This is not really a proper post. It’s just here to highlight the great news article by Nature on experiments in tropical forest fragmentation. It backs up what I was saying in my article on the effects of logging on tropical biodiversity and talks a bit more about what the SAFE project is doing in Borneo in testing the effects of oil palm plantations and logging on forest structure and biodiversity. We need more of this kind of work and I’m really interested to see the first results from SAFE.
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*.
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.
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.
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.
Everyone pretty much knows about the crisis of biodiversity loss facing the tropics.
In case you missed it tropical forests are being rapidly cleared, which human population increases and along with consumption. All this has lead to large losses of biodiversity in the tropics.
So far, so boring.
However, up until recently we didn’t have much of an idea how the characteristics of species in the tropics influenced their response to land-use change.
‘Why would we want to know that?’ – I hear you ask. Well if you’ve seen my blog before you will know that traits are a good way of linking biodiversity change to changes in ecosystem function and services. This is the first step to working out the consequences of the massive changes in biodiversity we have seen over the last century. Simply put – we need to know this stuff.
Given what I think, it was great to find out at the recent BES 2012 annual meeting in Birmingham about a paper looking at how bird species with different traits respond to land-use change in the tropics.
Tim Newbold, a postdoc at the World Conservation Monitoring Centre in Cambridge, and colleagues compiled an impressive dataset of >4500 records of >1300 bird species from 23 studies of land-use change in the tropics. They then used data on habitat preferences, migratory status, diet, generation length and body size to determine how differences in these traits related to birds’ response to land-use change.
They found that long-lived, non-migratory, primarily frugiverous or insectivorous forest specialists were likely to be less abundant and less likely to occur in intensively used habitats.
Of these characteristics diet preference is perhaps the most easy to link to changes in ecosystem function and services.
The loss of insect eating species may impact the control of pest species with potentially negative consequences for tropical agriculture. However, this assumption depends heavily on pest species abundances not reducing in line with bird declines. It is also entirely possible that if pest species also reduce in abundance forest loss will lead to little change in crop damage.
The reduction in fruit eating bird species may have consequences for forest regeneration and maintainance of plant diversity. Many secondary forests that are isolated from primary forest have been shown to lack large seeded tree species. Any reduction in the abundance of fruit eating birds suggests another barrier preventing the recovery of plant species communities in secondary forests.
I really liked this paper. It shows the value of large datasets for making generalisations and the results are potentially important for investigating change in ecosystem function and services in tropical forest ecosystems. The good news is it looks like there is a lot more of this type of work on the way with the PREDICTS project aiming to do take a similar approach to many questions related to land-use change. I’m excited to see what they come up with next, provided they don’t scoop me in the process…
Planning regulation in the UK is set to be relaxed, allowing more homes to be built for the millions who can’t afford them at the moment. Planning minister Nick Boles recently evoked the right of every Brit to own a house with a small garden in his plans to increase the amount of land available for development from 9% of England to 12%.
It doesn’t sound like much when you put it like that. But that 3 percentage point difference represents nearly 4,000 square kilometre, an area around the size of Suffolk. Or French Polynesia if you’re feeling adventurous.
This is a big chunk of an already human dominated landscape. The UK has one of the highest population densities in Europe and more than 60% of the landscape is given over to farming and urban areas.
Pressure groups like the Campaign for Rural for Rural England have objected to the recent changes in planning regulation but have been characterised as selfish, small-minded NIMBYs.
However, there has been little mention of nature or biodiversity as part of this debate. If we want to debate this it is important we ask how we should plan our settlements to cause least harm to nature.
Similarity to debates on agriculture
In many ways this is similar to debates on land sparing versus land sharing in agriculture or to some extent the debates on strategies for energy production. We should be looking to divide our landscape up in a way that benefits society and balance this with the need to protect biodiversity.
I am constantly amazed that this issue is barely discussed with regard to town planning.
Should we live in settlements that cover large areas, that are spread out and have gardens? Should we live in settlements that are dense, most people living in flats and fewer private gardens but with more public spaces? Or should we do something in between?
I tend to think that more intensive housing would probably be a quite good thing, the UK’s cities are already sprawling (though not like the US) and messy. It would probably bring biodiversity benefits as cities would have a smaller geographic, and carbon, footprint. However, there is very little primary research out there to base my opinions on, so at the moment I’m largely speculating.
For example the city I visit most outside of the UK is Bilbao which has a population of around 400,000, with a population density nearly twice that of London. This means you can more or less walk to the countryside from the city since it doesn’t spill out all over the place. This should benefit species that are specialists that might be sensitive to alteration of their environment by humans and would therefore suffer as a result of large suburban areas.
Most people in the UK don’t share my views. The majority of people probably want a house with a garden in a suburb somewhere. They also associate flats with poverty or old age, neither of which are exactly positive selling points.
None of these solutions will suddenly make the UK into a paradise for nature. Britain still has a population density twice that of its nearest neighbour France and is only topped by the Netherlands and Belgium in Europe.
We should, though, be thinking more strategically about how we plan our settlements. But before we get ahead of ourselves we need to carry out more work looking at how the density of settlements can affect biodiversity.
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.
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.
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.
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)
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)
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)
Biodiversity conservation by and large boils down to decisions about what we do and where we do it. Land use change is the major driver of biodiversity loss globally, mostly as a result of agricultural expansion. The discussion surrounding how to best divide up the landscape to best benefit biodiversity whilst meeting target of food production will run and run, but the land-sharing/land-sparing argument is equally applicable to other subjects.
One of these is power production. We all know that we are meant to be reducing emissions in the face of climate change. Some governments are doing this, while many others don’t really care. Meanwhile, with increasing global population it seems unlikely that energy demand will be reduced, at least in the short term.
To anyone reading this blog the consequences of not changing energy policies will probably be fairly clear. Increased severity of climate change will push species ever further polewards and disrupt the synchrony between species. Climate change could also disrupt agricultural production, which would be disastrous given that global human population is set to increase for at least the next 50 years. Given all this, it should be a no-brainer that we switch to less carbon intensive means of producing energy.
Most environmentalists would generally say that we should replace dirty coal and gas fired power stations with clean renewable energy generation. I agree, but only partially.
Renewables bring their own set of problems. Firstly, can they really meet our current energy demands? Probably not. It would need extremely quick uptake of green tech to do this. Even Germany, the EU country with the greatest percentage of its energy produced by renewables, can only manage 25%. Relatively speaking, this is great. However, they still need to do more.
Also there is the issue of space. How will we use our land to produce energy? Most renewables would take up relatively large chunks of land compared to the standard power station. Firstly, biofuels (which are a terrible idea for all sorts of reasons, but that’s the subject for another blog post) would need massive areas to be grown. They would also compete with agriculture for land, spelling problems for food security. Damming of rivers, like that recently announced for the Mekong, completely screw up the movement of aquatic species thus interfering with migration and breeding cycles. All renewable energy production methods will require more space than carbon intensive methods (for a more detailed analysis see David MacKay’s excellent TED talk below). From a UK perspective it appears the best bet in terms of energy produced per square mile would, surprisingly, be solar.
However, I think the best idea would be to mix renewables and nuclear. This would allow for greater energy production for the area used than a wholesale switch to renewables and would still reduce carbon emissions. If this policy was implemented harm to biodiversity would be reduced and there would be less threat to food crops from the expansion of biofuels. To me this seems blindingly obvious. There are, thankfully, other conservationists and green groups that have seen the light but in general the green movement seems to be opposed to nuclear.
I find this extremely frustrating. Particularly the backlash following the tsunami in Japan last year. Both Japan and Germany are decommissioning their power plants and other European countries are not planning to replace their ageing reactors. We need to make hard decisions, often choosing between the least bad options. We can’t live in a magical land where renewables can provide the energy we need ad infinitum. Those of us that realise this should pressurise groups like Greenpeace and the various green parties throughout Europe into rethinking their policies. Without this rethink, they could be doing more environmental harm than good.
Earlier this month I went to the 2012 European Congress of Conservation Biology where the best session by far was one on land sharing vs land sparing (see a summary of the session by Joern Fischer here). This session was inspired by the Science paper by Ben Phalan and resulting back-and-forth.
The main idea behind the paper was that with increasing global population we need to increase food production. We can either do this by increasing the area of agriculture or intensifying production in the agricultural land we currently use. To limit the impact of both these options on biodiversity we could use wildlife friendly farming, termed land sharing. Another option is to spare natural ecosystems from conversion in the case of intensification, termed land sparing.
Thus land sharing aims to integrate goals for food production and biodiversity protection on the same land, while land sparing aims to separate intensive farming from protected ecosystems at the larger scale (A caricature of this continuum is provided below) .
Phalan et al took these options and tested their potential effects on bird and tree populations in Ghana and Northern India by looking at landscapes which represented these differing strategies. Using this data they plotted yield against species density to define how populations may change with increased yield. These changes allowed them to classify species as ‘losers’ or ‘winners’ following agricultural conversion as well as defining the land use options most likely to be beneficial for maintenance of their populations.
On the whole they found that land sparing was the best option for most species, particularly for those species with small global ranges.This is important since it is largely these species that are considered to be conservation priorities.
However, some people have interpreted this as meaning that the authors advocate land sparing in all situations. Even if they do, it is obvious that land sparing might not be the best strategy in all situations. Different landscapes have different histories of land use, which will have inevitably had an effect on biodiversity and consequently what we see as priorities for protection.
For example, much of Western Europe has been cultivated for centuries if not millennia and has little forest cover. As a result the biodiversity we value here consists largely of generalist species which thrive in low intensity farmland and require some form of agricultural practice for persistence. Meadows are a great example of a cultural landscape that is highly valued in Europe but requires disturbance, such as grazing, to exist. In situations like these it is entirely possible that land sharing may help to boost the populations of priority species.
In addition the taxonomic group which you aim to protect will determine the scale at which management should be undertaken. What constitutes land sparing for an invertebrate will not be the same as that for a bird. Phalan et al’s paper arguably chose taxonomic groups which would be likely to benefit from large scale land sparing, it will be interesting to see how research into other taxa differs in their findings.
Ecosystem services will also be affected by these different land use strategies. Land sharing may favour services which rely on fragments of semi-natural habitat in order to be distributed throughout the landscape, such as pollination. Meanwhile services which are supplied far away from ecosystems which generate them, such as carbon storage and water purification, will be favoured by land sparing.
Though food production is an obvious priority, lots of conservation essentially adds up to how best to use particular parcels of land to meet multiple goals. Land sparing vs land sharing could be applied to urban planning and energy production to name just two. Hopefully, if my PhD doesn’t get in the way, I will explore this issues further in the coming weeks.
The land sparing vs land sharing debate is obviously set to run and run. However, it seems likely that with more research we may be able to form some generalisations. In areas where there are many species which depend on forests, or any other ecosystem incompatible with agriculture, land sparing may be best while land sharing may work best in areas with a long history of extensive farming and little forest cover.
Much work is needed to determine the consequences of these options for ecosystem services and also the social implications. For example, could promoting land sparing further add to our lack of connection with nature? What exactly is the relationship between provision of particular ecosystem services and these different options? I don’t have the answer to these questions. However, given that agriculture is the biggest single threat to biodiversity, but is something none of us can live without, I hope we will at least have a few more answers in the near future.