Quantifying the importance of habitat patches and links for landscape connectivity through graphs and habitat availability metrics

Please visit the Conefor website for additional information: http://www.conefor.org Conefor

Conefor is a software package that allows quantifying the importance of habitat areas and links for the maintenance or improvement of landscape connectivity. It is conceived as a tool for decision-making support in landscape planning and habitat conservation, through the identification and prioritization of critical sites for ecological connectivity.

Conefor includes new connectivity indices (integral index of connectivity, probability of connectivity) that have been shown to present an improved performance compared to some other existing indices and to be particularly suited for landscape conservation planning and change monitoring applications (Pascual-Hortal & Saura 2006, Saura & Pascual-Hortal 2007, Saura & Rubio 2010). These indices are based on graph structures and on the concept of measuring habitat availability at the landscape scale. They consider a patch itself as a space where connectivity occurs, integrating the connected area existing within the patches (intrapatch connectivity) with the area made available by the connections with other habitat patches in the landscape (interpatch connectivity).

Conefor is simple to use and runs in any standard computer with a Windows operative system. Conefor is distributed free of charge for non-commercial use, with the only condition of citing the software (Saura & Torné 2009) and the most-related references (Pascual-Hortal & Saura 2006, Saura & Pascual-Hortal 2007, Saura & Rubio 2010). Conefor has been developed by S. Saura and J. Torné and has been funded by the Spanish Ministry of Science and Innovation and European FEDER funds through Conefor (REN2003-01628/GLO), Ibepfor (CGL2006-00312/BOS), Montes-Consolider (CSD2008-00040) and Decofor (AGL2009-07140/FOR) projects. The Fortran source codes of Sensinode 1.0 (Landgraphs package) by Dean L. Urban (Duke University, USA) were the starting point for the development of the new Conefor codes in which the new metrics and developments were implemented.

The following map shows the approximate location of the different case studies that have applied the Conefor software package and the new connectivity metrics there implemented. Click on the icons for a reference and link with further details for each study. A larger version of this map is available here.

Please visit the Conefor website for additional information: http://www.conefor.org Conefor

Software for simulating landscape spatial patterns through the modified random clusters method

What is SIMMAP?

SIMMAP 2.0 is a software that generates categorical spatial patterns that are similar to those commonly found in real landscapes. SIMMAP allows obtaining a wide range of spatial patterns with any number of classes in which fragmentation and class abundance can be independently and systematically varied. It is also possible to obtain patterns with anisotropy and to control the minimum mapped unit (size of the smallest patch) of the artificially generated landscapes. See the SIMMAP manual for further details: Download PDF

SIMMAP is the result of implementing the modified random clusters (MRC) method. This method provides more general and realistic results than other commonly used landscape models. See the paper published in Lanscape Ecology for further details: Download PDF

SIMMAP 2.0 simulations are low computational time consuming. Even in a PC at 333 MHz (quite below the specifications of current computers), typical computational times are less than one second for 200x200 pixels patterns, around 2 seconds for 400x400 images and around 4 seconds for 800x800 pixels landscapes. SIMMAP also computes several landscape pattern configuration indices on the MRC patterns, such as those related to edges, number, size and shape of the patterns, among others. The obtained raster MRC patterns can be saved as image files in "bmp" format, which may be imported in other image processing or GIS software if necessary.

SIMMAP is distributed without charge for non-commercial use, provided that the related references (as described in the manual) are conveniently cited. Users are asked to provide the author a brief description of the applications for which SIMMAP is used.

Download SIMMAP 2.0 User's Manual: Download PDF

Download paper in Landscape Ecology: Download PDF

How to use SIMMAP?

There are four options (see below) for generating the MRC patterns using SIMMAP or other implementations of the same method. Please select the one that may be the best one for your case.

Option 1. SIMMAP 2.0 for Windows: stand alone application with graphical user interface for 32-bit systems (may not work in new computers or Windows versions)

SIMMAP 2.0 software comes with a graphical user interface and is compiled for 32-bit systems with a Windows operative system. SIMMAP has been developed by Santiago Saura. SIMMAP version 2.0 was compiled in year 2002, and it is therefore an "old" compilation that may not work in new computers or Windows versions.

          Download SIMMAP 2.0Auto-Installation file: Download Software

Run this file in a 32-bit computer with a Windows operative system and SIMMAP 2.0 will be installed.

Note that a point (and not a comma) should be set as your decimal separator in the regional configuration settings of your computer when using SIMMAP. Note that this auto-installation file does not include the user's manual and the related paper (find above the links for downloading these two documents as pdf files).

It is possible to run SIMMAP 2.0 in a computer with 64 bits and Windows 7 by first installing the Windows Virtual PC and Windows XP Mode. See http://windows.microsoft.com/en-us/windows7/install-and-use-windows-xp-mode-in-windows-7 and http://www.microsoft.com/en-us/download/details.aspx?id=3702, among other related websites.

Option 2. C++ source codes for SIMMAP 2.0   

If you have enough programming skills, or have someone in your team that could help, you may use the original C++ source codes of SIMMAP 2.0 to compile the code yourself or to adapt the part of the code that you may need for your particular application. These source codes were written by Santiago Saura and are available for download below with the condition of (i) acknowledging the source and author of the codes in any subsequent application and of (ii) informing the author of any new version or compilation of these codes that may be developed. The code is provided "as-is", without warranty of any kind. The user assumes all the responsibility for the accuracy and suitability of these codes for a specific application. Most of the code that you will need (the part related to the MRC simulations themselves) is within the file Unit1simmap.cpp available for download below.

          Download SIMMAP 2.0 source codes (C++) as a zip file: Conefor

Option 3. R programming language 

Scripts exist for R programming language that allow generating the MRC patterns that are implemented in SIMMAP. These R scripts, as they are available so far, include most but not all the functionalities of SIMMAP 2.0. This however might be sufficient for many users. These R scripts have been developed by Murray Efford (University of Auckland, New Zealand).

The secr (spatially explicit capture-recapture) package for R includes the randomHabitat function that generates the MRC patterns of SIMMAP. This package can be downloaded from http://cran.r-project.org/web/packages/secr/index.html. This package has been developed by Murray Efford (University of Auckland, New Zealand).

A slightly modified version of the R script in the randomHabitat implementation by Murray Efford that is less dependent from other functions in the secr package is available here (still needs to be implemented as a function but may anyway help you to generate the MRC simulated patterns within R):  MRCSimulatedHabitatPattern.txt (save this file and rename the extension from ".txt" to ".R"). Note that in this script the function 'adjacency' may need to be remaned to 'adjacent' depending on the R version you are using.

These R scripts allow controlling the main features of the MRC simulations, i.e. the amount of habitat, the degree of fragmentation and the number of pixels in the simulated patterns. These scripts however only allow generating patterns with two classes (e.g. habitat and non-habitat), with patches defined only based on the 4-neighbourhood criterion, and with a minimum mapped unit equal to 1 pixel. It might be easy to modify or generalize these codes so that they can account for some other functionalities available in SIMMAP 2.0.

Option 4. NetLogo implementation of SIMMAP

An implementation of SIMMAP (MRC method) for NetLogo (https://ccl.northwestern.edu/netlogo/) is available at http://modelingcommons.org/browse/one_model/3575#model_tabs_browse_info . The source codes for this NetLogo implementation are also available at that website.

Where has been SIMMAP or the MRC method used?

The following are several references to papers published in international peer-reviewed journals where SIMMAP and/or the modified random clusters (MRC) method have been used:

  • Duveiller, G., López-Lozano, R., Cescatti, A. 2015. Exploiting the multi-angularity of the MODIS temporal signal to identify spatially homogeneous vegetation cover: A demonstration for agricultural monitoring applications. Remote Sensing of Environment 166: 61–77.
  • Perry, G.L.W., Wilmshurst, J.M., Ogden, J., Enright, N.L. 2015. Exotic mammals and invasive plants alter fire-related thresholds in southern temperate forested landscapes. Ecosystems 18: 1290–1305.
  • Serra-Diaz, J.M., Scheller, R.M., Syphard, A.D., Franklin, J. 2015. Disturbance and climate microrefugia mediate tree range shifts during climate change. Landscape Ecology 30: 1039–1053.
  • Liao, J., Bogaert, J., Nijs, I. 2015. Species interactions determine the spatial mortality patterns emerging in plant communities after extreme events. Scientific Reports 11229, doi:10.1038/srep11229.
  • Almeida-Gomes, M., Prevedello, J.M., Crouzeilles, R. 2015. The use of native vegetation as a proxy for habitat may overestimate habitat availability in fragmented landscapes. Landscape Ecology (in press), DOI DOI 10.1007/s10980-015-0320-3.
  • Rubio, L., Bodin, Ö, Brotons, L., Saura, S. 2015. Connectivity conservation priorities for individual patches evaluated in the present landscape: how durable and effective are they in the long term? Ecography 38: 782-791. 
  • Savage, D., Renton, M. 2014. Requirements, design and implementation of a general model of biological invasion. Ecological Modelling 272: 394-409.
  • Zhang, G., Guhathakurta, S., Lee, S., Moore, A., Yan, L. 2014. Grid-based land-use composition and configuration optimization for watershed stormwater management. Water Resources Management 28: 2867–2883.
  • Kennedy, C.M., Lonsdorf, E., Neel, M.C. et al. 2013. A global quantitative synthesis of local and landscape effects on wild bee pollinators in agroecosystems. Ecology Letters 16: 584-599.
  • Efford, M.G., Fewster, R.M. 2013. Estimating population size by spatially explicit capture–recapture. Oikos 122: 918-928.
  • Perry, G.L.W., Wilmshurst, J.M., McGlone, M.S., McWethy, D.B., Whitlock, C. 2012. Explaining fire-driven landscape transformation during the Initial Burning Period of New Zealand's prehistory. Global Change Biology 18: 1609–1621.
  • Zhang, J., Gao, J. 2012. Assessing the landscape dynamics based on vector theory for a long time series. International Journal of Digital Content Technology and its Applications 6 (11): 77 - 85.
  • Rubio, L., Saura, S. 2012. Assessing the importance of individual habitat patches as irreplaceable connectivity providers: and analysis of simulated and real landscape data. Ecological Complexity 11: 28-37.
  • Chen, X., Yamaguchi, Y., Chen, J. 2011. Weighted misclassification rate: a new measure of classification error designed for landscape pattern index. Remote Sensing Letters 3: 57-65.
  • Li, X., Du, Y., Ling, F., Wu, S., Feng, Q. 2011. Using a sub-pixel mapping model to improve the accuracy of landscape pattern indices. Ecological Indicators 11: 1160-1170.
  • Díaz-Varela, E.R., Marey-Pérez, M.F., Álvarez-Álvarez, P. 2009. Use of simulated and real data to identify heterogeneity domains in scale-divergent forest landscapes. Forest Ecology and Management 258: 2490-2500.
  • Peng, J., Wang, Y., Zhang, Y., Wu, J., Li, W., Li, Y. 2010. Evaluating the effectiveness of landscape metrics in quantifying spatial patterns. Ecological Indicators 10:  217-223.
  • Shuangcheng, L., Qing, C., Jian, P., Yanglin, W. 2009. Indicating landscape fragmentation using L-Z complexity. Ecological Indicators 9: 780-790.
  • Hagen-Zanker, A. 2009. An improved Fuzzy Kappa statistic that accounts for spatial autocorrelation. International Journal of Geographical Information Science 23: 61-73.
  • Estrada-Peña, A., Acevedo, P., Ruiz-Fons, F., Gortázar, C., de la Fuente, J. 2008. Evidence of the importance of host habitat use in predicting the dilution effect of wild boar for deer exposure to Anaplasma spp. PLoS ONE 3(8): e2999.
  • Hufkens, K., Bogaert, J., Dong, Q.H., Lu, L., Huang, C.L., Ma, M.G., Che, T., Li, X., Veroustraete, F., Ceulemans, R. 2008. Impacts and uncertainties of upscaling of remote-sensing data validation for a semi-arid woodland. Journal of Arid Environments 72: 1490-1505.
  • Millington, J., Romero-Calcerrada, R., Wainwright J., Perry, G. 2008. An agent-based model of Mediterranean agricultural land-use/cover change for examining wildfire risk. Journal of Artificial Societies and Social Simulation 11: 4.
  • La Sorte, F.A., Hawkins, B.A. 2007. Range maps and species richness patterns: errors of commission and estimates of uncertainty. Ecography 30: 649-662.
  • Hagen-Zanker, A. 2009. An improved Fuzzy Kappa statistic that accounts for spatial autocorrelation. International Journal of Geographical Information Science 23: 61-73.
  • Li, X.Z., He, H.S., Bu, R.C., Wen, Q.C., Chang, Y., Hu, Y.M., Li, Y.H. 2005. The adequacy of different landscape metrics for various landscape patterns. Pattern recognition 38 (12): 2626-2638.
  • Shen, W.J., Jenerette, G.D., Wu, J.G., Gardner, R.H. 2004. Evaluating empirical scaling relations of pattern metrics with simulated landscapes. Ecography 27 (4): 459-469
  • Li, X.Z., He, H.S., Wang, X.G., Bu, R.C., Hu, Y.M., Chang, Y. 2004. Evaluating the effectiveness of neutral lanscape models to represent a real landscape. Landscape and Urban Planning 69 (1): 137-148.
  • Saura, S. 2002. Effects of minimum mapping unit on land cover data spatial configuration and composition. International Journal of Remote Sensing 23 (22): 4853-4880.
  • Saura, S., Martínez-Millán, J. 2001. Sensitivity of landscape pattern metrics to map spatial extent. Photogrammetric Engineering and Remote Sensing 67 (9): 1027-1036.