I repeat here what I suggested in another discussion going on on connectivity. The real question to me is not what free download software is best for ecological network analysis, but rather which kind of network/connectivity we are going to measure. An accepted goal of conservation is to build a conservation network that is resilient to fragmentation and environmental change. However, fragmentation is a relative concept as well as connectivity. Effective corridors should provide suitable and reliable connectivity among habitats across scales for species mobile or less mobile for gene exchange under uncertainty and change. However, often a "static" vision of landscapes is adopted (i.e. the cartography of land uses/covers) whereas landscapes (habitats included) are dynamic. Indeed, they do change either under different seasonal conditions, or under multiple driving forces like, for instance, climate change. As a result, what we are looking for, i.e. fragmentation or effective corridors, can systematically change on the map, and what is fragmented or suitable as corridor under certain conditions could not be suitable or fragmented when season, conditions or the set of focal species are changed. Just because we are not so good in predicting the future and what could be a suitable network sustaining biological diversity and gene exchange, we have to rely on past time series (at a suitable scale) to define the trajectory of every landscape piece to see whether it is predictable or not, that is, if it is persistent or not. Once you get a "predictability" map then you can think of applying different modelling tools to derive, under uncertainty; what possibly could be an effective corridor network and a suitable fragmentation for the future. So you could discover that along with "classical" green and blue ways other elements in the landscape could be crucial for the network based on their predictability. You could also discover which unpredictable landscape pieces are crucial for the maintenance of the overall connectivity in the face of climate change and try to transform them in "persistent" through planning and management efforts. See, for instance, the paper "Highlighting order and disorder in social–ecological landscapes to foster adaptive capacity and sustainability" recently appeared in Landscape Ecology. The same principle should be applied to fragmentation /connectivity for marine systems (see Modeling population connectivity by ocean currents, a graph-theoretic approach for marine conservation appeared in Landscape Ecology). Indeed, for many marine species, population connectivity is determined largely by ocean currents transporting larvae and juveniles between distant patches of suitable habitat. So, connectivity relies on the persistence of ocean currents suggesting areas that might be prioritized for marine conservation efforts and that are working like "stepping stones" in the maintenance of the overall network. On the other hand, you might identify "new" candidate stepping stone areas in case of predicted changes in the oceanic current pattern due to climate change. Unfortunately most of marine biologists and ecologists involved in marine conservation do not consider the importance of ocean currents. The problem is why we have to rely on more or less sophisticated tools for solving problems without first finding out the best way to approach the problem?

I use Gephi for network analysis. It is an open source network visualization and analysis platform (I find it much more intuitive and elegant than Cytoscape):

http://gephi.org/

I am sure there are functions UCINET offers and Gephi doesn't, but getting missing functionality implemented in Gephi might be as easy as an e-mail to their e-mailing list.

Best,

You should go for past software. freely available on internate and easy to use

I recommend using R - everything is open source and the on line help forums are extremely useful. The 'sna' package in R should do what you need

The R program is used for statistical analyzes by ecologist lately!

I will agree with Conor, Giovanni and Blerina.

R environment for statistical computing and graphics is the best choice.

You may try the package "foodweb" (http://cran.r-project.org/web/packages/foodweb/foodweb.pdf) and/or the package "NetIndices" (http://cran.r-project.org/web/packages/NetIndices/vignettes/NetIndices.pdf).

You can also use Aninhado (http://www.guimaraes.bio.br/soft.html) for statistical computing on nestedness. It is very simple.

R

Definitely R for the analyses, but Gephi is really easy for the visualisations.

For relatively simple statistical analysis PAST ver.2 is a very good solution. Not like R it is very simple and have limited st of basic functions.

If you go down the R route, there is also the cheddar package - "Cheddar provides a flexible, extendable representation of an ecological community and a range of functions for analysis and visualisation, focusing on food web, body mass and numerical abundance data. It also allows inter-web comparisons such as examining changes in community structure over environmental, temporal or spatial gradients." (cheddar package description)

I also agree with Conor, Giovanni, Blerina and Kostas Katselidis.

Go to R.

R is the way

All hail R.

R is definitely the way to go but, obviously depending on your question, you don't necessarily need a software package (other than Excel) to explore interactions in a network - simply a matter of quantifying nodes and vectors, which can easily be done by hand. You can borrow lots of information from social networks (see for example papers by DJ Watts).

R of course

I repeat here what I suggested in another discussion going on on connectivity. The real question to me is not what free download software is best for ecological network analysis, but rather which kind of network/connectivity we are going to measure. An accepted goal of conservation is to build a conservation network that is resilient to fragmentation and environmental change. However, fragmentation is a relative concept as well as connectivity. Effective corridors should provide suitable and reliable connectivity among habitats across scales for species mobile or less mobile for gene exchange under uncertainty and change. However, often a "static" vision of landscapes is adopted (i.e. the cartography of land uses/covers) whereas landscapes (habitats included) are dynamic. Indeed, they do change either under different seasonal conditions, or under multiple driving forces like, for instance, climate change. As a result, what we are looking for, i.e. fragmentation or effective corridors, can systematically change on the map, and what is fragmented or suitable as corridor under certain conditions could not be suitable or fragmented when season, conditions or the set of focal species are changed. Just because we are not so good in predicting the future and what could be a suitable network sustaining biological diversity and gene exchange, we have to rely on past time series (at a suitable scale) to define the trajectory of every landscape piece to see whether it is predictable or not, that is, if it is persistent or not. Once you get a "predictability" map then you can think of applying different modelling tools to derive, under uncertainty; what possibly could be an effective corridor network and a suitable fragmentation for the future. So you could discover that along with "classical" green and blue ways other elements in the landscape could be crucial for the network based on their predictability. You could also discover which unpredictable landscape pieces are crucial for the maintenance of the overall connectivity in the face of climate change and try to transform them in "persistent" through planning and management efforts. See, for instance, the paper "Highlighting order and disorder in social–ecological landscapes to foster adaptive capacity and sustainability" recently appeared in Landscape Ecology. The same principle should be applied to fragmentation /connectivity for marine systems (see Modeling population connectivity by ocean currents, a graph-theoretic approach for marine conservation appeared in Landscape Ecology). Indeed, for many marine species, population connectivity is determined largely by ocean currents transporting larvae and juveniles between distant patches of suitable habitat. So, connectivity relies on the persistence of ocean currents suggesting areas that might be prioritized for marine conservation efforts and that are working like "stepping stones" in the maintenance of the overall network. On the other hand, you might identify "new" candidate stepping stone areas in case of predicted changes in the oceanic current pattern due to climate change. Unfortunately most of marine biologists and ecologists involved in marine conservation do not consider the importance of ocean currents. The problem is why we have to rely on more or less sophisticated tools for solving problems without first finding out the best way to approach the problem?

I agree with Ivan Nekhaev