Is ecological succession predictable?

Over the last few years I have written quite a lot about forest succession. I have published a paper on the topic, have a paper in review about recovery of a forest under multiple stressors and will be starting more work on the it over the next few weeks. All in all, I think I have a reasonable idea what I’m talking about when it comes to succession, at least in forests. However, I’ve just read a paper on tropical forest succession that caught me a bit unawares*.

The paper in question is Natalia Norden and colleagues’ work that was recently published in PNAS. The authors collected data from 72 secondary forest plots monitored for 7-24 years at 7 different sites across tropical South and Central America. They then used this data to look whether we can predict trajectories plot stem density, basal area and species density during forest succession after total clearance. On the whole the paper found that trajectories were poorly predicted by models that looked at change as a function of forest age. From the figure below, you can pick some general trends in the direction of change with age – stem density might have a humped relationship with age for example. However, it is also clear that there is a huge amount of variation and some trajectories bounce around all over the place.

Observed successional trajectories of stem density, basal area and species density for the sites used by Norden et al.

It’s obvious from looking at the figure above that the age of a secondary forest doesn’t really act as a proxy for its successional stage. In fact Norden and colleagues found that on average age only explains 20% of within site variation. Even if that is better than the average ecology paper, it’s still not very good. To explain the rates of change of different variables, Norden et al. fitted a set of different non-linear models for each site. Again, their findings emphasised the large amount of variation between different sites. Due to these idiosyncrasies, the authors of the paper see space-for-time substitution as a flawed method for predicting the dynamics of forests. They also suggest that such approaches should not be used for studies of succession of any sort of vegetation, arguing that previous work these methods has made succession appear as if it is deterministic, and it is not.

Now I’m not sure how the numbers of studies that use chronosequences vs monitoring over time to study succession stack up, but I’d be willing to be bet >80% of these papers use chronosequences, at least in forests. There are good reasons for using them: they take much less time than monitoring (especially in systems containing long-lived organisms), they are much less expensive, the logistics are less complex and as a result of all of these things, they are easier to get funded than a 10-20 year research programme. Norden et al.’s warning against using chronosequences based on their results, begs the question “Do we have other evidence of how well chronosequences perform?” The answer is that we do, and it doesn’t look too good for chronosequences. For example, Ted Feldpausch and colleagues found that space-for-time substitution resulted in overestimates of biomass accumulation for young secondary forests in the Amazon. Recently Mora and colleagues similarly suggested that chronosequences were poor predictors of forest characteristics.

So, is the chronosequence dead? Well, maybe not just yet. However, I think as researchers we need to be more circumspect about their use. In particular I think there are 4 questions that we need to answer to get a more well rounded view of the usefulness of chronosequences:

1. How much variation in future dynamics do they actually predict? – Chronosequences are far from perfect, but it still offers us some insight into future dynamics. Mora et al. showed that chronosequences can still account for 32-57% of variance in future forest characteristics. There must be a reasonably large number of chronosequences that have been sampled more than once that could be used to test their predictive ability. We need more studies that address this head on. If it turns out that they are very poor at explaining future dynamics, then maybe it is time to switch to better methods.
2. What variables do they predict most effectively? – Structural components of a system (biomass, stem density etc) should be easier to predict than community composition, since changes in structure are less likely to depend on idiosyncrasies such as the identity of initial colonising species. However, again, this has been tested relatively rarely.
3. Do chronosequences have more predictive power in some systems than others? – Predictive power should be greatest when abiotic conditions are relatively constant across a landscape, disturbance history at all sites is relatively similar and in regions with relatively small species pools. Under all of these conditions there should be less chance of wildly different successional trajectories occurring.
4. Where do animals fit into all this? – Predicting animal abundance and community composition is rarely studied in chronosequences, probably because their response to succession is that much less predictable than plant communities. Even though they are likely to perform relatively poorly, a comparison of the predictive ability of chronosequences for animal compared to plant communities would be interesting.

What do you think? Are there any other questions we need to answer to determine the value of chronosequences? Or do you have any views on the use of chronosequences in non-forest systems?

*To be fair, this probably shouldn’t have been that much of a surprise, review papers have been suggesting that chronosequences are far from the best way to do things for a while. Although, there are also papers that suggest that careful use of chronosequences is perfectly ok.

Looking to the past for insights into tropical forest resilience

A few weeks back Lydia Cole and colleagues published a really cool paper exploring recovery rates of tropical forests. Seeing as it’s something I’ve covered a here before in relation to my work on secondary forests recovering after agricultural clearance and recovery from selective logging, I invited Lydia to write a guest post giving a different perspective to a topic I have discussed here before. Thanks to Lydia for stepping up to the plate and I hope you find her post as interesting as I did.

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Anyone reading this blog probably doesn’t need reminding of how important tropical forests are!  Birds, bees, berries and a whole load of other plants, animals and services that we probably underestimate our reliance on.  Despite the many arguments in favour of keeping tropical forests standing, vast areas continue to be deforested at rapid rates resulting in changes like that shown below (Fig 1), under pressures of expanding human population, rising consumption and the agricultural footprint to match (Geist & Lambin, 2002).

Fig 1 – Forest disturbance like logging can lead to forests such as this one in Borneo being converted from intact (left) to heavily degraded (right).

Disturbance and recovery in tropical forests Despite this widespread clearance as a result of  recent international forest conservation initiatives and rising rural-to-urban migration (Mather, 1992), some degraded tropical forests are being given a chance to recover.  But how long does it take them to recover?  Much recent research has attempted to answer this question (e.g. the great work of Chazdon et al., 2007) but little has monitored change over time scales of >50 years. Since many tropical trees have lifespans much longer than this previous studies have only captured a snap-shot of the ecological process of recovery.  In our study, we attempted to answer the question again; this time by looking into the past to gather data over longer time scales that could offer a more complete picture of forest recovery post disturbance.

The palaeoecological approach

Palaeoecology, otherwise known as long-term ecology, uses fossils to decipher how plants and animals interacted with their environment in the past.  Fossil pollen grains come in all shapes and sizes, and their morphological characteristics can be used to identify the plant family, genus or even the species to which they belong.  When a collection of these grains are identified and counted from a layer of sediment, we can reconstruct what the vegetation was like at that point in time when those grains were deposited. In our project, we were interested in studies that documented disturbance-induced changes in fossil pollen from forested communities across the Tropics, over the last 20,000 years.  Types of disturbances ranged from climatic drying events and landslides, to shifting cultivation and human-induced biomass burning.  We found 71 studies published on tropical forest palaeoecology that satisfied our selection criteria (e.g. within 23^oN/S of the equator, possessing a sufficient chronology), documenting 283 disturbance and associated recovery events.  The rate at which recovery was occurring across the different forests and disturbance events was the key variable of interest and was calculated as the percentage increase in forest pollen abundance per year relative to the pre-disturbance level.

How far and how fast have tropical forests recovered in the past?

Our results demonstrate that in the past the majority of forests regrew to less than 100% of pre-disturbance levels, prior to declining again or reaching a new baseline; the median recovery was to 95.5%.  They also recovered at a variety of speeds, ranging from rates that would lead to 95.5% regrowth in less than 10 years to those taking nearly 7,000 years; the average was 503 years.  This is significantly longer than the periods adopted by logging companies between extraction cycles!

What affects the rate of recovery?

Three of the different factors we investigated for their potential effect on the forest recovery rate seemed to be of particular importance: geographical location, disturbance type and frequency of disturbance events. Of the four key tropical regions, Central American forests recovered the fastest and those in Asia the slowest (Figs. 2 & 3).  This is concerning, given that forests in Southeast Asia are currently experiencing some of the greatest rates of deforestation of all tropical regions, primarily due to the economic profitability of oil palm agriculture (check out mongabay for details).

Fig. 2  Map of tropical forest distribution, the location of studies and relative recovery rates across regions.

The most common form of disturbance, and one from which forest regrowth happened relatively slowly, was anthropogenic impact, i.e. via logging, burning and/or for agriculture (Fig. 3).  The slowest rates of recovery occurred after climatic disturbances and the fastest after large infrequent events, e.g. landslides, hurricanes and natural fire.  This latter result is somewhat intuitive given that these perturbations are a natural part of all ecosystems, leading to the evolution of a dynamic response in the native plant communities.  

Fig. 3  Composite figure showing how the recovery rate varies with different variables.

Insights into resilience

When we looked at the standardised rate of disturbance events (SRD), i.e. the number of disturbance events per 1,000 years, we found that the greater the frequency events occurred in the past, the more quickly the forest responded to each subsequent disturbance.  This runs counter to contemporary theories on resilience that describe slowing rates and diminishing ability to recover with each subsequent perturbation (e.g. Veraart et al., 2012).  Our results suggest that over ecologically meaningful timescales, i.e. over the life-span of entire forest communities rather than single trees, increased exposure results in adaptation to that disturbance over time, leading to a greater ability to recover quickly from the perturbation.

What does this all mean for tropical forests?

From looking back into the past, it seems that tropical forests can take a long time to recover from disturbances, and that different regions may require different management regimes to encourage more complete reforestation after natural or anthropogenic events, such as fire.  Central American and African forests may bounce back from impacts more quickly than the other regions, with disturbances such as tropical hurricanes and climatic fluctuations being a more common component of these ecosystems than in the other tropical regions.  However, all of the forests we looked at demonstrated a greater vulnerability to anthropogenic impacts and climatic changes than large infrequent disturbances: the two major forms of disturbance occurring today and at levels that far exceed those experienced over the past 20,000 years – reasons for caution.

Sustainable management

Identifying and understanding the different ecological requirements of forests across the different geographical regions, and of the forest-types within those regions, is vital for developing more sustainable landscape management plans.  With increasing international concern over deforestation rates, the associated loss of biodiversity and elevated carbon dioxide emissions, the conservation and restoration of tropical forests is becoming more politically and economically feasible.  Indonesia, for example, has introduced ‘ecosystem restoration concessions’ in the last decade, providing a legal means for forest protection from the further expansion of industrial agriculture.  And the potential of Reducing Emissions from Deforestation and Forest Degradation (now REDD+) to save the World’s forests continues to generate international debate. Of importance to all of these programmes and initiatives, is the suggestion from our study that forests take time to recover, and if we give them that time, they will persist, and continue to provide their faunal inhabitants, including us, the greatest collection of biological riches on Earth.

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

How good are we at restoring ecosystem services?

Two major global initiatives have set ambitious goals for ecological restoration over the next decade. The Convention on Biological Diversity (CBD) has the objectives of restoring ecosystems critical for vital ecosystem services, enhancing carbon storage through forest restoration and restoring 15% of the world’s degraded ecosystems all by 2020. Similarly Reducing Emissions from Deforestation and Degradation (REDD+) aims to enahance carbon forest storage and reduce biodiversity loss, partly through forest restoration.

These are worthy goals, but they will only be achievable if there is social and political will to achieve them. However, quite apart from this problem these goals also raise vital questions for applied ecologists. One of these  is, how good are we at actually restoring ecosystem services?

A global assessment of forest restoration opportunities, produced by WRI

The honest answer is that it’s difficult to tell. Apart from carbon storage and pollination most services are extremely difficult to measure. However, by considering some ecosystem functions, such as nutrient cycling, to be intermediate ecosystem services (those that support the final benefits to human well-being) we can estimate some of the potential impacts of restoration.

A meta-analysis of restoration projects carried out by Jose Rey Benayas and colleagues did just that ¹. They compared measures of biodiversity and intermediate ecosystem services in degraded, restored and relatively undisturbed reference sites. Restored sites showed a 25% increase in intermediate ecosystem service provision compared to degraded sites . However, restoration sites showed approximately 20% lower provision of services when compared to reference sites .

Types of sites compared in meta-analysis by Rey Benayas et al 2009

We appear to be relatively good at restoring biodiversity when compared to function – measures of biodiversity were 43% higher in restored than degraded sites, while they were 15% lower in restored compared to reference sites. A recent meta-analysis looking at restoration in wetlands produced remarkably similar results with functions being restored to 75% of reference levels ².

Given this evidence, restoration appears to be relatively good at restoring ecosystem function. However, different ecosystems vary in how they respond to restoration with forests generally considered to be the amongst slowest to recover. Given that both the CBD and REDD+ initiatives target forest restoration to improve ecosystem service provision, this may be a slow process.

On the whole I would guess that we are probably better at restoring biodiversity than ecosystem services. Or at least we are better at restoring the biodiversity that people value and that is easily measurable. The are various reasons for this. Firstly, the primary goal of many restoration projects is to restore populations of particular species. Secondly, metrics of biodiversity are often easier to quantify than  functions and particularly ecosystem services. Thirdly, we know more about limiting factors of species population size than how complex functions work and how these link to services.

Done well restoration can provide benefits to both nature and humans. Given that ecosystems survival ultimately depends on their relationship to the people that inhabit them, it is vital more work investigates how restoration alters the provision of ecosystem services.

1. Rey Benayas, J. M., Newton, A. C., Diaz, A., Bullock, J. M., & Benayas, J. M. R. (2009). Enhancement of biodiversity and ecosystem services by ecological restoration: a meta-analysis. Science, 325(5944), 1121-4.

2. Moreno-Mateos, D., Power, M. E., Comín, F. A., & Yockteng, R. (2012). Structural and functional loss in restored wetland ecosystems. PLoS biology, 10(1), e1001247. Public Library of Science. doi:10.1371/journal.pbio.1001247