Australia's European Rabbit Invasion

When you hear the term "invasive species" you might think of a plant like the Kudzu - the vine that covers the southern US, draping over trees to generate an eerie peaked landscape and blanketing entire forests.  Or the icky zebra mussel that clings to manmade structures so well that it clogs industrial water intakes.  The Asian long-horned beetle that has munched through the forests of North America does not have many friends, even among animal lovers.  

Can something this cute really be an invasive species?
source: http://lol-rofl.com/wild-rabbit-habitat/

But what about the adorable bunny?  There is no Australian equivalent to the European bunny.  Bunnies introduced to Australia by Europeans have been ravaging the Aussie countryside for over a century.

Australians call this a "plague of rabbits"
Source: http://www.pir.sa.gov.au/biosecuritysa/nrm_biosecurity/pest_animal/pest_animal_programs/european_rabbits

When Europeans established penal colonies in Australia in 1788, they brought European culture, including an affinity for rabbit farming and sport.  Starting in 1859, the Acclimatisation Society encouraged the release of rabbits into the wild so that settlers could enjoy rabbit hunting (Crisp 2009, Kerr 2008) unaware that they had started one of the fastest colonization of any invasive species.  By 1900, the rabbits had almost spread across the entire continent.  By 1950, their population had grown to 600 million. Despite reductions in population due to disease, rabbits still outnumber humans 13:1 and have significantly impacted ecosystems throughout Australia (Crisp 2009, Kerr 2008, DEC NSW 2005).  Recent efforts to halt or reduce rabbit population growth have failed as the rabbits continues to grow in number (Zuckerman 2009.

Negative Impacts of Rabbits Invading Australia

Despite their cuteness, rabbits have devastated the native ecosystems of Australia through:
• overgrazing

• reduced plant biodiversity

• promotion of non-native plants

• suppression of trees and shrubs with resulting cascading ecosystem impacts

• increased competition with native herbivores

• increased prey for introduced cats and foxes (Cooke 2012)

• food shortage in Australia

• financial crises for Australian farmers  (Zukerman 2009)

The Causes

The spread of rabbits across Australia can be traced to failures in science, policy and management.

Scientific research provided several biological control agents that directly reduced the population (Cooke 2012).  However, experiments have not been well controlled, resulting in the accidental early release of a lethal virus into rabbit populations before experiments had been completed.  This early release prevented coordination of the subsequent dip in population with corresponding management decisions as well as the release of the disease at a suboptimal time in terms of rabbit life cycle (Kerr 2008).  Biological control is one of the more cost-effective ways of reducing populations, but even very lethal viruses will leave some survivors who are resistant to future infection (Zukermon 2009).  The best tactic is to time biological agent release with other factors and efforts.

Public policy has improved since the deliberate release of 24 rabbits by the Victorian Acclimatisation Society in 1859 so that settlers would feel more 'at home' (DEC NSW 2005).  By 1901, a special Royal Commission meeting decided on an aggressive rabbit-proof fencing project.  But recently, these fences have fallen out of favor. The Queensland Government disbanded the Board responsible for the last serviced rabbit proof fence in the country, stretching 555 km to protect 28,000 km of farm land (Crisp 2009).  Inconsistent policies have allowed rabbits time to surge back and thwart long-term management capacity.

Management failures are more difficult to pinpoint.  The failure of the rabbit-proof fencing project in 1900-1907 is most likely a failure of management since the effort had sufficient political will from the Royal Commission .  The Commission stated with urgency, "to ensure rapidity of construction, the contractor [is to] be bound to simultaneously start operations with one party working north from the railway line … another party working south from the railway line, a third party working north from Fitzgerald Inlet, and other parties from any other convenient starting points" (Crawford 2001?).  Unfortunately, by the time the fences were completed, the "rabbits had already moved into the areas being fenced off." (Zukerman 2009).  This appears to be a time when an adaptive approach would have helped so that the managers could make decisions on the spot while watching the expansion of rabbit populations.

 

The Aftermath

Today, populations are surging again and the efforts of science, policy and management must coordinate to address the problem (Williams et al. 1995).

From the scientific realm, a new biological agent would jumpstart population reduction.  Also, gaps in knowledge hinder effective rabbit population control, in particular, questions of how predators, diseases, and resource availability interact (Robley et al. 2004). 

Gosling and Mintzberg's five mindsets of a manager help to frame the management approach (2003).  In the reflective mindset, it is important to take time to understand the system, evaluate past failings, and be sure to ask the right question.  The analytic mindset helps to understand the circumstances under which widespread rabbit eradication is possible. Read et al. says it takes proper timing, sustained effort over time "significant long-term planning, resources, commitment and dedication" (2011, 52). The worldly mindset can identify the context that best lends itself to eradication, such as the optimum scale and the amount of resources needed for long-term impact.  The collaborative mindset keeps track of public opinion and involvement as well as political will.  The action mindset ensures that the project avoids "paralysis by analysis" (Sunstein 2002) and stays nimble (Meadows and Marshall 2001).

There is no central information agency with standards for population data and tracking (Zukermon 2009).

Political support is needed to provide long-term funding for both management and scientific research (Robley et al. 2004).

Conclusion

What is most needed to address rabbit populations in Australia is higher coordination between scientific discovery, legislative support, and management techniques reduce populations and keep them low.  A central repository of scientific knowledge coordination, management that follows the five mindsets and consistent political support would go a long way in addressing this issue.

References

Anonymous. 1917. Rabbits in Australia. Journal of Geography 16, 31. Retrieved from http://search.proquest.com/docview/1290516993?accountid=10267

Cooke, B.D. 2012. Rabbits: manageable environmental pests or participants in new Australian ecosystems? Wildlife Research 39 (April): 279-289.

Crawford, J.S. 2001? History of the State Vermin Barrier Fences. The State Barrier Fence of Western Australia: Centenery 1901-2001. Accessed December 6. URL: http://pandora.nla.gov.au/pan/43156/20040709-0000/agspsrv34.agric.wa.gov.au/programs/app/barrier/history/Crawford_Rcommission.htm

Crisp, R. 2009. Run Rabbit, Run Rabbit, Run, Run, Run. AQ: Australian Quarterly 81, 5 (September-October): 36-37

Department of Environment and Conservation New South Wales (DEC NSW). 2005. Rabbits fact sheet. Pest Management in NSW national parks (December). URL: http://www.environment.nsw.gov.au/resources/pestsweeds/factsheetRabbits.pdf

Gosling, J. and Mintzberg, H. 2003. The Five Minds of a Manager. Harvard Business Review (November): 54-63.

Kerr, P. 2008. Biocontrol of rabbits in Australia. Outlooks on Pest Management (August): 184-188.

Meadows, D., and Marshall, P. 2001. Dancing with systems. Whole Earth (winter): 58-63.

Read, J.L., Moseby K.E., Briffa, J. Kilpatrick, A.D., and Freeman, A. 2011. Eradication of rabbits from landscape scale exclosures: pipedream or possibility? Ecological Society of Australia 12, 1 (April): 46-53.

Robley, A., Reddiex, B., Arthur, T., Pech, R., and Forsyth, D. 2004. Interactions between feral cats, foxes, native carnivores and rabbits in Australia. Final report to the Department of Environment and Heritage, Department for Sustainability and Environment, Melbourne, Vic.

Sunstein, C.R. 2002-2003. The Paralyzing Principle. Regulation (Winter): 32-37.

Williams, K., Parer, I., Coman, B., Burley, J., and Braysher, M. 1995. Managing vertebrate pests: rabbits. Australian Government Publishing Service: Canberra.

Zukerman, W. 2009. Australia's Battle with the Bunny. ABC Science (8 April). URL: http://www.abc.net.au/science/articles/2009/04/08/2538860.htm

Opposition to Agenda 21 and What You Can Do

How does a nonbinding sustainability framework become, for some, a symbol of the loss of both individual rights and private property?

Agenda 21 is a nonbinding international agreement made at the United Nations (UN) Conference on Environment and Development in Rio de Janeiro in 1992.  In most countries it is simply a guideline to help countries make a more sustainable world.  But in the U.S., Agenda 21 is a term to be avoided, due to its more loaded interpretation by the Tea Party.  

In the U.S., it is common for Tea Partyists to disagree with environmentalists and planners, and for this disagreement to result in standstill.  In the interest of becoming a more empathic planner, and a more effective communicator, I have been curious about how to understand the Tea Party perspective.  Here I will explore the ideas behind the Tea Party interpretation of Agenda 21, the popularity of this opposition in the U.S., its impacts, the Tea Party mentality, and what environmental planners can do to engage the opposition constructively.

Opposition to Agenda 21

The dialogue of the opposition to Agenda 21 includes theories that extrapolate concerns about Agenda 21's intentions and implementation into dystopic futures.  Sound bites such as "pack'em and stack'em" enumerate the fear of suburbanites losing their way of life when they are forced to move into high rise buildings downtown (Lenz 2012).  Fear of this dystopia bleeds over into opposition to bicycle paths, public transit, and even into any initiative that has sustainable terms, including more social terms such as "equity" (Koire 2011).  

The opposition to Agenda 21 stems from a dislike of big government and defense of private property and individual rights (CBS 2010a, CBS 2010b).  It manifests as anti-global governance, such as the UN, and an association of green initiatives with socialism, fascism, or communism.  The debate plays out in a battle between the Tea Party and environmental governance institutions.  At the heart of this battle is the U.S. office of ICLEI, the UN's partner NGO for local Agenda 21 support (Clabough 2010, Koire 2011).  

Popularity of the Opposition in the U.S.

Agenda 21 oppositionists and Tea Party members are minorities in the U.S (APA 2012).  The majority of Americans oppose the Tea Party, 14% support them but are not active, and only 4% of Americans are active members of the Tea Party, having either donated or attended a Tea Party event (CBS 2010).  These active members constitute over 12 million people who have been highly instrumental despite their minority status in reframing the conversation around local environmental governance.

 

Impacts of Agenda 21 Opposition

As a result of the Tea Party's pressure, 47 bills have been introduced at the state level against Agenda 21 (Schonerd 2013).  Five of these have passed (Schonerd 2013), though in Missouri, the governor vetoed the bill (Jost 2013).  An account of the change in ICLEI memberships across the country reveals the influence of Tea Party activism as well.  Of the at least 715 local governments who have at one time been members of ICLEI, only 505 hold memberships today (this is a minimum based on analyzing memberships in 2010, 2012, and 2013 from icleiusa.org).  Withdrawn memberships have occurred across the country in forty states.

The Tea Party

The Tea Party first formed in 2008 after the downturn, but gelled after a large protest on September 12, 2009.  Today, several organizations constitute the Tea Party, and each has its own nuances.  

Three groups are instrumental in the anti-Agenda 21 rhetoric: FreedomWorks, the Tea Party Patriots, and the John Birch Society.  The Koch brothers founded FreedomWorks in 2004, and this organization now runs training events, conferences, research, and media production for many of the other Tea Party groups on top of maintaining a large membership (Clabough 2010).  FreedomWorks was originally credited with founding the Tea Party itself (CSE 2002).  The John Birch Society has been active since 1958 but is now resurging on the tails of the Tea Party (Hurghart and Zezkind 2011).  The Tea Party Patriots is one of the grass roots Tea Party organizations, formed by three ordinary citizens who were fed up with the way America was being run.  It now has a wide membership (IREHR 2010). 

These organizations find leadership on the opposition to Agenda 21 through members of the U.S. Government, including Senator Ted Cruz (TX R) and Representative Michele Bachmann (MN R) (Cruz 2013).  Glenn Beck provides much of the information for the movement through his radio talk show, his magazine The Blaze, and even a novel he co-authored.  Rosa Koire has a novel of her own, and encourages Tea Party members to distribute her flyers, be active in local government, encourage their local government to withdraw ICLEI membership, and to oppose any initiatives that contain certain Agenda 21 buzzwords (Koire 2011).

Foundations of the Tea Party

At heart, the Tea Party is about smaller government and individual rights regarding property ownership.  The core of the Tea Party does not center around a common ground regarding environmental issues.  When surveyed, Tea Partyists are much more aligned about questions of government size than on climate change, for example.

Understanding the Tea Party

One way to understand the Tea Party is through an interesting framework established by psychologist Jonathan Haidt that measures the moral reasoning of individuals.  In 2011, Haidt identified five moral intuitions: care, fairness, loyalty, authority, and purity.

Using Haidt's five moral intuitions, Wojcik compared Tea Party members with members of other parties (Haidt and Graham 2007).  Wojcik found that Tea Party members utilized the five moral intuitions more evenly, much like conservatives and libertarians.

This makes it difficult for liberals to understand and communicate with Tea Partyists because they primarily use just two, care and fairness (2010; 2011a).  Wojcik also found that Tea Party supporters on average have a lower "Need for Cognition," defined as an innate attraction to tasks that are intellectually challenging (Mussell 2010).  A low need for cognition is correlated with a greater halo effect and high social anxiety (Petty et al. 2009, Osberg 1987).  On average, Tea Party supporters also have a higher need for social acceptance according to the Marlowe-Crowne Social Desirability Scale (Crowne and Marlowe 1960; Wojcik 2011b).  These differences reveal basic underlying ways of seeing the world, reasoning, and making value judgements that hinder communication across the political divide.

How to Engage the Tea Party

When determining how best to engage the Tea Party, environmental planners have two choices: they can focus more on short-term or on long-term effects. 

The short-term path involves marginalization, combating, and proselytizing Tea Party members.  However, this path can have high costs because negative reactions can feed the flames of subversive groups, increasing antagonistic membership and activism (Banerjee 2013).  The alternative is a more long-term approach that focuses on learning and understanding through participation.  This approach assumes that there is some collaborative benefit of working with those opposed to Agenda 21 rather than fighting, which is often the case for environmental conflict at the local level (Forester 2009).  At the grass roots level, the opposition to Agenda 21 does not come from a dislike of environmental issues per se, and therein lies the possibility for coordination between environmental advocates and the anti-Agenda 21 supporters.

In order to take advantage of the second approach, environmental planners must be able to recognize that there are disadvantages to Agenda 21 and that there are times when the Tea Party is right.   They must consider the perspective of Tea Party members in their interactions with the public.  This approach requires training in environmental conflict management, time and resources for the participatory process, and the courage to be open to a more bottom-up method that is not controlled from the top.  This approach has the potential benefits of reducing oppositional posturing, increasing the effectiveness of local government, and improving outcomes.

Building trust, and finding areas of common ground are two crucial factors when engaging any groups that have a history of disagreement.  If environmentalists are to engage Tea Partyists, they must do the same.  

Trust building is based on developing relationships, and this can only happen if there is dialogue between groups as well as some transparency.  Therefore, environmentalists need to get out there and talk with (no, make that listen to) more people that are on the other side of the political spectrum.

Potential areas of overlap include the principal of subsidiarity, which is the legalese term for using the smallest size governing body that is effective to get something done (i.e. if it can be done at the local level, it should be done there, and not at the national level).  Using smaller-scale governments is often recognized by both groups, in principle, though this can change depending if the larger scale is aligning with the views of the group on any particular issue.

This post is based on material from a presentation and paper from Dec 2013 by Jennifer Rae Pierce at the Intersection of Crisis and Transition conference held by the Department of Environmental Science and Policy at Central European University, in Budapest, Hungary.

References

American Planning Association (APA). 2012. Planning in America: perceptions and priorities. Making Great Communities Happen (June).

Banerjee, T. 2013. Media, Movements and mobilization: Tea Party protests in the U.S., 2009-2010. In Research in Social Movements, Conflicts and Change, Volume 35. Ed. P.G. Coy.

Burghart, D. and Zezkind, L. 2011. Special report: Freedomworks and the John Birch Society problem. IREHR. http://www.irehr.org/issue-areas/tea-party-nationalism/tea-party-news-and-analysis/item/299-special-report-freedomworks-and-the-john-birch-society-problem

CBS News/New York Times (CBS). 2010a. The Tea Party movement: what they think. Poll (April 14). http://www.cbsnews.com/htdocs/pdf/poll_tea_party_041410.pdf

---. 2010b. The Tea Party movement: who they are. Poll (April 14). http://www.cbsnews.com/htdocs/pdf/poll_tea_party_who_they_are_041410.pdf

Citizens for a Sound Economy (CSE). 2002. U.S. Tea Party. Internet Archive WaybackMachine (September 13). URL: http://web.archive.org/web/20020913052026/http://www.usteaparty.com/

Clabough, R. 2010. Beck Closely Examines Tea Party Movement. The New American (August 1). URL: http://www.thenewamerican.com/usnews/politics/item/3259-beck-closely-examines-tea-party-movement

Crowne, D. P., & Marlowe, D. (1960). A new scale of social desirability independent of psychopathology. Journal of Consulting Psychology, 24, 349-354.

Cruz, T. 2013. Glenn talks to Senator Ted Cruz for the first time since marathon 21-hour anti-Obamacare speech. The Blaze TV (September 27). Video. 13:34. URL: www.glennbeck.com/2013/09/27/glenn-talks-to-sen-ted-cruz-for-the-first-time-since-marathon-21-hour-anti-obamacare-speech

Forester, J.  2009.  Dealing with differences : dramas of mediating public disputes.  Oxford ; New York :  Oxford University Press

Haidt, J. and Graham, J. 2007. When morality opposes justice: conservatives have moral intuitions that liberals may not recognize. Social Justice Research 20, 1 (March): 98-116.

Institute for Research and Education on Human Rights (IREHR). 2010. Tea Party Nationalism. Special Report (Fall). pdf. URL: http://www.irehr.org/news/special-reports/item/443

Iyer, R. 2013. The moral foundations of environmentalists. YourMorals Blog (April 4). URL: http://www.yourmorals.org/blog/2013/04/the-moral-foundations-of-environmentalists/

Jost, A. 2013.  Rowland pushes for veto override of bill banning Agenda 21. The Missouri Times (July 23). URL: http://multistate.com/insider/?p=615

Koire, R. 2011. Agenda 21: The United Nations Plan for Global Control. Tea Party Television (June 24). Video. URL: http://fellowshipoftheminds.com/2011/06/24/lesbian-democrat-lays-it-on-the-line-about-agenda-21/

Lenz, R. 2012. Antigovernment conspiracy theorists rail against UN's Agenda 21 program. Intelligence Report 145 (Spring).

Mussell, Patrick (2010). Epistemic curiosity and related constructs: Lacking evidence of discriminant validity. Personality and Individual Differences 49 (5): 506–510.

Osberg, Timothy M. (1987). The Convergent and Discriminant Validity of the Need for Cognition Scale. Journal of Personality Assessment 3 (3): 441–450.

Petty, Richard E.; Briñol, P; Loersch, C.; McCaslin, M.J. (2009). Chapter 21: The Need for Cognition. In Leary, Mark R. & Hoyle, Rick H. Handbook of Individual Differences in Social behavior. New York/London: The Guildford Press. pp. 318–329.

Schonerd, D. 2013. Agenda 21: State Legislative Scorecard. Multistate Insider (May 16). URL: http://multistate.com/insider/?p=615

Wojcik, S. 2010. A moral profile of Tea Party supporters. YourMorals Blog (October 16). URL: http://www.yourmorals.org/blog/2010/10/a-moral-profile-of-tea-party-supporters/

Wojcik, S. 2011a. Tea for two: the split personality of the tea party. YourMorals Blog (February 9). URL: http://www.yourmorals.org/blog/2011/02/tea-for-two-the-split-personality-of-the-tea-party/

Wojcik, S. 2011b. The Tea Party and compromise. YourMorals Blog (October 13). URL: http://www.yourmorals.org/blog/2011/10/the-tea-party-and-compromise/

How to Build A Climate Diagram in R

Walter & Leith climate diagrams illustrate precipitation and temperature changes throughout the year in one standardized chart.  They are especially useful to determine water stress or other significant climatic factors on plants.  This step-by-step guide teaches how to generate your own Walter & Leith climate diagram using the software called R.  Here are some samples that I made for several places in the Andean highlands following this process:

This climate diagram of Juliaca shows water stress from May to April, and excess water in January and February.  The red line is temperature, measured on the left axis.  The purple line is precipitation, measured on the right axis.  The x-axis is one year, measured in months, from January to December.  Below are the same diagram for four other locations in the Andes.  They show the wide variation in climate found in this bioregion.

R is a command-line based open source software that is hugely flexible for computations and quantitative visuals. This post assumes basic knowledge of R. If you don't know anything about R, see this post for an introduction and links to more information. You will also need basic working knowledge of excel or similar spreadsheet software. I find that OpenOffice's spreadsheet software works even better than Excel and it's open source to boot.

For any more advanced R users reading this post, there are definitely more efficient ways to do this task. I put together this tutorial for a 2-hour introductory workshop with people who had never used the software before, so I chose to make it as simple as possible. Please feel free to do this your way, and to post comments with any recommendations.

Downloads you will need:

In this tutorial we will use the climatol package. Its description and guide can be found here:
http://cran.r-project.org/web/packages/climatol/index.html

http://webs.ono.com/climatol/climatol-guide.pdf

You will need to download this package, using R, prior to starting this process.

Part 1: Gathering Data

Before starting, have this data on hand for your location of interest:

1. Name of the location.
2. Elevation of the location in meters above sea level.
3. The range of years that the climate data was collected.
4. The monthly climate data as per the table below (Abs min t is optional).

Part 2: Preparing the data in a spreadsheet

1. Create a spreadsheet with the following information in the upper left-hand corner of the file (replace the numbers to match your data - this is just an example).

Precip = average precipitation in mm per month
Max temp = maximum average temperature per month in ºC
Min temp = minimum average temperature per month in ºC
Abs min t = daily minimum temperature per month in ºC.

If you do not have the abs min t data, simply copy the minimum temperature data from the column above, but do not leave the cells blank.

2. Delete the first column in excel so that only the data remains. Be sure to delete any extra text and formatting anywhere in the file.

3. Save the file as a .csv

Part 3: Working in R

1. In R, use the Package drop down menu to install the climatol package. You will need an intenet connection to do this.

2. Attach the installed package to your session using this command:

>require(climatol)

3. Set the working directory to the folder where you keep your files using the Misc drop down menu. Verify it with this command, which should return the file path to the directory you selected in the drop down command.

> getwd()

4. Load and assign a name to your data:

> name=read.csv("folder/subfolder/datafile.csv")

5. View your data and check for anything unexpected. Reload as needed.

>name

COMMON ERROR: If you see anything strange here, then go back to step 2 and delete rows and columns that are adjacent to your data (even if they are blank). Be sure to put your cursor in cell A1, which should be the upper left-hand corner of your table, before saving.  You can also try a "paste special" of "values" only into a new spreadsheet and resaving.  If it still isn't normal-looking, then try opening your spreadsheet in Openoffice (it's free) and converting into .csv from there, with no extra formatting.

6. Create the plot (this returns in a new window):

> diagwl(name, est="Location",alt=elevation, per="dates", mlab="en")

COMMON ERROR: If you do not see a window with the graph open up, select the button that looks like a bar graph at the top of the R window, and try the command again, using the up arrow.

7. Adjust the colors if you like, using the color chart pdf:

> diagwl(name,est="Location",alt=elevation,per="dates",mlab="en",pcol="#color1",sfcol="#color2")

These are the color parameter names, with their default colors:

pcol Color pen for precipitation ("#005ac8").
Tcol Color pen for temperature ("#e81800").
Pfcol Fill color for probable frosts ("#79e6e8").
Sfcol Fill color for sure frosts ("#09a0d1").

8. Assign a name to the plot:

> plotname=diagwl(name,est="Location",alt=elevation,per="dates",mlab="en",
pcol="#color1",sfcol="#color2")

9. Generate a plot file of the plot in your working directory as .eps:

For an .eps file, use this string of commands:

> postscript("plotname.eps", horizontal = FALSE, onefile = FALSE, paper = "special", height = 10, width = 10)
> setEPS()
> postscript("plotname.eps")
> diagwl(name,est="Location",alt=elevation,per="dates",mlab="en",
pcol="#color1",sfcol="#color2")
> dev.off()


10. Repeat from step 4 to create any additional diagrams while in the same session.

I originally developed this tutorial as part of a workshop for graduate students in Environmental Science and Policy at Central European University in Budapest, Hungary.

Introduction to R Software

This introductory post on R software is just enough to get you started so that the other posts on R don't need to repeat the same information at the beginning each time.  It was developed in collaboration with Thomas Pienkowski.

What is R?

R is a free, open source software that uses a command interface to interact with the user.  Outputs include strings of text within the command line as well as graphical outputs in a separate window.  It has some basic commands that are supplemented by packages developed by third parties and individually downloaded and installed, like plugins or apps.   

What is an R Session?

Each session in the R Console starts from the beginning.  Strings of commands are stored in R studio or some other separate program.  A session ends when you close the R window.  This means that each time you restart R, you must re-attach any packages and insert the string of commands that you have worked out thus far.  This may seem like a major pain, but actually much of the time in R is spent working out the commands, not entering them.

Packages:

Packages exist in one of three sequential states in relation to your use of R:

1.     A developed package that is not yet on your computer, but exists

2.     A package that has been installed on your computer and is available to be attached during each session

3.     A package that has been attached to your particular session and is ready for use

Commands:

Commands are typed by you in the command line.  A command line always starts with > and then has a command name followed by parameters enclosed in ( ) and separated by commas.  Names are sometimes enclosed in " ".   

For example:

> plot ( "x", height = 10, width = 10)

Hit "return" to enter a command.  The program will read the command and then do one of four things:

1.     Return requested information below the command line, which may involved performing a task or calculation

2.     Perform a task visualized in another window (usually a graph)

3.     Perform a task, but not return any information in the command line

4.     Return an error message

To go through commands you have previously typed in your session, use the up arrow.

To create your own "uniquename" for a data set (this is called an assignment), or even for a command performed to a data set:

> uniquename = command

Note that R commands are case-sensitive, so X is different from x.  Spaces within the command line do not matter.

Commands can also pull information from a subset of an object or dataset.  To do this, use [ ]  For example, in the case of a matrix (or spreadsheet or table) of data called X, typing: > X[1,2] will return the data in the first row, second column.

Helpful Commands:

To get help on the function of a command X:

> ??X

To return command names that contain X:

> apropos("X")

To see the color options:

> colors()

To see installed packages:

> library()

To install packages, use the dropdown menu called "Packages and Data," and select "Package Installer," or use this command to install package X:

> install.packages("X")

To see a list of the objects in your current session:

> ls()

To view basic information on a dataset X:

>summary(X)

Resources:

Download R to install on your computer.

http://www.r-project.org/

Good self-learning introduction:

http://www.burns-stat.com/documents/tutorials/impatient-r/#packages

A place to explore packages:

http://crantastic.org

Where to find packages to install:

http://cran.r-project.org

To find packages that help R interface with other software, such as Google Earth:

http://www.omegahat.org/

PDF color chart:

http://research.stowers-institute.org/efg/R/Color/Chart/

What is your favorite R resource?  Feel free to share it below.

 

Factors Influencing ICLEI Membership

What makes an ICLEI member city?

ICLEI – Local Governments for Sustainability, is an international NGO who works with local institutions, primarily municipalities, to support the implementation of sustainable goals.  One of the primary mechanisms that ICLEI uses to implement its programs is through official membership, for which they charge a small yearly fee.  In return, members gain access to grant opportunities, international recognition, publications, workshops, and opportunities to participate in sustainability programs.  The ICLEI website lists 1173 local governments and associated entities as current members, representing 81 countries.  947 of these members are local municipalities (cities, towns, etc.) and the other 226 are city networks, nonprofits, and county or regional governments. With the ICLEI membership being so vital to ICLEI as an organization, and often serving as a primary support vehicle for the implementation of the UN’s Agenda 21, this analysis seeks to determine whether there are correlative properties that serve as country-level indicators for city membership in ICLEI.

Methodology

My analysis utilizes the list of members available here on ICLEI’s website.  I then separated cities, villages, towns, and other small governments from regional or county level governments, networks, NGOs and other such groups.  As a rough indicator of the level of participation of cities in each country, I used the number of city members divided by country population, resulting in numbers from just over 3 per million citizens to zero.  Zero indicates countries that did not have small government members, but only had regional or other types of members instead (see figure below).  The two top scoring countries, the Maldives and Iceland, each have relatively small populations and contain one city that is a member and that also accounts for about a third of their total populations.  Subsequent countries have less extreme ratios of member cities to total population.

I then looked at 4 potential country-level indicators for city membership, including (1) the presence of an ICLEI regional office in the country, (2) Kyoto protocol signatories, (3) GDP per capita, and (4) the GINI index (a common measure of equity defined by the World Bank).  I will explain here why I selected each variable.

(1) The ICLEI website indicates regional offices in South Africa, Canada, the United States, Germany, Japan, Korea, Brazil, Mexico, Australia, India, and the Philippines.  These 11 countries account for 850 memberships – nearly 75% of total memberships (529 of these are in the U.S.)  I hypothesized that having a regional office in country would correlate with higher membership rates due to stronger ICLEI networks in countries with ICLEI staff.  (source: http://www.iclei.org/index.php?id=global-contact-us)

(2) I used signing the Kyoto protocol as a rough indicator of how environmentally mindful the national government was in a particular country (see somewhat out-of-date map below to get an idea).  I did not take into account ratification status, since nearly every country in this analysis has ratified other than Canada and the U.S.  This variable could result in a higher likelihood of membership due to the environmental leanings of the national government being a reflection of the views of citizenry.  Alternatively, however, it is possible that local governments in countries that had not signed the Kyoto protocol would need the use ICLEI’s services more, due to lack of national government support.  This would generate a negative correlation between the two variables.  Either way, I expected this variable to have some effect on membership rates. (source: http://en.wikipedia.org/wiki/List_of_parties_to_the_Kyoto_Protocol)

(3) GDP per capita serves as a measure of country wealth, and often as an indicator of citizen interest in environmental measures, especially due to the impression that sustainable activities reduce economic prosperity.  I expected GDP to correlate positively with membership. (source: http://data.worldbank.org/indicator/NY.GDP.PCAP.CD/countries)

(4) The GINI index measures the disparity between the wealthiest and the poorest in a country.  This can be used as another type of measurement for country prosperity, and I expected it to correlate positively with membership. (source: http://data.worldbank.org/indicator/SI.POV.GINI)

In plain English, I expected that member cities would be more likely in countries that contained a regional ICLEI office, had higher GDP, and higher income equality.  I wasn’t sure whether being Kyoto signatories would encourage or decrease membership rates, but expected there to be some effect.  I did not expect these indicators to correlate to a high degree with membership due to the complexities inherent in making the decision to join ICLEI, but expected to find some predictably of results.

World Map of Kyoto signatories and ratification status. 
Many more countries have since ratified the treaty, including Australia, Turkey, and over 20 others.
(photo source: morriscourse.com)

For the sake of simplicity, I used country-level information, since there were only 81 countries but over 900 cities.  Further investigations could look at city-level indicators in order to find potential greater correlation values and could take into account other variables such as municipality size or budget.

For the actual analysis, I utilized the freeware R, and performed a multiple linear regression on the data.

Results

After performing the multiple linear regression analysis, which uses the computer to perform calculus on the four variables to determine if any of them correlate with ICLEI membership, I found that the most correlative descriptor was GDP/capita.  Even this was not a big predictor, and could account only for a 1.32% increase in memberships per million people for each US$1,000 increase in GDP/capita.  Surprisingly, hosting an ICLEI office and the GINI index were not statistically significant factors.  I ended up dropping the GINI index from the analysis altogether since it was not helping overall accuracy of the results (read: R-squared values decreased).  Signing the Kyoto protocol had a slight negative correlation, but not one significant enough to account for much.

For those who prefer to read the statistics, here are the base results given by R:

Residuals:

     Min                 1Q         Median            3Q             Max

-0.82992    -0.40870    -0.15950     0.08209    2.66220

Coefficients:

                                           Estimate        Std. Error        t value        Pr(>|t|)   

(y-Intercept)                  4.047e-01      1.404e-01         2.882        0.00512

ICLEI office                     2.597e-02      2.373e-01         0.109        0.91315

Kyoto Signatory          -3.164e-01      1.646e-01       -1.922        0.05833

GDP/capita (US$1)      1.326e-05      3.846e-06         3.448        0.00092

Residual standard error: 0.7257 on 77 degrees of freedom

Multiple R-squared: 0.1597

Adjusted R-squared: 0.127

F-statistic: 4.879 on 3 and 77 DF

p-value: 0.003694

In an effort to find stronger correlations, I tried using all memberships rather than just cities, and various variables transformations.  None of these manipulations resulted in any headway on answering the question at hand.

Conclusions

I was so sure that hosting an ICLEI office would have a positive correlation with memberships that I would caution using these results without further analysis.  Purely looking at membership numbers would suggest such a trend, but it may be that higher GDP is in fact a stronger correlation.  These findings suggest that ICLEI memberships are more difficult to predict than I had originally anticipated.  This indicates that while it may be easier to gain memberships in wealthier countries, this is not a strong correlation, and is much weaker than might have been realized.  Thus far, it does not appear that there is a shortcut that can aid in gaining memberships more quickly.  This may also indicate that the globe is indeed pulling together, at least in cities, to work on global issues, and is not as divided as the Kyoto protocol or income inequalities might indicate.

Future analyses may want to pursue energy sources or climate impact as possible drivers for membership as well.  Please send me your comments on what other options could be explored, or to request the raw data.