A half thought out critique

Inspired by a recent post by Joern Fischer I have decided to share one of my (many) half baked ideas. It’s based on a paper I read recently that I have a few issues with and want to work up into a letter to the editor, so please see all this as a work in progress and if you want to co-author it with me feel free, because that way I’m less likely to get a bad rep.So to the paper.Liu_coverIt’s this one by Liu et al in Global Ecology and Biogeography on how climate and age determine biomass in global mature forests.It sounded right up my street.I like forests.I like carbon. So I gave it a look.In the paper Liu et al aim to:

  • Investigate the relationship between aboveground biomass and climatic conditions and stand age in mature forests across the globe.
  •  Identify an age threshold at which forests should be considered ‘mature.’

The first question is interesting because people have done similar things with secondary forests in the past but I’m not sure I’ve ever seen this done looking at stand age in mature forests as a factor affecting biomass.The second I’m not a big fan of, but I will come to why later in the post.So Liu et al carry out a few analyses looking at the effects of mean annual temperature, mean annual rainfall, and stand age on biomass. However, the graphs of the analysis looks like this:Liu_fig_1 Liu_fig_2The figures had me worried and on closer inspection my suspicions were confirmed. They considered each explanatory variable independently in separate models. This is bad statistics but also doesn’t take account of the fact that previous studies have suggested that age, precipitation and temperature may interact to determine carbon accumulation rates. In addition the paper fails to account for spatial autocorrelation or differences between datasets that may be purely because of different methods used in their collection, rendering the results they present as questionable.To their credit Liu et al provide the data they used as supplementary materials so I thought I’d have a play with it to try and fix some of their errors.First I created a distance matrix and used that to look at spatial autocorrelation in biomass – surprise, surprise there were signs of spatial autocorrelation.I built a model that accounted for this and used a random effect to distinguish between each of the different datasets used in the study to account for between study error. I then did some model averaging so that all possible combinations of precipitation, temperature and age were included. I’ve put all of the code on github so feel free to look there and comment if you have any suggestions about the technical aspects of what I was up to.To cut a long story short the results suggest that all the variables considered by Liu et al are important, with one model that included all of them, as well as an interaction between temperature and age coming out as by far and away the best model.Age_tempTemp_age

Precipitation
Comparison of coefficients of our model compared to that of Liu et al. In each case the dotted black line represent’s Liu et al’s models and the coloured lines our models. Predictions were only made for interactions where there was sufficient data for both variables to allow this.

 

This model was much better than those of Liu et al (Table 1) – suggesting their approach was overly simplistic, as well as being statistically flawed. So, the models I developed explained much more variability than the equivalent ones in the Liu et al paper and changes the spin they put on their results.

Table 1 – Comparison of my top model and the models of Liu. AICc indicates relative parsimony of the model.

Model AICc AICc delta R squared
My model 623.98 0 0.29
Liu – Precipitation only 678.51 54.53 0.11
Liu – Temperature only 698.88 74.90 0.08
Liu – Age only 733.94 109.96 0.02

 

This model has an R squared 0.28, which is very good given the scale of the analysis but also suggests that there is quite a lot going on that we aren’t capturing in this model.  Part of this is probably because of the noise inherently added by using data collected in different ways.  In what I think is the best study of it’s type to date suggests that biomass in mature forests is only weakly related to commonly used climate metrics like mean temperature and mean precipitation. Instead, James Stegen and colleagues suggest that total biomass is well predicted by the biomass of the largest individual tree and that this is constrained by water deficit.

Now I to the second aim of Liu et al: to define a threshold age for mature forest.

This I have a big problem with. Even mature forests subject to relatively similar climatic conditions can vary massively in biomass and the reasons for this are not completely clear. Given this it is unwise to try to define a global threshold. It would be a much better idea to use chronosequence studies or long-term monitoring to try to discern dynamics at a landscape scale and build upon that to determine when forest should be classed as mature (and I’m only partly saying that because that’s what we did with secondary forest data…). I also have fears about defining ‘mature forest’ only using the biomass of these forests, and would be interested in seeing how biodiversity varies along with age in these old growth forests. Given that secondary forest carbon can get close to recovery quite quickly while biodiversity lags behind similar relationships may be seen for old growth forest. Any policy definition of what mature forest is could potentially have big implications for global biodiversity, so it important we get it right.

So those are my ideas. Critique them or add to them as you wish. And as I said, all code is available on github along with the data from the paper. I’m serious about writing a response, but like I said it needs more work so if you want to join me drop me a message below or in an email.

Tropical forest carbon storage is related to tree richness and traits. Or is it?

There’s been a lot said about relationships between species diversity and ecosystem function over the last two decades. The general view of these relationships is that diverse ecosystems are more productive, use resources more efficiently and are more stable.

But, and it’s a big but, almost none of this work has been done in forests and even less in mega-diverse tropical forests. Because of this diversity productivity relationships can’t really be described as general. How do we know what is general across the globe if we have only concentrated on temperate grasslands?

This is something a new paper in Global Ecology and Biogeography by Kyle Cavanaugh and colleagues hopes to set straight. Their study drew on a dataset of carbon storage and tree biodiversity from 59 plots across the tropics produced by members of the Terrestrial Ecosystem Assessment Monitoring  (TEAM) Network.

The great thing about this work is that all plots were surveyed using the same methods, meaning they should be reasonably comparable. All sites collected measures of aboveground carbon storage, genus diversity, functional diversity – by measuring wood density of trees and potential maximum diameter, and the mean value for wood density and maximum diameter for each plot. All of this was then analysed while trying to account for climatic differences between sites.

The general findings of the study were that both genus diversity and the mean potential maximum diversity of species appear to be  positively related to aboveground carbon storage.

Relationships between
Relationships between site (a) elevation and genus richness, (b) precipitation and functional diversity, (c) carbon storage and genus richness and (d) carbon richness and mean maximum diameter of trees. Stolen from the paper.

This enforces the view that diverse ecosystems are more productive and that large species may contribute a disproportionate amount of biomass – as I have written before. Very few studies have shown a relationship between diversity and biomass in tropical systems before, so this is exciting stuff.

And yet, I still have a few queries about some findings. The study failed to find any relationship between climate and carbon storage – a connection that is fairly well established. Also it uses stepwise model selection, which is beginning to become one of my (and others’) pet peeves . I am of the feeling that testing all possible models and then averaging amongst them based on the ones that have greatest support is the best way to do things, and this often comes up with very different findings to stepwise selection.

Previous similar work has suggested that carbon – biodiversity relationships are scale-dependant, with positive relationships in small plots and mixed results at larger plot sizes. Given the increasing number of tropical forest research networks I am sure this study will not be the last of its type. Once these get published we will have a better idea of how general these findings are.  At the moment I am not completely convinced.

Why “zero deforestation” is bad for forests

Over the last decade there has been increasing interest in reducing deforestation, spurred on by the Reducing Emissions from Deforestation and Degradation (REDD+) initiative.

It’s a great thing that reducing deforestation is now being discussed a bit more seriously by policy makers, but it can lead to some perverse situations – as a new article by Sandra Brown & Daniel Zarin in Science discusses. (I know this article is not so new anymore, it just took me 2 weeks to get round to writing this post, ok?)

Numerous governments and companies have set themselves goals of “zero deforestation.” In some cases this means net deforestation, in others gross deforestation and others haven’t really defined what they mean…

So why is this dangerous? I hear you ask.

Well for one thing, not all “forests” were created equal. Net deforestation measures include both the loss of forest from deforestation and the gains from forest regrowth and plantations. Plantation and recovering forests are not the same as undisturbed forests which contain more carbon and unique species. Under this definition all native forests could be lost from an area and replaced with plantation without any apparent deforestation. Stupid, right?

Brown & Zarin suggest a way to get proper estimates of deforestation would be to build on the Brazilian system of a satellite monitoring of deforestation that is clear and transparent. In Brazil deforestation statistics only account for forest loss and not any of the gains from regrowth or plantations. There is no reason why we can’t do this type of monitoring given the fantastic technology available that is now able to produce high resolution, global maps of forest change, like those below (go here to see more of these).

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The paper also suggests practical ways to reduce the problems of deforestation in individual countries, depending on their development. Countries are commonly categorised as having little forest loss, accelerating forest loss, decelerating forest loss and reforestation – these are said to represent the stages of forest transition. Use of non-forest land for agriculture should be encouraged in countries where forest loss is high, as has been encouraged for palm oil in South East Asia previously. However, in countries with low forest loss but little non-forested land that can be cultivated setting zero deforestation targets is unreasonable, given expanding populations.

As a big idea zero deforestation sounds great. But next time you hear someone talking about it think about what they actually mean.  Gross deforestation and reforestation should be considered as separate aims, to avoid confusion. Without this “zero deforestation” is set to mean very little.

How well do logged forests recover?

Photo credit to tlupic of flickr
Photo credit to tlupic on flickr

Logging  is one of the most widespread threats to tropical forests. It doesn’t seem to be disastrous for forest biodiversity, although that is somewhat unclear as I have discussed previously. However, it does reduce carbon storage because of the removal of trees – causing carbon emissions, which is bad – no matter what some people would prefer you to believe.

Even if you think climate change is green hype there are reasons to worry about logging.  The time given to logged foreststo recover is often not enough to allow timber species to recover properly. If this is widespread, it would put the long term sustainability of logging practices in the tropics in doubt.

Recovery of biodiversity, carbon, timber stocks and a whole host of other things are vital to work out how long forests should be left to recover between each logging period. Despite this there is actually relatively little data on recovery following logging, and this is particularly lacking from Africa.

To fill this gap researchers from France, Belgium, Central African Republic and Gabon looked at the recovery of a logged forest in Central Africa over more than 20 years. This involved setting up a logging experiment  in the forests near M’Biaki in the Central African Republic, which looks something like this. The area has been monitored for changes in biomass and timber stocks since logging took place in 1984 in forest that was unlogged, logged and logged and then trees thinned out during recovery to encourage growth.

Change in (a) aboveground biomass and (b) timber stock over time after logging. Figure taken from Gourlet-Fleury et al 2013.
Change in (a) aboveground biomass and (b) timber stock over time after logging. Figure taken from Gourlet-Fleury et al 2013.

Predictably logging forests sharply reduced their biomass and timber stock, with biomass reduced by about 30% and timber stock by 50%. More interestingly biomass then increased back to levels similar to undisturbed forests in  by 2011, while timber stock did not.

This is alarming because this forest was logged  much less intensively than those on other continents, but still did not recover its timber stocks. We should be worried by this. It means that even at the relatively low intensities of logging that happen in Central Africa, it might not be sustainable.

The authors’ argue that in order to make logging more sustainable the diameter at which trees are cut should be increased, whilst encouraging thinning to promote regrowth. I agree. However, we also need innovative solutions that go beyond those proposed already.

Reduced Impact Logging, which aims to reduce the amount of damage done to non-timber trees may help carbon stocks, but is less likely to aid timber stock recovery. A possible solution for tropical logging may be a combination of reduced logging techniques with planting of timber species which have been grown off site, in a similar way to that done by restoration projects. This could be lead by more researchers engaging with logging companies and encouraging projects to benefit sustainability of biodiversity and timber.

At the moment the problem of logging is similar to that of fisheries:  forests provide a resource that is difficult limit access to and is difficult to track once exploited. As well as improving logging practices it is vital that we improve governance, but if I knew how to do that I wouldn’t be sat here writing a blog for free.

We do need to make progress on this issue though, we can’t stick everything in protected areas – that smacks of green colonialism and people need to get resources in one way or another. Idealists may accuse those who work with logging companies of dining with the devil. Fair enough. But we’re not going to solve these problems by ignoring them.

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.

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