“Like walking through an open cemetery”

“I have been working in human-modified tropical forests for the past 14 years, but seeing these fires first hand was devastating,” wrote Erika responding to one of my questions “The smell of wet soil was gone and I could only smell smoke…even the usual cacophony of forest sounds disappeared…it was like walking through an open cemetery.”

Erika de Berengeur Cesar, an up and coming Brazilian forest researcher, works at Lancaster University. For last two months, she has been slogging away in the field collecting data for her team’s project on human-modified forests. But this year hasn’t gone to plan. This isn’t a case of bad planning though, as with so may projects – 7 of her 20 sites had burned in some of the most widespread fires in recent times. After seeing her tweeting about this, I thought “I need to write something about this. It feels important.” So we fired a few tweets and emails back and forth, with Erika fitting answering my questions between her days in the field. After I had waited impatiently for a couple of days, Erika messaged me:

“Sorry, trying not to work weekends…not going very well though…Today I just learned that 9 of my 20 plots have burned.” 2 more plots. Aside from the wider situation, this was the stuff of researcher’s nightmares.

B260 T5 - Before and after the fire.1
Erika de Berengeur Cesar in one of her logged plots before it burned (top) and the same plot after recent fires (bottom). Photo courtesy of Erika.

Fires in Brazil reached record levels in 2015, with more than a quarter of a million separate fires recorded. However, these fires are not generally ‘natural’ – “Fires in the region always have a human ignition source.” Erika told me “They are used in slash-and-burn agriculture, to clear pastures of weeds and also to burn downed timber in newly deforested areas.” This year’s strong El Niño has caused drier conditions than normal making it “easier for agricultural fires to escape the targeted area and sweep through the forests.” Indonesia is facing a similar problem, where forests have been burned to clear space for new oil plantations, in what the Guardian’s George Monbiot  has described as the ‘greatest environmental disaster of the 21st century – so far.’

When I queried why it matters that the forest is burning, Erika was clear what the major issue is – the loss of unique biodiversity. “Every year over 100 new species are found in Amazonian forests. To see all this going up in smoke is a crime against humanity. It is a tragedy.”

“How are these fires likely to affect biodiversity?” I asked.

“The Amazon has not co-evolved with periodic fires…This means that Amazonian forests are not used to these events and…do not cope very well with it. In terms of plant communities, there is a sharp increase in the abundance of pioneer species, while high-wood density climax species disappear….Fires negatively affect…rare bird species, and the habitat specialists, such as the ant-following insectivores and the terrestrial gleaners. Overall, burned forests are significantly less diverse than their unburned counterparts.”

Amazonian forests that have burned repeatedly may eventually come to resemble more open savannahs and contain  very different species to relatively undisturbed old-growth forest.

But it’s not just biodiversity that is affected by these fires, but humans as well. In Indonesia there were evacuations of children by the navy, although some of the children, according to reports, still died from breathing difficulties . In Brazil the fires have “affected many of the local people…who reported a number of respiratory problems, such as dry cough, difficulties in breathing, and sore throats,” according to Erika. “People had to spend days building fire breaks to protect their land, instead of directly working on their crops.” People working on these farms already have a tough life as it is, without having to worry if their source of income will go up in smoke.

So what will happen to these forests in the future? Given time and, vitally, protection they can recover but Erika thinks this is unlikely “These burned forests may never recover. After the fire, several large trees die, creating a number of gaps in the forest canopy, through which more light and wind can reach the forest floor, making it drier and, as a consequence, more vulnerable to further fire events.”

The research Erika and her team are carrying out will help to answer the question of how burned forests recover but it is obvious that degraded forests, such as these, need to be seen as a greater conservation priority. More than 50% of the globe’s forests are degraded in one way or another. We cannot afford to only protect primary forests anymore.

Edit: I got an email from Erika a bit ago after I asked her what the best solution would be. I thought I should include it here:

“Funnily enough there are already quite a few good policies in place. The problem is that none is followed. For example, every year there is a ‘burning calendar’ establishing when farmers can use fire to burn their pastures or their croplands. During the peak of the dry season, the use of fire is forbidden. In 2015, given the extreme drought, some states even extended the prohibitive period. So all quite reasonable and good, right? The problem is that no one follows this rules and there is no law enforcement in place. So people carry business as usual and the forests carry on burning. To put in practice the existing laws would be the best solution.”


If you want to read more about the situation in Brazil take a look at the excellent article Erika has written  for ‘The Conversation.’

There are also a pair of videos that Erika’s team have made documenting the fires that you can see here and here.

Advertisements

How long does tropical forest take to recover from agricultural clearance?

Secondaryforest4
Intermediate secondary forest in Paragominas, Para, Brazil – Photo credit to the fantastic Ricardo Solar, you can see more of his pics here

Today our work on the recovery of secondary tropical forests got published in Royal Society Proceedings B. I’m really chuffed with this piece of work and in this blog I’m going to summarise what we found out and why I think it’s important. If you want to read the paper you can get it here.

Large areas of tropical forest have been cleared for agriculture over the last 100 years.

Why does this matter? Well it matters because these forests are vital for the unique biodiversity in the tropics but also because humans can benefit from them remaining intact.

Their loss causes extinction, release of carbon into the atmosphere – worsening climate change, and changes the ecosystem services we get from these forests.

Because of the importance of these forests their restoration is seen as a priority by some. There are valiant attempts to restore tropical forests in Brazil and various Central American countries. In addition there are also international initiatives that aim to encourage the restoration of carbon and biodiversity (E.g. CBD & REDD+). These are great and ambitious aims but, until now, we didn’t really know how long these recoveries took, or whether recovery was different for different disturbance and forest types.

To solve all this we collected the biggest dataset yet compiled on recovery of aboveground, belowground and soil carbon as well as plant species richness and community composition following agricultural clearance. All this data came from previous studies.

Carbon_pools_colour

We found that after about 80 years aboveground carbon storage was around 85% of that found in undisturbed forests, while belowground carbon storage seemed to recover more slowly. Soil carbon showed no relationship with time since clearance.

Richness_colour

In terms of biodiversity both tree and epiphyte species richness seemed to increase over time, with tree richness recovering after around 50 years since disturbance but epiphytes took around 100 years.

Prop_sp

However, when we looked at species that are found in the undisturbed forests, relatively few of them are found in the recovering forests. They didn’t seem to accumulate over time either. Given that these species are likely to be more prone to extinction it is worrying that they don’t seem to be doing very well in secondary forests.

We think that carbon recovers relatively well following abandonment of land since there tends to be a rapid influx of woody species. However, we also think that complete recovery of carbon is likely to take more than a century since this is likely to be dependent upon large, slow-growing trees.

Differences between tree species richness recovery and that of epiphytes is likely to be because tree seeds are more easily transported between forests than those of epiphytes. Also epiphytes seem to be found more on big trees, and there don’t tend to be many of these in secondary forests.

The lack of recovery of species found in undisturbed forests is perhaps the most disturbing thing that we found. We think that to improve this situation there may be a need for management of these forests by planting trees and helping to increase dispersal of seeds throughout the non-forest areas.

Disturbed forests like this are not worthless.
Regrowing forests like this are vital if we wish to conserve biodiversity in our human dominated world. Photo credit again to Ricardo Solar

There’s been lots of great work recently on the value of disturbed forests. We hope our work goes into a bit more detail where the soon-to-be-classic work of Luke Gibson etl al  left off which showed that primary forest has greater conservation value than any types of disturbed forest in the tropics. We agree with this, particularly for specialist species. However, most tropical forests are not primary forests and have been logged, cut down or burnt at some point in recent history. Because of this we think that older secondary forests need to be recognised as important for conservation and carbon storage and their clearance should be avoided. These forests are not worthless.

Knowledge gaps for urban land sharing & sparing

Photo courtesy of villes on flickr.
Photo courtesy of villes on flickr.

If you are reading this there is a good chance that you live in a city.

You’re not alone. About half of all humans now live in cities.

These cities will continue to grow for the next century and the rise of the megacity with more than 10 million souls will continue apace.

Because of this we need to think seriously about how we plan our cities so they can fulfil our needs as well as possible. They should be easy to get around, they should be a pleasant place to live and they should be as nature friendly as we can make them.

When you talk about cities and nature people often give you odd looks. “But surely all the nature is out there, in fields,” they say. They have a point. But when it comes to direct experience of nature most of us do that in cities.

Regular readers will know I have form in this area and this week a really interesting paper came out in the journal of Applied Ecology looking at the potential for using the land-sharing/land-sparing idea for urban planning.

I have to be honest that the initial thought when I saw this paper was “someone’s robbed the idea from my blog post!” After I calmed down and actually read the paper I realised that the authors had thought about it all a hell of a lot more than I had. I could hardly accuse them of stealing my ideas – after all there are a finite number of subjects out there, much like the material for jokes. With enough monkeys and enough typewriters and all that…

Anyway, the paper points out the similarities between the design of landscapes for agricultural production and urban areas, summarised below.

Sparing vs sharing urbanPanels (a) and (c) are the extreme ends of the land sparing continuum for agriculture and urban planning. Panels (b) and (d) are the land sharing ends of this spectrum.

The paper then discusses what we know about urban design in the context of the land sharing/land sparing debate. The answer is (spoiler alert!) not much.

To fix this the authors suggest 4 key areas we need more work on:

  • Understand how biodiversity reacts to urban intensification, particularly at the lower end of the scale.
  • Investigate how the shape and arrangement of fragments of habitat influences extinction risks.
  • Understand which urban ecosystems and designs are best for conserving populations and ecosystem services.
  • Undertake whole city analyses to compare between different city layouts and determine their ecological impact.

We live in an ever more crowded world, where people are shifting towards cities so we need to think about this stuff.  Go and read the paper.

 

Balancing biodiversity conservation and carbon storage

A morning view of forest in Borneo - photo credit to cknara on flickr
Morning view of forest in Borneo – photo credit to cknara on flickr

Everyone knows about the tropical forest biodiversity crisis. Agricultural conversion, logging and fire are pushing species ever closer to the precipice of extinction, while some have already plummeted over the edge. This destruction is also causing loss of carbon into the atmosphere, contributing to climate change, and changes the ecosystem services we get from these forests.

But you already knew all that, right?

What you may not know about is the Reducing Emissions from Deforestation and Degradation (REDD+) initiative (those of you who do know, feel free to skip ahead to the next paragraph). In short REDD+ is a policy championed by some that aims to reduce the potential emissions from forests, largely by paying communities that live in and around them to manage them sustainably. Of all the tools we could use to help reduce deforestation this policy has been the one to generate most hype over the last few years. I’ve lost count of the number of talks, debates and papers I’ve seen discussing it since I became a PhD student.

540x452_forest_carbon_initiative_redd
Conservation International’s interpretation of what REDD+ means

Though it seems like a generally good idea (and yes I know there are lots of caveats to this), there have been fears about its potential effects on biodiversity. There is the potential that only forests which have high carbon density would be prioritised, thereby missing out large areas with unique biodiversity. This seems stupid for a policy which targets forests, especially when these forests are home to so many unique species. Equally however, just concentrating on biodiversity might not catch areas with high carbon density.

It is here where a new(ish) paper by Chris Thomas in Ecology Letters comes in. The paper acknowledges the problems of focussing solely on one goal, and explores how you could balance the two most effectively.

To do this they used maps of carbon, along with maps of species ranges in both the Americas and UK. This is, in my opinion, a bit unrealistic since they perceive a world in which protection of carbon everywhere is considered of equal value – at the moment REDD+ is very much targeted at forests in developing countries. Nonetheless, this paper provides a few pointers on how to target this policy. Using the carbon and biodiversity maps they used the program Zonation to come up with areas considered priorities for carbon storage and biodiversity by selecting the 30% of cells with the greatest value.

Doing this for carbon solely realised peoples fears about poor protection for biodiversity. In the Americas protecting the 30% of land with highest carbon could protect nearly half the carbon stocks of the continents, but only 34% of bird biodiversity. Similarly in the UK prioritising carbon solely could protect 59% of carbon stocks, but only 25% of biodiversity.

Maps of priority areas for carbon storage in the Americas and the UK. Taken from Thomas et al 2013.
Maps of priority areas for carbon storage in the Americas and the UK. Taken from Thomas et al 2013.

The picture was similar when they targeted biodiversity only. In the Americas this would lead to protection of 71% of biodiversity, but only 30% of carbon. In the UK this would protect 93% of biodiversity but only 25% of carbon.

Maps of priority areas for biodiversity in the Americas and the UK. Taken from Thomas et al 2013.
Maps of priority areas for biodiversity in the Americas and the UK. Taken from Thomas et al 2013.

Both of these results clearly present a problem, targeting one goal does not automatically mean that you do particularly well with the other even if areas of high carbon tend to be in areas of high biodiversity.

Maps of priority areas which aim to acheive maximal protection for both carbon storage and biodiversity in the Americas and the UK. Taken from Thomas et al 2013.
Maps of priority areas which aim to achieve maximal protection for both carbon storage and biodiversity in the Americas and the UK. Taken from Thomas et al 2013.

The final analysis they did was to see how well the two goals could be achieved. By foregoing a loss of 10% of the maximum carbon in the America it was possible to maintain 91% of the maximal biodiversity value. The picture was similar in the UK where foregoing 10% of the maximum carbon meant it was possible to protect around 90% of biodiversity.

Given the previous concerns about targeting protection of carbon this paper seems remarkably positive. There seems no reason why, with careful and systematic planning, we can’t design policies to protect the two.

My criticisms of the paper still hold though, and it seems like a bit of an oversight that someone hasn’t done a similar analysis for countries which will realistically be part of REDD+ in the near future.

Having said that, we need to more analyses like this for more places – Africa and Asia, as well as Europe spring to mind, but we also need to get on and map and model ecosystem services other than carbon storage. This way we can get a better balance between managing the things we need to live and the biodiversity that underpins in. There is more to the benefits we get from ecosystems than just carbon

How bad is logging for tropical biodiversity?

How bad is logging like this for biodiversity?
(Image by flickr user Wakx)

Logging of tropical forests effects an area 10 times greater than the area converted to agriculture each year. Around 400 million hectares of tropical forest have been set aside for permanent logging – an area twice the size of Russia. Or one hundred and ninety two and a half times the size of Wales – if that’s your thing*.

Shocking, right?

But just how bad is this logging?

For starters it obviously not as bad as agricultural conversion. When land is cleared for farming all trees are removed. However,  logging is generally selective – only trees that are valuable for timber are removed, though many others can be damaged in the process. These differences between logging and agricultural conversion change the structure  of ecosystems in different ways and thus effect the species that are present in them differently.

Forest converted for agriculture  is largely dominated by generalist species. Logged forest on the other hand retains some of the conservation value of undisturbed forests. However, answering just how bad logging actually is for tropical forest biodiversity is tricky.

In the biggest study of its kind Gibson et al (2011) found that logging was the least harmful of the human impacts they investigated on tropical forest biodiversity. However, this meta-analysis brought together lots of different measures of biodiversity including, population sizes, species richness, demographics and community structure and used them to come up with a single metric. Whilst this serves to give an overall understanding of ‘forest health’ following different human disturbances, it tells us little about the general changes in particular features of biodiversity.

Effect of different disturbances on tropical forest biodiversity. Boxes represent median +/- 95% confidence intervals. Taken from Gibson et al 2011
Effect of different disturbances on tropical forest biodiversity. Boxes represent median +/- 95% confidence intervals. Taken from Gibson et al 2011

The simplest measure of biodiversity is species richness. On the whole logged forests seem to have pretty similar richness to neighbouring undisturbed forests for most taxonomic groups.  Richness is not a very useful metric though. It tells us nothing about what the species are that you find in logged forests. On one hand they could all be generalist species which are not endangered. On the other they could all be endangered species. By looking at species richness alone we have no idea about these details.

This is key to working out the conservation value of this forest since conservationists usually want to protect the rarest species to stop them from going extinct. So, how good is logged forest for these species? And do the communities resemble those of unlogged forest?

The truth is we’re not sure. Some work has suggested there is little difference in the communities and numbers of endangered species, while others suggest differently. Whatever the reality a new piece of work has found that >60% of studies on the effect of logging on community composition are flawed. The paper in Conservation Biology looked at the design of studies of logging done between 2000 and 2012 and found nearly all of them had designs that meant they couldn’t differentiate the effects of logging from the potential differences in the forests even before logging. This apparently was all down to (the dreaded) pseudoreplication.

To have a properly replicated design you need the logged and unlogged sites to be scattered throughout the landscape. However, most study sites were sampled so that all the logged sites fell in one area and all the unlogged sites in another area. This means that simply because samples are close to each other they are more likely to be similar to their respective group. In tropical forests this is a problem because species composition can change over relatively short distances.

An idealised sampling design of a study looking at community composition change in logged forests
Sampling designs of a hypothetical pseudoreplicated study and an idealised well replicated study investigating community change in logged forests

In addition few studies sampled more than one area of unlogged forest to test similarity between unlogged forest communities. The authors of the article suggest a possible way to get around this problem for some studies is to determine the relationship between plot similarity and distances between them. However, this option is second best. Properly replicated studies would give us a better idea of the effect of logging on tropical forest species.

Given how large an area has been logged, and will be logged in the near future we need to work out what’s going on with these forests. Many logging companies are open to reducing biodiversity loss so they can qualify for certification such as FSC, allowing timber to be sold at a premium price. We need partnerships with these companies, like has been done with the SAFE project and oil palm companies in Malaysia. Only by doing this will we be able to produce experimentally robust designs that allow us to draw proper conclusions about the future of tropical forest species in logged forests.

*If any US citizens want this calculating as relative to Rhode island, I did it. It’s 1273.8 Rhode islands.

What does degraded mean?

Logged forest in Perak, Malaysia. But is it degraded?  Photo crid to flickr user Wakx

The Convention on Biological Diversity (CBD) aims to restore 15% of degraded ecosystems by 2020.

This is very ambitious. Even by the CBD’s standards.

But before we get to how we’re going to raise the money to do this, where we’re going to find the manpower to do all this work and what land is a priority we need to work out what we mean by degraded.

A lot of us struggle to define what degraded really means. Don’t worry though, you’re in good company – the CBD don’t know what it means either.

When I searched in google it came up with this:

de·grade

/diˈgrād/
Verb

1. Treat or regard (someone) with contempt or disrespect.

2. Lower the character of quality of

Not very useful right?

Looking at the literature a bit further you see that there have been constant attempts to define degraded forests in particular. The latest of these is a forest which ‘delivers a reduced supply of goods and services from the given site and maintains only limited biological diversity.’ This seems like a reasonable starting point.

However, it is not particularly useful in practice.

The main problems are:

  • What do you use as a reference? In some regions it might be relatively easy to find primary forest but other areas don’t really have undisturbed forest any more.
  • How much biodiversity/ecosystem service supply do you need to lose in order for the change to count as degradation?
  • Where does forest become non-forest? Is there a sensible threshold?
  • How can we avoid savannah being classed as degraded forest?
  • What ecosystem services are we talking about here? Trade-offs are inherent in any management of ecosystem services so even relatively small changes will reduce supply of one good or another.

That’s all I could come up with at the moment. I’m sure there are more.

All of these problems, and their lack of clarity in the CBD, completely scupper this 2020 goal. Forest biomes should those be for which it is easiest to define degradation, but this hasn’t been done.

Even though I have ranted about it here I realise it is not an easy thing to do. I am not going to solve this with a blog post, which is why I’m going to pursue the topic further in my personal research.

I have a few thoughts on how to push things forward as a starter. We need to be pragmatic and we can’t have woolly definitions in important international agreements if it stops us from balancing the needs of humans with conserving biodiversity.

For what its worth I think we need to:

  • Determine reference states for all broad biomes. Only then can we really start to measure degradation.
  • Work out thresholds below which ecosystems should be classed as degraded. This will obviously have to be ecosystem specific. It could include things like magnitude of changes in carbon pools, time required to recover from disturbance or some measure of species community similarity to reference states. Species richness should not be used as a biodiversity metric because many disturbed ecosystems have higher richness that neighbouring pristine systems.
  • We must develop a means of classifying ecosystem types for use in international agreements. Though this is a difficult task as there are many transition ecosystems, we still need to do it.
  • We need to recognise that ‘reduced loss in good and services’ means nothing. If you restore arable farmland to forest you would lose food production. Is this forest then degraded farmland? Obviously not. We must define what ecosystem services we are talking about for each biome and then use these as potential indicators of degradation.
  • We need to develop indicators of degradation since we will not be able to measure everything we would like everywhere. Canopy cover and tree height have been suggested for forests, but have rarely been tested.

This list is not exhaustive by any means, but I think its a good start.

I am constantly amazed by the ability of those who come up with CBD goals to forget about how we will actually measure progress towards them. I really think this needs to change in the future. For the moment we should try to develop indicators for the 2020 goals.   Without them we will have little idea whether we’ve achieved them and what we might need to change in the future.

Is secondary tropical forest of secondary importance?

Secondary cloud forest in Ecuador. Credit to Flickr user Peter Howe.

Everyone knows about the deforestation crisis that is going on. Most of this has taken place in the tropics and the majority of it has been as a result of agricultural expansion. However, less is made of the large growth of secondary tropical forest on abandoned pastures and agricultural land. In Central America in particular secondary succession has lead to increases in forest area, largely as a result of socio-economic changes and urbanisation. These secondary forests occupy large areas, but are obviously not equivalent to relatively undisturbed primary forests.

Plenty of research has been done comparing primary and secondary forest in the tropics. Luke Gibson and colleagues gave the most comprehensive overview of the differences between secondary and primary tropical forests in their recent Nature paper. In their meta-analysis they showed that secondary forests generally had lower biodiversity value, but also that they were more valuable than most other types of degraded forest.

However, this paper also oversimplified the value of tropical secondary forests.

It is widely known that secondary forests change dramatically with increasing age, and that older secondary forests are generally of greater conservation value. Firstly, secondary forests accumulate species richness of animal taxa reasonably quickly following abandonment, while plant species richness are likely to take a bit longer.

Change in species richness with time since last disturbance of secondary forest for bird and ant species. Adapted from Dunn et al 2004.

However, species richness is a rubbish measure of conservation value. It tells you nothing about the identity of the species present and therefore isn’t very useful. It can be higher in slightly disturbed forests than primary forests as a result of an influx of generalist species and a modest loss of forest specialists. So in the long run (>100 years) species richness in secondary forests should start to decrease back to levels similar to those found in primary forest.

Secondly,  the proportion of forest specialist species increases with age of secondary forest, again with vertebrate species colonising most quickly and plants logging behind. These differences are likely to be due to plants relatively limited dispersal ability.

Accumulation of old growth species in secondary tropical forests with increasing age, adapted from Chazdon et al 2009.

However, only about 50% of forest specialist plant species are present in the oldest tropical secondary forests we have records for. This poses the interesting questions about how long these communities take to recover, and whether they will ever reach similarity to primary forests.

Finally, secondary forest vertebrate communities may converge with those of primary forests after about 150 years. I have a few problems with the paper this analysis is taken from (presented blow), but at present it seems to be the best we have. I would actually argue that it suggests a relatively weak relationship between forest age and similarity, since the relationship largely depends on a few outliers amongst older forests. To get a better picture of what is going on we really need analysis which uses more secondary forests over 50 years old. This is a problem though, since most secondary forest is relatively young and it is difficult to age forest which is older than a couple of human generations.

Sorensen similarity change in vertebrate communities with increasing time since disturbance. Adapted from Dent et al 2009.

Despite the potential value of older secondary tropical forest, there is very little of it about. Much secondary forest is repeatedly cut as part of shifting agriculture and thus never develops communities characteristic of primary forest. In this way, the analysis of Gibson et al was probably correct, in that  most secondary forest is of lower value for conservation than primary forest.

However, if secondary forest is spared from conversion then it may be of great value in aiding the conservation of globally endangered species and carbon stocks in the face of expanding agriculture. Currently the greatest potential for this is likely to be in montane areas where the steep slopes make it difficult to access potential fields. However, outside of dedicated restoration projects, encouraging secondary growth will be difficult.

This is a subject I will return to in the coming weeks and months since I am currently working on a project investigating some of these issues. Meanwhile if you have anything to say,  leave a comment below.

Update: I have just gathered together the code I used to make the graphs in these posts and since I haven’t had time to write a blog post this week, I thought I’d post this instead.

Code for the Dunn et al graph:

#load in Data from Dunn et al paper
Dunn<-structure(list(Age = c(1.002, 0.9979, 0.9939, 0.9938, 5.0048, 10.9567, 10.9973, 14.0228, 17.1238, 20.9715, 51.3436, 41.19, 1.0014, 0.9973, 0.9935, 0.9931, 0.9965, 2.0012, 1.9931, 1.9997, 3.0166, 6.0685, 10.9948, 6.0292, 6.0752, 9.0246, 18.1524, 21.1961, 26.3481, 37.611, 41.4627), Species.richness = c(89.1859, 68.5023, 49.7654, 44.4118, 46.0596, 73.4128, 76.5756, 84.3176, 103.0184, 90.3269, 93.8115, 127.434, 63.148, 40.0309, 31.2711, 12.2903, 6.936, 63.02, 46.9599, 29.6817, 98.4726, 72.062, 66.1117, 105.6449, 121.2176, 149.8593, 118.0951, 80.5911, 84.9311, 115.0402, 98.718), Taxa = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Ants","Birds"),class = "factor")), .Names = c("Age","Species.richness", "Taxa"), class = "data.frame", row.names = c(NA,-31L))                                                                                                                                                                                                                                                                                                                                                                   

#load packages needed
library(ggplot2)

#graph relationship
a<-ggplot(Dunn,aes(x=Age,y=Species.richness/100,alpha=0.5,colour=Taxa))
b<-a+geom_point(size=3,shape=16)+scale_area(c(1,3))+theme_bw()
c<-b+theme(legend.position = "none")+theme(panel.grid.major = element_line(colour =NA))+theme(axis.title.x = element_text(size = 12, colour = 'black'))+theme(axis.title.y = element_text(angle=90,size = 12, colour = 'black'))
d<-c+ylab ('Species richness \nrelative to primary forest')+xlab ('Age of secondary forest (Years)')
d+xlim(0,60)+ylim(0,1.6)+geom_hline(y=1,lty=2)+stat_smooth(se=F,method="lm",formula = y ~ x+I(x^2),size=1)+coord_cartesian(xlim =c(0,55), ylim =c(0,1.6), wise = NULL)+facet_wrap(~Taxa)

#save plot
ggsave("Dunn et al 2009.png",height=3,width=6,dpi=1200)

Created by Pretty R at inside-R.org

Code for the Chazdon et al 2009 graph:

#load in data
chaz<-structure(list(Sqrt_age = c(10.0405, 6.3648, 5.5131, 5.5105, 5.0395, 4.2022, 3.9136, 3.6978, 3.1653, 2.9973, 2.8463, 2.2557, 1.7412, 5.2755, 3.8921, 2.9493, 2.2355, 2.2341, 2.2147, 0.9659, 5.0391, 3.6583, 3.2024, 2.96, 5.946, 5.0492, 4.1573, 3.6404,  3.1997, 3.4873, 2.9569, 2.8628, 2.7869, 2.257, 3.6512, 1.7506, 2.2671, 5.3014, 4.2211, 4.5083, 3.9005, 3.201, 3.1851, 2.2463, 2.0025, 2.0163, 0.9811, 2.2387, 1.9766, 4.4682, 3.287, 3.8925, 3.8793, 3.8959, 4.9149, 4.6122, 3.3359, 4.0257, 4.9985, 5.4059,  5.7681, 5.002, 6.023, 7.117, 9.0111), Proportion = c(0.8328, 0.8632, 0.8401, 0.7619, 0.7617, 0.7117, 0.713, 0.6218, 0.6002, 0.5789, 0.6017, 0.6599, 0.721, 0.5495, 0.5308, 0.502, 0.5104, 0.4705, 0.3495, 0.2594, 0.7504, 0.8013, 0.7997, 0.821, 0.611,  0.5979, 0.7302, 0.7215, 0.7185, 0.6886, 0.7285, 0.6401, 0.6415, 0.6998, 0.5918, 0.5501, 0.5503, 0.4215, 0.3713, 0.3273, 0.3271, 0.3084, 0.2871, 0.3822, 0.3594, 0.3181, 0.258, 0.1558, 0.043, 0.041, 0.1546, 0.0921, 0.1491, 0.1904, 0.2192, 0.2589, 0.2529, 0.4325, 0.4456, 0.3531, 0.2906, 0.0906, 0.191, 0.1912, 0.4922), Type = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("Flying animals", "Non-flying animals", "Trees"), class = "factor")), .Names = c("Sqrt_age", "Proportion", "Type"), class = "data.frame", row.names = c(NA, -65L))

#load ggplot2
library(ggplot2)

#graph relationship
a<-ggplot(chaz,aes(x=Sqrt_age^2,y=Proportion,alpha=0.5,colour=factor(Type)))
b<-a+geom_point(size=2)+stat_smooth(method="glm",formula = y ~ x,se=F,size=1)+scale_area()+scale_colour_manual(values=c("red","blue","orange"))
c<-b+theme_bw()+facet_wrap(~Type)
d<-c+theme(legend.position = "none")+theme(panel.grid.major = element_line(colour =NA))+theme(axis.title.x = element_text(size = 20, colour = 'black'))+theme(axis.title.y = element_text(angle=90,size = 20, colour = 'black'))
e<-d+ylab ('Proportion of \nold growth species')+xlab ('Age of secondary forest (Years)')
e+coord_cartesian(xlim=c(0,110),ylim=c(0,1))

#save plot
ggsave("Chazdon et al 2009 facet.png",height=3,width=6,dpi=1200)

Created by Pretty R at inside-R.org

Code for Dent et al 2009 graph:

#load in data
dent<-structure(list(Age = c(151.2753, 141.0416, 101.2877, 91.0569, 81.1214, 101.0649, 60.6883, 90.067, 70.3094, 65.2109, 40.6187, 23.5788, 8.3336, 9.371, 30.9226, 26.8362, 8.4707, 10.8033, 4.8007, 17.7511, 8.1344, 30.5806, 25.9007, 25.8831, 9.5432, 14.1966, 18.5549, 6.6113, 6.3277, 3.6179, 5.6787, 12.6911, 15.6025, 17.6164, 35.5596, 20.8186, 17.1589, 8.4203, 13.6423, 10.2735, 13.6131, 5.8801, 10.6768, 27.9142, 26.7288, 35.7948, 5.6885, 8.2841, 15.4354,11.6134, 25.3636, 30.4658, 26.3691, 27.9504, 24.2643, 5.4647, 5.2966, 5.2819, 8.9196, 13.1625, 14.1797, 15.9203, 8.8961, 5.5332, 5.4643, 4.6837, 25.068, 25.3471, 25.1892, 32.5845, 10.2331, 16.7896, 16.7617, 20.2606, 5.2405, 2.1346, 4.3152, 4.1426, 10.0807, 14.9214, 1.0177, 0.8349, 4.0356, 0.7206, 3.8641, 4.9122, 7.8045, 14.8184, 35.0082, 9.7931, 13.7407), Similarity = c(1.0252, 1.0216, 1.0161, 1.0147, 1.0156, 0.8468, 0.8122, 0.4842, 0.3501, 0.5844, 0.8852, 0.9285, 1.0034, 0.9032, 0.846, 0.8498, 0.8853, 0.8812, 0.8725, 0.8309, 0.8518, 0.8081, 0.8052, 0.7919, 0.8119, 0.7947, 0.7753, 0.8048, 0.8115, 0.7509, 0.7623, 0.761, 0.7526, 0.7283, 0.7052, 0.7187, 0.7138, 0.736, 0.7066, 0.7006, 0.6843, 0.6933, 0.6739, 0.6696, 0.6572, 0.6617, 0.6587, 0.6322, 0.6255, 0.6082, 0.6191, 0.6098, 0.6059, 0.5859, 0.561, 0.5996, 0.5829, 0.5718, 0.56,  0.5638, 0.5596, 0.5497, 0.5422, 0.5406, 0.4882, 0.4502, 0.5053, 0.4954, 0.4865, 0.4429, 0.4476, 0.4329, 0.4118, 0.4056, 0.4291, 0.4009, 0.3921, 0.3721, 0.3317, 0.3458, 0.3294, 0.3014, 0.2908, 0.2146, 0.1604, 0.1795, 0.1565, 0.1564, 0.1747, 0.0019, 0.00356), Similarity2 = c(0.9992, 0.9956, 0.9901, 0.9887, 0.9896, 0.8208, 0.7862, 0.4582, 0.3241, 0.5584, 0.8592, 0.9025, 0.9774, 0.8772, 0.82, 0.8238, 0.8593, 0.8552, 0.8465, 0.8049, 0.8258, 0.7821, 0.7792, 0.7659, 0.7859, 0.7687, 0.7493, 0.7788, 0.7855, 0.7249, 0.7363, 0.735, 0.7266, 0.7023, 0.6792, 0.6927, 0.6878, 0.71, 0.6806, 0.6746, 0.6583, 0.6673, 0.6479, 0.6436, 0.6312, 0.6357, 0.6327, 0.6062, 0.5995, 0.5822, 0.5931, 0.5838, 0.5799, 0.5599,  0.535, 0.5736, 0.5569, 0.5458, 0.534, 0.5378, 0.5336, 0.5237,  0.5162, 0.5146, 0.4622, 0.4242, 0.4793, 0.4694, 0.4605, 0.4169, 0.4216, 0.4069, 0.3858, 0.3796, 0.4031, 0.3749, 0.3661, 0.3461, 0.3057, 0.3198, 0.3034, 0.2754, 0.2648, 0.1886, 0.1344, 0.1535, 0.1305, 0.1304, 0.1487, 0.0019, 0.00356)), .Names = c("Age", "Similarity", "Similarity2"), class = "data.frame", row.names = c(NA, -91L))

#load ggplot2
library(ggplot2)

#graph relationship
a<-ggplot(dent,aes(x=Age,y=Similarity2,alpha=0.5))
b<-a+geom_point(size=3,shape=16,colour="red")
c<-b+scale_area(c(1,3))+theme_bw()
d<-c+theme(legend.position = "none")+theme(panel.grid.major = element_line(colour =NA))+theme(axis.title.x = element_text(size = 12, colour = 'black'))+theme(axis.title.y = element_text(angle=90,size = 12, colour = 'black'))
e<-d+ylab ('Sorensen similarity')+xlab ('Age of secondary forest (Years)')
e+xlim(0,160)+ylim(0,1)+geom_hline(y=1,lty=2)+stat_smooth(se=F,method="lm",formula = y ~ x+I(x^2),size=1)+coord_cartesian(xlim =c(0,151), ylim =c(0,1.1), wise = NULL)

#save plot
ggsave("Dent et al 2009.png",height=3,width=6,dpi=1200)

Created by Pretty R at inside-R.org