2012). For example, Bayesian-oriented methods developed in generic software like OPENBUGS (openbugs.net) can be used to incorporate demographic as well as ecological processes (e.g. population growth rate, dispersal) into range dynamics estimation at an arbitrary level of detail. Like other simulation models such as stochastic patch occupancy models (Table 1), the power of these statistical techniques also lies in the simplicity of their parameterization. This could be advantageous if, for example,
detailed data on a broad range of life-history traits are not available. CONSERVATION MANAGEMENT
In the face of the rapid pace of change experienced by the Earth’s climate and ongoing habitat modification due to economic development, it is crucial that scientists have readily available and easy-to-use tools for addressing important questions regarding potential biodiversity loss from global change (Dawson et al. 2011). In particular, the ability to test different conservation strategies at appropriate spatial scales and to analyse the synergistic effects of multiple human-induced environmental changes is of key importance for both understanding and predicting future changes in species distributions and extinction risk and guiding policymakers and conservation managers on the use of scientific tools to properly weight different scenarios and options (Fordham et al. 2013c). Demographic-based models, which account for important processes such as source–sink dynamics (Bonnot et al. 2013; Fordham et al. 2013c) have provided scientifically grounded ways of testing and comparing different management strategies, especially under changing climate scenarios (Wintle et al. 2011; Regan et al. 2012; Fordham et al. 2013a). Here, we have identified that many tools are now capable of fulfilling these requirements, proposing ways of selecting the most appropriate conservation-orientated model based on the specific questions to be asked, the type of empirical data available and the processes to be incorporated into the models. Example 1 in Box 1 presents an example from the literature in which one of the platforms reviewed here has been used to address a specific conservation management question and illustrates the selection process for the appropriate platform (proposed in Fig. 1) for this case.
© 2014 The Authors. Methods in Ecology and Evolution © 2014 British Ecological Society, Methods in Ecology and Evolution
8 M. Lurgi et al. FUTURE PERSPECTIVES
We are now faced with the strategic challenge of organizing ourselves, as an effective global science community. A central goal should be focused research teams that utilize (and improve) a set of flexible modelling platforms to better understand the likely effects of forecast global change on species range dynamics. Using a subset of models (developed at different institutions using different programming languages and architectures), rather than a single model, can improve model predictions. This is because evidence from various areas of numerical modelling suggests that multimodel averages often yield better predictions than a single model (Johnson & Omland 2004). In ecological work, including the use of phenomenological SDMs (Ara ujo & New 2007), weighted averaging of different model results is now widely used to account for model-selection uncertainty under the assumption that this will lead to more robust estimates of model predictions. Furthermore, inter-model comparisons are used to improve model development by identifying the importance of key model assumptions. This approach has been used to improve predictions from dynamic vegetation models (Smith, Prentice & Sykes 2001), phenomenological SDMs (Gritti et al. 2013) and general circulation models (Fordham et al. 2012b). The development and testing of a core set of platforms will reduce bias in predictions since the degree of testing and encountering of unusual situations that the code is subject to will typically be much higher compared to platforms with only a small number of users. A larger user community allows improvements to be developed, implemented and tested progressively, and their outcomes compared with previous versions. In contrast, a situation where every researcher develops his/her own code independently makes testing difficult, reduces model enhancement and leads to results that are not comparable across platforms and output metrics that are obscure. A main gap common to all the modelling platforms presented here is the lack of available support not only for users but, perhaps more importantly, for developers trying to enhance the capabilities of existing architectures. Although some of the platforms reviewed offer user manuals (e.g. META-X, MDIG, VORTEX, RAMAS and HEXSIM), few have any support system where users and developers could potentially post their questions and problems and obtain feedback (VORTEX is an obvious exception, with an active users community and e-mailing list). In most cases, the only option a user has is to contact the developers directly, which makes the process of model development less efficient. The set-up of online support communities (e.g. moderated forums), like the one created around VORTEX, should thus be given priority if we are to make the use of the existing platforms easier and encourage their further development.
Concluding remarks Conservation biologists and biogeographers are using demographic-based approximations of species’ range dynamics with increasing frequency. This is because they are likely to provide
improved estimates of the effects of climate and land-use change on species distributions and abundances (Fordham et al. 2013b). Although technological constraints and data availability might limit such assessments, we argue that several available platforms are already equipped to model key complex ecological phenomena relevant for improving predictions of species’ responses to climate and land-use change (for example: individual heterogeneity in dispersal behaviour (RANGE SHIFTER, Bocedi et al. 2014b), community succession (TREEMIG, Meier et al. 2012) and density-dependent population growth (RAMAS, Keith et al. 2008)]. The main issue for researchers is finding a way to decide which tool is most appropriate for their modelling needs. We meet this requirement by providing an easy-to-use step-by-step guide for determining which species range dynamics model to use, when, and why.
Acknowledgements We would like to thank Justin Travis and two anonymous reviewers for useful comments and suggestions that helped improve this manuscript. Australian Research Council (ARC) grants supported contributions of M.L. B.W.B. and D.A.F. (LP12020024, FT100100200 and FT140101192).
Data accessibility This paper does not use any data.
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