1. General Model Information

Name: Species distribution models - BIOCLIM

Acronym: BIOCLIM


Main medium: terrestrial
Main subject: spatial distribution
Organization level: landscape
Type of model: rule-based
Main application: research, simulation/optimisation tool, decision support/expert system
Keywords: biodiversity, species distribution, mapping, GIS, ruleset modeling, nonlinear optimisation, local climate, soil maps, mapping tool, envelope model, spatial distribution modeling framework, genetic algorithm, optimisation of multimodal objective functions, search algorithm

Contact:

About the developers of the BIOCLIM/GARP MOSAIC tool
Phone: +61-6-274 1134
Fax : +61-6-274 1333
email: davids@erin.gov.au

Author(s):

Bioclim:
Busby, J.R., McMahon, J.P., Hutchinson, M.F., Nix, H.A. and Ord, K.D
Garp/Mosaic:
David Stockwell, David Peters, Tasmanian Parks and WildlifeService and Tony Boston of the Environmental Resources Information Network (ERIN)

Abstract:

The idea of BIOCLIM is to find a single rule that identifies all areas with a similar climate to the locations of the species. To do this, the basic BIOCLIM algorithm (Nix 1986, Busby 1991, McMahon et al. 1996) finds the climatic range of the points for each climatic variable. The Climatic Envelope Model is a GARP-simulation of the bounding box, climate envelope method as used in BIOCLIM. It uses the concept of a bounding box to enclose the data points formed from a number of climate variables derived from climate surfaces (see below). It differs from the BIOCLIM program in the rule-based algorithm it uses to derive the predictions.The rules used here, consisting of ranges of climate for all climate variables, then encloses all points, within statistically defined limits. For example, the following rule was formed from the ranges of climate variables that enclose 90% of the data points as determined by calculating the mean and the standard deviation of the points.


IF      TANN=(23,29]degC AND TMNCM=(10,16]degC AND TMXWM=(35,38]degC 
        AND TSPAN=(19,27]degC AND TCLQ=(21,23]degC AND TWMQ=(29,30]degC 
        AND TWETQ=(24,32]degC AND TDRYQ=(19,26]degC AND RANN=(609,1420]mm 
        AND RWETM=(156,319]mm AND RDRY=(1,1]mm AND RCV=(101,123]mm2 
        AND RWETQ=(460,874]mm AND RDRYQ=(0,9]mm AND RCLQ=(1,16]mm 
        AND RWMQ=(272,532]mm AND TMEL=(17,263]masl AND TMXEL=(40,303]masl 
        AND TMNEL=(4,230]masl AND TREL=(0,105]masl AND LONG=(128,136]deg 
        AND LAT=(-12,-15]deg
THEN    SP=PRESENT
Central assumptions are used in Climate Envelope Model are: the distribution of the species is determined by climate, the distribution of the climatic variables is standard normal, and all variables with restricted ranges influence the species of interest. With all modelling systems if the assumptions of a method are not satisfied then the results will be unreliable, or simply quite wrong. While Bioclim has been shown to give satisfactory results for many species, there was a percieved need to develop a system with less restrictive assumptions. Thus GARP (Genetic Algorithm for Rule-set Production) was developed with aims to develop models with: The solution proposed by GARP is to produce a set of rules predicting presence and absence, each one statistically significant at increasing the probability of presence or absence of a species. An example of a rule set is given below:
IF      TCLQ=(6,19]degC
THEN    SP=ABSENT

IF      GEO=(28,241]c AND SRT=(3,4]c AND TMNEL=(-19,308]masl AND LAT=(-13,-39]deg
THEN    SP=ABSENT

IF      RWMQ=(107,1176]mm
THEN    SP=PRESENT

IF      GEO=(6,244]c AND TMNEL=(285,1480]masl
THEN    SP=ABSENT
The production of a set of rules raises the problem of conflict. For example, at a given point, one rule might predict the presence of a species and another the absence. In these cases GARP predicts using the rule with the highest expected accuracy. GARP has a number of other features for increasing the rigour, reliability, and flexability when modelling species distributions. In the context of this application GARP can be seen as value-adding to the BIOCLIM method by increasing the accuracy and suggesting causal factors. This can be seen by comparing the predictive accuracy and the rules generated by the two methods.

II. Technical Information

II.1 Executables:

Operating System(s): DOS, UNIX

II.2 Source-code:

Programming Language(s): ANSI C / FORTRAN 77 older versions of GARP available by ftp

II.3 Manuals:



II.4 Data:



III. Mathematical Information


III.1 Mathematics


III.2 Quantities


III.2.1 Input

III.2.2 Output


IV. References

Busby, J.R. (1991) BIOCLIM - A Bioclimatic Analysis and Prediction System. In: Margules, C.R.& M.P. Austin (eds.) Nature Conservation: Cost Effective Biological Surveys and Data Analysis.pp. 64-68. Canberra: CSIRO.
McMahon, J.P., Hutchinson, M.F., Nix, H.A. and Ord, K.D. (1996). ANUCLIM Version 1 User'sGuide. Canberra: ANU, CRES. [see also ANUCLIM 5.0: http://cres.anu.edu.au/outputs/anuclim.html.]
Nix, H.A. (1986). A biogeogaphic analysis of Australian Elapid snakes, in Longmore, R. (ed.)Atlas of Australian Elapid Snakes. Australian Flora and Fauna Series 8: 4-15.

V. Further information in the World-Wide-Web


VI. Additional remarks


Last review of this document by: J. Bierwirth, 12.01.2001 --
Status of the document:
last modified by Tobias Gabele Wed Aug 21 21:44:39 CEST 2002

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