RAMAS® GIS is designed to link your GIS with a metapopulation model for population viability analysis (PVA) and extinction risk assessment. Habitats used by most species are becoming increasingly fragmented, requiring a metapopulation modeling approach to risk analysis. Recognizing habitat patchiness from an endangered species' point of view requires spatial information on habitat suitability. RAMAS® GIS meets both of these requirements by linking metapopulation modeling with landscape data and GIS technology.
Use RAMAS® GIS to
link your GIS to an ecological model,
use landscape data in your population viability analysis (PVA),
incorporate changes in habitat into a metapopulation model,
combine geographic and demographic data for risk assessment.
New Free Tools for Developing and Analyzing Population Models with RAMAS
Recent developments make it easier to use RAMAS Metapop and RAMAS GIS to develop and analyze population models.
Using mark-recapture data
Mark-recapture data are collected by marking individuals (e.g., using bands or tags) at their first capture and recording their subsequent recaptures. This type of data is immensely valuable for estimating parameters of population models, including survival rates and fecundities. Many methods have been developed for analyzing such data, but most of them are either incomplete
(i.e., they do not allow a full population model) or are too complex. A new method, implemented as an R script, allows building fully-specified population models for RAMAS, based only on mark-recapture data (Ryu et al. 2016). It creates a fully specified RAMAS model file, which includes stage structure, standard deviations, and density dependence functions.
Its main features include:
estimating true survival based on apparent survival estimates and population trends;
fecundity as an unbiased estimate of juvenile:adult ratio, by using the relative capture probabilities of juveniles and adults;
estimating density dependence in survival and fecundity;
estimating natural temporal variability in survival and fecundity (excluding sampling variability);
creating ready-to- run RAMAS input files;
incorporating uncertainties and preparing the files necessary for a global sensitivity analysis (see below).
The new method, including the R script, data for case studies and sample results, is freely available at: https://github.com/Akcakaya/MAPS-to-Models.
Global sensitivity analysis
The sensitivity analysis module of RAMAS GIS (see Chapter 13 of the manual) allows analyzing sensitivity to one parameter at a time. A more comprehensive method, called global sensitivity analysis, considers all parameters simultaneously. A new method, implemented as an R package, allows global sensitivity analyses using RAMAS (Aiello-Lammens and Akçakaya 2016). The R package, including sample data and a tutorial, is
freely available at https://github.com/mlammens/demgsa.
Aiello-Lammens, M.A. and H.R. Akçakaya. 2016. Using global sensitivity analysis of demographic models for ecological impact assessment. Conservation Biology (in press). DOI: 10.1111/cobi.12726
Ryu, H.Y., K.T. Shoemaker, É. Kneip, A.M. Pidgeon, P.J. Heglund, B.L. Bateman, W.E. Thogmartin, and H.R. Akçakaya. 2016. Developing population models with data from marked individuals. Biological Conservation 197:190–199.
RAMAS® GIS is a comprehensive extinction risk assessment system that operates in five steps:
Spatial data. RAMAS® GIS imports spatial data on ecological requirements of a species. These may include GIS-generated maps of vegetation cover, land-use, or any other map that contains information on some aspect of the habitat that is important for the species (temperature, precipitation, slope, aspect, etc.). RAMAS® GIS then combines the information in all these map layers into a map of habitat suitability (HS) with a user-defined habitat suitability function.
Patch recognition. RAMAS® GIS uses the HS map to find habitat patches. It uses a patch-recognition algorithm and identifies areas of high suitability as a patch where a subpopulation may survive. The carrying capacity and other population-specific parameters of this patch (e.g., survival, fecundity, maximum growth rate) can be calculated as a user-defined function of the total HS, average HS, core area, patch perimeter (edge) and other habitat characteristics (from the GIS maps) for that the patch. RAMAS GIS then displays the spatial structure of the metapopulation, super-imposed with a color-coded map of habitat suitability and any other geographical feature that the user wants to include (coastlines, rivers, cities, etc.), and saves the patch structure as input for the metapopulation model. It also calculates various landscape indices (shape index, fractal dimension, edge:area ratio, etc.).
Habitat dynamics. If you have predictions about how habitat may change in the future (e.g., as a result of forest growth, logging, etc.), including merging or splitting patches because of habitat growth or fragmentation. RAMAS® GIS imports this information in the form of a time series of habitat maps, and creates time series of population-specific parameters for input into the metapopulation model.
Metapopulation model. RAMAS® GIS combines the spatial information on the metapopulation with user-input ecological parameters of the species to complete the metapopulation model. This model is identical to RAMAS® Metapop, which is described below. RAMAS® GIS contains RAMAS® Metapop, so you should not buy both programs.
Ecological risk assessment. The user can run the metapopulation model to predict the risk of species extinction, time to extinction, expected occupancy rates and metapopulation abundance. The output options include all results listed for RAMAS® Metapop. In addition, RAMAS® GIS has a sensitivity analysis feature that allows multiple simulations with automatically changed input parameters. You can compare results from different simulations by superimposing graphs of risk curves, time-to-extinction distributions, trajectory summary, metapopulation occupancy and other outputs.
GIS Data Requirements
Your GIS data layers must have the following: (i) the maps must be in raster format; (ii) all layers must be numerical; (iii) all must describe the landscape with the same precision (i.e., number of cells in both north-south and east-west directions should be the same); (iv) coverage of all maps must be identical (the corners of the rectangular area described by all maps should be the same); (v) the number of columns must be the same for each row; (vi) the "cells" (i.e., locations given a value by the map) must be arranged in a square (not hexagonal) format; (vii) the maximum number of rows and columns depend on the available memory, but each cannot exceed 16000. The formats supported are:
GRASS: Exported as an ASCII file from GRASS (with header). See description ofr.out.ascii in the GRASS Reference Manual. Do not use the option -h.
IDRISI: Raster (image) file of IDRISI in binary or ASCII format. The document or description file (.RDC or .DOC) must also be in the same directory as the image file (.RST or .IMG). The image can be ASCII (integer or real) or binary (byte, integer, real), but not packed binary.
ARC/INFO: Exported as an ASCII file from ARC/Info (with header). See description of the command GRIDASCII in ARC Command References in the ARC/Info manual.
PC ARC/INFO: Exported as a Non-compressed ASCII file from PC ARC/Info, with the command POLYGRID.
ERDAS: Raster gis file format of Erdas version 7.4. Only .GIS files are supported.
blanks: ASCII format (no header); each row of map must be in a single line (terminated by line-feed/carriage-return characters), and values must be separated by blanks. If each row takes more than one line in the file, the "fixed grid" format must be used.
fixed grid: ASCII format (no header); each row of map may be in one or more lines. Cell values must be separated by blanks. Number of columns (i.e., the number of cells in each row) must be specified.
RAMAS® GIS has an interactive, user-friendly menu system. Editing input parameters, displaying results, and selecting output options are done with this menu system that includes a context-sensitive on-line help facility. There is also a large set of error and warning messages, and each input parameter (whether input from keyboard or file) is checked for consistency to prevent errors. Both input data and results can be saved to disk files. Each type of result can be
viewed as graphs on the screen,
printed as graphs to a printer,
saved as tables to disk files,
viewed as tables on the screen,
printed as tables to a printer,
saved as a numerical table to a disk file,
exported to a spreadsheet as a data table.
Documentation and Examples
The program is accompanied by a 200-page manual which includes discussions on basics of population and metapopulation dynamics, and descriptions of various menus and screens. One chapter contains a tutorial that illustrates the concepts of metapopulation dynamics with the use of several examples, and demonstrates the use of the software by guiding the user through step-by-step instructions. RAMAS® GIS comes with sample files for about 60 species, including northern spotted owl, helmeted honeyeater, California gnatcatcher, California spotted owl, land snail, blue whale, jack-in-the-pulpit, speckled alder, teasel, loggerhead sea turtle, pool frog and other species.
For a more detailed description of RAMAS® GIS, see Linking GIS with Models.
Q. Is RAMAS® Metapop a "black box"?
No, not at all. There are several features in RAMAS Metapop and RAMAS GIS that are specifically designed to make the programs transparent:
The methods used in the program are described in detail in the manual as well as in several peer-reviewed publications.
The source code of the program is summarized in the form of pseudo-code in a file distributed with the program, as well as in an appendix to the manual.
You can link your own code to the program, with a "user-defined function" option.
Several examples of "user-defined functions" are provided with the program, including their full source codes as well as the compiled versions.
The manual and the help file list the basic assumptions and technical limitations of the program.
The program allows you to print a list of "Model Summary and Assumptions" for your model.
The program displays your input parameters in the form of various graphs that allow you to examine the properties of your model.
The program indicates which input parameters it is not using (e.g., if catastrophe probability is zero, then the magnitude of the catastrophe impact is not relevant).
The manual and the help file include the exact definitions of the model parameters, how they are used by the program, tips on parameter estimation, and suggestions for various methods of testing and validating your model.
The program makes many checks, and displays warnings if it detects potential problems in your model.
The help file provides a list and detailed explanation of these warning messages, as well as suggestions about how to correct your model.
Q. If I get RAMAS® GIS, do I also need RAMAS® Metapop?
No. RAMAS® GIS contains RAMAS® Metapop, in addition to other programs for interface with geographic information systems (GIS) and for sensitivity analysis.
Q. Can I use RAMAS® GIS without landscape (GIS) data?
Yes. In such cases, you'll use only the metapopulation modeling component, and if you want to model more than one population, you'll need to specify the location and carrying capacity of each population.
Q. Can I use RAMAS® Metapop (or RAMAS® GIS) to model a single population?
Yes. The single population may have age or stage structure, or it may be a scalar model (such as the logistic model). It may have density dependence, or you may model simple exponential growth. It may have demographic and/or environmental stochasticity, or it may be a deterministic model. The minimum model in RAMAS® must have a growth rate (R) and an initial abundance (N0).
Q. How many populations/stages/years/replications can I simulate?
You can model 1 to 500 populations, with 1 to 50 age classes or stages, for 1 to 500 time steps, and run simulations with up to 10,000 replications.
The duration of a time step depends on the time step of the age or stage matrix. If, for example, the age classes are at 1-year intervals, then you can simulate the dynamics for up to 500 years. However, note that the uncertainty of model results increase with increased time steps, and often it does not make sense to run a simulation for more than 50 or 100 time steps.
The number of replications determines the precision (but not the accuracy) of the risk curves. At least 1000 replications are necessary in many cases; 10,000 replications are enough for almost all cases.
Q. What are the assumptions and limitations of the program?
Models in RAMAS® are expressed in discrete time. The program does not use continuous-time models. We believe that most modeling needs in population dynamics can be met with (and perhaps are most naturally expressed as) discrete-time formulations.
Since RAMAS® GIS and RAMAS® Metapop models can only address a single species, they cannot explicitly represent competition, predation, mutualism or other interspecific interactions. These interactions can be modeled as constant, stationary (randomly fluctuating with constant mean), or deterministically varying (e.g., cyclic) influences on demographic parameters. In addition, it may be possible to model such interactions in a user-written code linked to the program.
Population dynamics within each population is modeled with a stage- or age-structured matrix model, which may also include sex structure. The model may contain non-linearities describing density dependence relations between matrix elements and the population abundance. The program also allows used-written code to be linked to a model. This provides a lot of flexibility that will let you model most species. However, some very complex structures (for example those requiring complicated rules representing social structure) may be difficult or impossible to incorporate unless they can be simplified to a matrix model.
Within-population correlations among vital rates are restricted to 1 (full), 0 (none) and -1. (Among populations, any correlation between -1 and +1 can be specified.)
A catastrophe can either be regional (occur at the same time for all the populations) orlocal (occur independently for each population).
If a model includes two different types of catastrophes, the correlation between the two catastrophes (in time) can be zero, maximum positive or maximum negative.
The program does not estimate any input parameters. The manual provides background information, simple examples, and references to the relevant literature to help the users with parameter estimation. The program includes sample files that are parameterized from literature data on several species.
The program makes certain assumptions about the way parameters are combined into the overall model. These are implicit in the equations used in the program, and include dispersal-distance and correlation-distance functions, density-dependent dispersal, density dependence functions, etc. For the exact methods and equations the program uses, see discussions in the manual (particularly the algorithm in Appendix I).
In addition to these limitations, any particular model built in RAMAS® will have a set of assumptions, characterized by its parameters (or lack of them). As with any other program or model, how realistic the metapopulation model is largely depends on the amount and type of data. This program will, hopefully, let the user utilize such data effectively in predicting extinction risks, and perhaps even guide additional field work by helping the user identify parameters to which the risk results seem to be most sensitive. However, no program or model is a substitute for empirical work (see also the question on validation below).
Q. How can I validate a model in RAMAS®?
RAMAS® GIS and RAMAS® Metapop have been validated at the program level by checking all subunits and algorithms of the subprograms, by making sure that the program does what is described in the manual, by checking the lower-level algorithms for consistency (e.g., random distributions of vital rates), etc. RAMAS® GIS and RAMAS® Metapop are not models, but tools which help users build models. The validation at the model level is your responsibility. To validate a model, start with a review of the types of results that the program gives, and make sure that they will address the question you are asking. Next, you should review the assumptions and limitations of the program (see the manual) and convince yourself that these fit the species you are modeling. If they don't fit (or you are not sure whether they do or not) then at least you are aware of these assumptions and limitations. The major component of the validation involves your input of the model parameters into the program. It is essential that you understand how these parameters are used by the program, and not rely on your intuitive understanding based on the names given to various parameters in the program. The same names or symbols may be used in entirely different contexts by different programs or textbooks, leading to several common mistakes. If you have large amounts of data, you can also validate your model by comparing the model predictions with observations. However desirable this type of validation may be, it is difficult and often impossible for stochastic models since you need observations on abundances of several populations as well as their variation and risks of local extinction.
The manual provides several suggestions for testing or checking your model.
Q. How much data do I need?
The amount of data you need to build a model in RAMAS® Metapop or RAMAS® GIS depends mostly on the question you want to address and on the ecology of the species you are modeling. For most question related to conservation and management of species, you need to know the initial abundances and the vital rates (fecundity and survival) and have some idea about the amount of temporal variation in these rates.
The more data you have, the more detailed models you can build. Including more details makes a model more realistic, and lets you address more specific questions. However, in most practical cases, available data permit only the simplest models. Attempts to include more details than can be justified by the quality of the available data may result in decreased predictive power and understanding. The trade-off between realism and functionality depends on the characteristics of the system under study (e.g., the ecology of the species), what you know of the system (the availability of data), and what you want to know or predict about the system (the questions addressed). Even when detailed data are available, models intended to analyze long-term metapopulation persistence may include less detail than those intended to predict next year's distribution of breeding pairs within a local population.
Q. How many parameters does a RAMAS® Metapop model have?
The number of parameters in a model you create in RAMAS® Metapop or RAMAS® GIS may range from just a few to several thousands, and depends mostly on the amount and quality of data you have (see above).
You can model the population dynamics at whatever level of complexity you feel is appropriate. For example, you can model an unstructured model by specifying the number of stages as 1; you can assume stable age distribution instead of entering the initial number of individuals in each age or stage class; you can ignore spatial structure by assuming all correlations to be 1 (or 0), and all dispersal (migration) rates to be at the same rate regardless of distance; you can assume that there is no density dependence in population growth or in migration rates; you can build a deterministic model by setting all standard deviations to 0; if you don't have data on the demography of each population, you can assume all populations have the same demographic characteristic by copying the parameters of one population, etc.
The advantage of using a detailed program like RAMAS® GIS or RAMAS® Metapop is that these programs not only let you build detailed models, but even when you build only simple ones, they remind you of the assumptions you implicitly make. For example, most occupancy-type models of metapopulation dynamics assume that there is no correlation among fluctuations of populations. You can easily build such a model in RAMAS® GIS or RAMAS® Metapop, but whatever the model is, the correlation matrix screen is there to remind you of this assumption. However, RAMAS® cannot handle some of the more unrealistic assumptions, such as infinite number of patches (yes, believe it or not, there are metapopulation models which implicitly assume an infinite number of patches!). We believe that models with such assumptions have little practical importance for conservation biology.
Q. What type of results does the program report?
The main result of the "Spatial Data" component of RAMAS® GIS is the spatial structure of the metapopulation, including the size and location of populations, and edge-to-edge or center-to-center distances among them. In addition, you can output the habitat suitability map, a histogram of habitat suitability values, and landscape indices such as patch size, edge length, edge:area ratio, core area, shape index, fractal dimesion, etc. If the habitat is changing, the "Habitat dynamics" component of RAMAS® GIS incorporates these changes and creates an input file for the "Metapopulation model" component that includes dynamic spatial structure.
Q. How does the program incorporate age or stage structure?
The age or stage structure of populations are modeled with a transition matrix (a Leslie matrix or a Lefkovich matrix). The elements of the matrix are the vital rates (fecundities and survivals, or the transition rates among stages). The manual describes how such a matrix can be estimated, and the program includes sample files with age- and stage-structured models from the literature.
The model can have up to 100 different age or stage matrices, each of which can be assigned to one or more populations. In addition, each population can have two modifiers (constants) with which the fecundities and survivals are multiplied. Thus each population can have a different age or stage matrix.
Each population can have a different initial age or stage structure (number of individuals in each age class or stage), or the user may ask the program to set the initial age/stage abundances to be at the stable age/stage distribution.
The vital rates may be modeled to fluctuate (see below). Their mean values must be positive. In addition, the program checks that the sum of all survivals from a given stage is less than or equal to one. These checks are done on the mean values before a simulation begins, and on the simulated (random) matrices at each time step of each replication of a simulation.
The vital rates may also have a temporal trend (deterministic change) specified as a time series of relative values.
Q. How does the program incorporate stochasticity?
Environmental stochasticity is modeled by (i) random fluctuations in age or stage-specific fecundities and survivorships, (ii) random fluctuations in carrying capacities, (iii) random fluctuations in dispersal rates, and (iv) 2 types of local or regional catastrophes.
The variability of each vital rate and each carrying capacity is modeled with a standard deviation. Each population can have a separate set of standard deviations. The random fluctuations can be normal- or lognormal-distributed, and can be correlated among populations. Within a population, survivals, fecundities and carrying capacities can be uncorrelated, perfectly correlated or negatively correlated.
Two different types of catastrophes can be specified. Each type may have population- or age-specific effects and probabilities, and may act by decreasing abundances, carrying capacities, survival, fecundity, and/or dispersal. Catastrophes may be local (affecting each population independently) or regional (affecting all populations. Local catastrophes may spread from one population to another, either by dispersers (e.g., a disease) or by other means. Probability and impact of catastrophes may change as a function of the number of years since the last catastrophe (e.g., risk of fire and its temperature may increase with time until the next fire).
Demographic stochasticity is modeled by sampling the number of survivors from a binomial distribution and the number of offspring from a Poisson distribution (Akcakaya 1991).
Q. How does the program incorporate sex structure and mating systems?
The program allows separate stages for males and females. The mating system can be monogamous, polygynous, or polyandrous. For polygynous and polyandrous systems, the degree of polygamy is set with a "number of mates per individual" parameter.
In RAMAS® Metapop, the mating system determines the degree to which each sex is limiting in reproduction. In monogamous mating, both sexes are equally limiting; the reproduction is a function of the minimum number of breeding males or females. In polygynous breeding, females are more limiting than males, because each male can mate with multiple females (and it is assumed that females take care of the young). For example, if each male can mate with 2 females, then reproduction is a function of the minimum of the number of female breeders, or 2 times the number of male breeders. In this case, as long as there are at least half as many male breeders as female breeders, reproduction is determined by the number of female breeders only. In polyandrous breeding, males are more limiting than females, because each female can mate with multiple males (and it is assumed that males take care of the young).
For monogamous or polygynous mating, fecundity is expressed in terms of daughters and sons per female (i.e., if the model is age-structured, there are 2 fecundity values for each female age class). In polyandrous mating, it is assumed that males do most of the parental care, so fecundity is more conveniently expressed in terms of daughters and sons per male.
Q. How does the program incorporate uncertainty?
In addition to the sources of variation (such as environmental fluctuations, catastrophes and demographic stochasticity), measurement error introduces additional uncertainty to predictions about the future of a population. Here, "measurement error" is used in a general sense, including all types of variation that originate from incomplete knowledge about the parameters to be used in RAMAS®.
The current versions of RAMAS® cannot explicitly incorporate such uncertainties. This is partly because of the fact that these are quite model-specific. For example, for some models you may be uncertain about the value of carrying capacity, for other models whether to use "scramble" or "contest" type of density dependence, and still for others whether to use density dependence at all. It is also partly because of the fact that in most field studies, it is next to impossible to separate variation introduced by measurement error from that introduced by natural fluctuations, including those induced by human impact, demographic stochasticity, catastrophes, etc.
One of the best ways to deal with such uncertainties is to use them to derive worst and best case estimates of extinction risks. This way, you use all the natural variation included in your model to estimate extinction risks, and all uncertainties about your model to evaluate the precision of these risk estimates.
How do you go about doing this? Ideally, for each parameter you would have a range instead of a single estimate, and build models with at least 3 values (minimum, best, and maximum estimates) of the parameter. In addition, you would have multiple models whenever you are uncertain about a qualitative feature of the model, such as the type of density dependence. After running all these models, you would get a bunch of risk curves instead of a single one. You can determine the upper and lower bounds on this collection of risk curves by recording, for every probability level, the largest and smallest population threshold reached by any risk curve. Note that such a bounding curve may be composed of parts from several curves.
If you have several uncertain parameters for which you have ranges instead of single estimates, this method can quickly get out of hand because of the large number of simulations you need to run. For example, if you have 5 uncertain parameters, each with three estimates (minimum, best, maximum), you need to run ( 3 · 3 · 3 · 3 · 3 ) = 243 simulations. Remember that these are simulations, not replications. Each of these simulations should have a sufficiently large number of replications, say 1000.
In cases when you have more than a few uncertain parameters, you can use a short-cut that will give approximately the same result. The short-cut involves an attempt to select parameter values that will give the lowest and highest risks, and to group them together. For example, if you are uncertain about the standard deviation of three vital rates, such as fecundity, juvenile survival and adult survival, you can assume that a model with the lower estimates of all three standard deviations will give the lowest risks, and a model with the higher estimates of all three standard deviations will give the highest risks. Thus, instead of running several simulations with all possible combinations of minimum, best and maximum estimates of the three parameters ( 3³ = 27 simulations), you would run only two simulations. Of course, for some parameters your assumption may be wrong. The program manual includes a list of guidelines that may help you decide what values of uncertain parameters to use to obtain models that will give the highest and lowest risks. Remember however that these are not strict rules, and they may be wrong in some cases, particularly if there are interactions among factors. Whenever in doubt, use all possible combinations, as discussed above.
Q. How does the program incorporate density dependence?
Each population in the model can have a different density dependence function. Density dependence may act on survivorships, fecundities, or all vital rates. The functions for density dependence include
none: exponential growth or decline according to the age or stage matrix; no additional parameters are required.
ceiling: exponential growth up to a population ceiling; requires the value of maximum population size or ceiling (K).
scramble: logistic or Ricker function; requires Rmax and K (see below).
contest: Beverton-Holt function; requires Rmax and K (see below).
user-defined (see below).
These density dependence functions use the same two basic parameters:
Maximum growth rate (Rmax) is the growth rate at low population sizes, used by scramble and contest types of density dependence,
Carrying capacity (K) is the equilibrium population size (for scramble and contest types) or the population ceiling (ceiling-type).
Density dependence can be based on the number of individuals in all stages, or in selected stages (e.g., when only adults compete for territories). It can also be based on the sum of the products of stage-specific fecundity and stage abundance (as in some fisheries models).
In addition, you can create models with Allee effects (decreased vital rates at low population sizes), and Allee effects combine
d with density dependence at high population sizes such as ceiling, scramble or contest types described above.
User-defined density dependence functions: You can also write your own density dependence function and link it to RAMAS® Metapop as a DLL. The manual includes a detailed description of this process and the program comes with several example functions (including their source codes as well as the compiled versions).
Q. How does the program incorporate genetics?
The program incorporates genetics by allowing fecundity and/or survival to be a function of the coefficient of inbreeding. This is programmed as a user-defined density dependence function (see above). Currently, the function that implemets inbreeding depresion works only for age-structured, single-population models. However, the source code for this function is provided, so users may experiment with expanding the function to multiple populations and stage structure.
Q. Can I use maps from ArcView in RAMAS® GIS?
You can export maps from ArcView for use in RAMAS® GIS with two different methods. One method requires IDRISI for Windows, the other requires the Spatial Analyst extension of ArcView. Note most maps in ArcView are in vector format, whereas RAMAS® GIS uses only raster data. Before you use the following methods with your vector-based maps, we recommend that you learn about vector-to-raster conversions in general. The following methods assume that your vector-based maps have polygon (rather than line or point) data.
In ArcView, add the theme that you are interested in to the view.
In the view, make this theme active, and from the Theme pull-down menu, select "Convert to Shapefile…".
Select a name for the new shapefile and click OK to save it in the specified location.
In IDRISI, change the working folder to the folder where you have saved the shape file:
If you have Idrisi for Windows: select ENVIRON from the Environment (pull-down) menu, and change "Path of working data directory". Click "OK".
If you have Idrisi32: select Data paths from the File menu, and change "Main working folder". Click "OK".
Open the "SHAPEIDR: Shapefile/Idrisi Conversion" dialog:
If you have Idrisi for Windows: Select "Import/Export" from the File pull-down menu. This will open the "Idrisi Import/Export Utility" window. Select "Software-specific formats" from the Import pull-down menu. This will open another menu. Select SHAPEIDR.
If you have Idrisi32: Select "Import" from the File pull-down menu. This will another menu; select "Software-specific formats". This will open another menu; select "ESRI Formats". This will another menu; select SHAPEIDR.
Select "Shapefile to Idrisi". For "Input Shapefile", specify the name of the shape file you have saved. For "Output Idrisi vector file" specify a filename. Select the appropriate choices for "Reference system", "Reference units" and "Unit distance" (or, leave then at the default settings of "plane" "meters" and "1", respectively). See the help file of Idrisi for SHAPEIDR for more information. Click "OK".
After the conversion is done, return to the Idrisi main window. Use POLYRAS in the "Raster/Vector Conversion" menu (under the Reformat pull-down menu) to convert the vector file to raster file. See the help file of Idrisi for POLYRAS for more information.
In RAMAS® GIS, start the "Spatial data" program (if it is not already running).
Select "Input maps" under the Model menu.
Add a map (see RAMAS® GIS help for more information), and give it a name. For "File", enter the name of the raster file you have created in Idrisi. For "Format", specify IDRISI or Idrisi32, depending on the version of Idrisi you used. Click "View".
In ArcView, load the extension Spatial Analyst.
Add the theme that you are interested in to the view.
In the view, make this theme active. From the Analysis pull-down menu, select "Analysis Properties". Set the "Analysis extent" to this theme, and set the cell size to what you wish. Click "OK". (This will automatically establish the number of rows and columns.)
Under the Theme pull-down menu, select "Convert to grid".
Select the field that you are interested in.
Select "no" to adding all of the information to the table, and select "yes" to adding the theme to the view.
Under the File pull-down menu, select "Export Data Source".
Select "ASCII Raster".
Navigate to where the grid you just created is and select it, and click "OK".
Give the file a name and navigate to where you want the file, and click "OK".
In RAMAS® GIS, start the "Spatial data" program (if it is not already running).
Select "Input maps" under the Model menu.
Add a map (see RAMAS® GIS help for more information), and give it a name. For "File", enter the filename you have specified in Step 10. For "Format", specifyARC/INFO. Click "View".
Q. Can I use shape files in RAMAS® GIS?
You can convert shape files for use in RAMAS® GIS with two different methods. One method requires IDRISI for Windows, the other requires ArcView with Spatial Analyst extension. Note that shape files are in vector format, whereas RAMAS® GIS uses only raster data. Before you make this conversion, we recommend that you learn about vector-to-raster conversions in general. The following methods assume that you have polygon (rather than line or point) data in your shape file.
Via IDRISI: See "Exporting maps from ArcView (via IDRISI) to RAMAS® GIS" above, and follow steps 4 through 10.
Via ArcView (with Spatial Analyst): Open the shape file in ArcView, and then follow the steps in "Exporting maps from ArcView (with Spatial Analyst) to RAMAS® GIS" above.
Q. How can I make the program display maps faster when resizing a window?
Sometimes, the maps in the "Spatial data" and "Habitat dynamics" programs are repeatedly redrawn when you click on the window to resize, making resizing very slow. To prevent this, do the following. Right click on an empty part of the desktop and select "Properties". In the "Display properties", find the option for "Show window contents while dragging" and uncheck it. This option may be under the "Plus!" tab, the "Effects" tab, or the "Effects" button of the "Appearance" tab.
A habitat-based metapopulation model of the California Gnatcatcher
Citation: Akçakaya and Atwood (1997; Conservation Biology 11:422-434).`
California Gnatcatcher (Polioptila c. californica) is a federally threatened subspecies inhabiting the coastal sage scrub community in southern California. The coastal sage scrub is a distinctive plant community that has declined due to extensive agricultural and urban development in this area. Our project involved an analysis of the dynamics of the California Gnatcatcher in central and coastal Orange County, California. Using GIS data, we developed and validated a habitat model for the species in this analysis. We then used this habitat model as a basis of a metapopulation model, which included demographic data such as fecundity, survival, as well as variability in these demographic rates.
Habitat Modeling Based on GIS Data
We used GIS data (raster maps exported from ARC/INFO) on the vegetation and topography of an approximately 850 km² region of Orange County, California. Using this data and the locations of gnatcatcher pair observations, we estimated a habitat model with logistic regression. Significant variables included the percentage of coastal sage scrub, elevation, distance from grasslands, distance from "trees" (forest, woodland, chaparral), and various interactions among these variables. We validated the model by estimating the habitat function using only data on gnatcatcher locations in the northern half of the study area, and predicting the habitat suitability of the locations where gnatcatcher pairs were observed in the southern half. We entered the habitat model in RAMAS® GIS to create a habitat suitability map.
Habitat suitability map for the California Gnatcatcher, Orange County, CA. Darker red indicates more suitable habitat, white indicates insuitable habitat. The black lines show the borders of habitat patches identified by RAMAS® GIS.
We used RAMAS® GIS to identify patches in the habitat suitability map. A habitat patch is a cluster of suitable cells that can support a local gnatcatcher population. The collection of these local populations make up the gnatcatcher metapopulation in the study area. Thus we used the habitat model to calculate the spatial structure of the metapopulation, including size and location of habitat patches and the distances among them. RAMAS® GIS also calculated the average and total habitat suitability in each patch. We combined the spatial structure of the model with demographic parameters (such as survival, fecundity, dispersal, and catastrophe) that we estimated with data from field studies This resulted in a stage-structured, stochastic, spatially-explicit metapopulation model. Using this model, we simulated the dynamics of the metapopulation under various assumptions.
Results and Future Directions
The model predicted a high risk of decline in the next 50 years with most combinations of parameters. However, there was a considerable range of outcomes due to uncertainties in parameters. Results were most sensitive to density-dependent effects, the probability of weather-related catastrophes, adult survival, and adult fecundity. Based on data used in the model, the greatest difference in results was given when the simulation's time horizon was only a few decades, suggesting that modeling based on longer or shorter time horizons may underestimate the effects of alternative management actions. For more information see Akçakaya and Atwood (1997; Conservation Biology 11:422-434).
We are planning to refine the model in the future, and use it to assess or rank management and conservation alternatives. One type of management that can be evaluated with this kind of a model is habitat conservation and restoration. Suppose, for example, that three of the habitat patches identified in this study are potential candidates for habitat conservation and restoration. If these patches vary in size, then there would a total of 7 alternatives (ranging from restoring only the smallest patch to restoring all three). These, plus the "no action" alternative, can be evaluated by running a series of simulations that incorporate the expected improvements in the carrying capacity and other parameters of the patches where habitat would be restored.
Cost Benefit Graph
The 8 options can then be ranked in order of increasing effectiveness (in, for example, reducing the risk of extinction). For this example, we might expect that the larger the area where habitat is improved, the lower the extinction risk of the gnatcatchers. The obvious choice is to improve the habitat in all three patches. In reality the choices are much less obvious, because improving all three patches may cost more than what is available for California gnatcatcher habitat management, which means we need to consider the costs as well. We could rank the 8 options with respect to both their benefit (reduction in risk of extinction) and with respect to their cost (see Figure above). Such a graph allows the evaluation of each conservation action in terms of costs and benefits, without falling into the trap of assigning a monetary value to the existence of a species.
Applied Biomathematics has world-leading experts in metapopulation modeling and the use of RAMAS® GIS 6.0. We are here to help, and can lead projects in modeling, results synthesis, report writing, and peer reviewed publications.
Modeling and Analysis
We have extensive experience in population viability analysis (PVA), the use of spatial and landscape data, and related analyses, and are available to perform original research in this area to suit your needs.
Data Synthesis and Report Writing
Our expert scientists can assess and summarize data and existing research and clearly communicate this synthesis in reports useful for policy development or decision making.
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