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

2 thoughts on “Is secondary tropical forest of secondary importance?

  1. This is a nice piece, but one factor you omit completely is the proximity to primary forest, which is crucial when it comes to recovery time. The wider landscape matrix determines the dispersal into the secondary forest, hence its recovery time in terms of biodiversity, community composition and structure. Some work on Jamaican forests recovering after plantations showed that even after 250 years there were still differences to primary forest among the plant communities.

    1. This is a good point. I agree there is evidence that the distance to primary forest and the wider make-up of the matrix are likely to be crucial in determining secondary forest communities.

      I can see this kind of thing is relatively easy to test for plants as you can get an idea of dispersal limitation by looking at seed banks at various distances from primary forest. This can then be linked to forest plant communities.

      For vertebrates though, I would find it hard to determine how you disentangle edge effects (i.e. species which spend a lot of time in primary forest but also some time in secondary forests) from the influence of distance on the community of less transient species.

      Also, although I agree with you this is likely to be an issue for secondary forests, I would like to see a paper that brings together data from various sites and tries to draw general conclusions.

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