Our study provides a first attempt to as-sess the long-term impact of fragmenta-tion on tropical rain forest, and our results are consistent with available field studies in South America. We believe that the mod-elling approach is an efficient tool to address landscape-scale issues. Moreover, it makes it possible to relate our findings with satel-lite imagery information.
For example, data at 1 km2 spatial res-olution are available, with a percentage of forest cover from 0 % to 100 % (De-Fries et al. 2000). Using this dataset, it is easy to compute the fraction of rain for-est edge pixels in South America (1 km2 plots of rain forest at the edge of the population). Overall, these sites represent
0.60 million km2, out of 8.37 million km2 of rain forest (7.17 %). Of this edge area, 0.219 million km2 (37 %) is a boundary be-tween forest and agriculture/grasslands.
We may want to address the generality of these patterns across forest types. Our model was validated for two other forests, in Venezuela and in Sabah, Malaysia. Never-theless, we think that the model application to the French Guiana rain forest was more appropriate than to Venezuela or Malaysia.
Most field data on fragmentation were col-lected in different research plots in South America. Moreover, the current knowledge on forest growth and published data was wider for the French Guiana site than for that of Venezuela. Furthermore, available satellite imagery data sets already men-tioned allow us to validate our approach for forests in French Guiana (future project).
However, even if this case study was re-stricted to a rain foret in French Guiana, we think that the general pattern of how fragmentation will influence forest dynam-ics will be similar in different sites.
We have investigated scenarios assum-ing that the major mechanisms were the increased mortality of trees near edges (≤ 100 m) and a reduced recruitment rate. Although these assumptions are real-istic they compound several distinct mech-anisms, such as, for the mortality gradi-ent, microclimate changes (higher tempera-ture and lower moistempera-ture), higher probablity of tree uprooting, and explicit competition with ecotone species. It would be difficult, yet valuable, to construct a model which would take these as separate mechanisms.
A similar comment should be made for land-used areas. The soil properties are usually radically modified by agricultural activities, with a rapid loss of nutrients (Sal-darriaga 1986; Uhl et al. 1988; Buschbacher et al. 1988; Mackensen & F¨olster 2000), that might considerably slow down the forest re-generation pace. Also, possibly invasive species can take over abandoned pastures, as for the palm baba¸cu (Orbignya phalerata
Mart.) in Brazil, which impedes the succes-sion.
Our analysis was restricted to forest sizes up to 100 ha. Recent studies identify indi-rect edge effects of fragmentation on larger scales up to several thousands of hectars (Curran & Leighton 2000). Fauna-flora in-teractions on recruitment in terms of seed dispersal and predation might be far more important for the long-term forest dynamics as assumed today. Animals migrate to the remnant forests, if land clearing destroys their old habitats. Thus, even large for-est fragments will be affected by anthopo-geneous impacts. A size above which frag-mentation effects are negliglible might not exist on scales still found in tropical rain forests.
Acknowledgements
We thank Paul Moorecroft for giving us ref.
Moorecroft et al. (2000) prior to publica-tion. J. Chave was supported by grants to S.A. Levin, from the Andrew W. Mellon Foundation and from the David and Lu-cille Packard Foundation (grant 99-8307).
P. K¨ohler was funded by the Otto-Braun-Foundation at the University of Kassel.
H. Bossel provided helpful comments to the manuscript.
Appendix
Table 8.7: Short description of parameters including functional relationships (modified from K¨ohler et al.2000c).
Parameter Description
Environmental parameters
k Light extinction coefficient
I0 Light intensity above canopy
SD Day length
Establishment parameters DS Initial diameter of seedlings
ISs Minimal light intensity for germination NSs Ingrowth rate of seeds into seed pool NRs Seed dispersal rate of mother trees XRs Average seed dispersal distance DRh Minimal diameter of mother trees
Mortality parameters MBs,h Basic mortality rate
MSs Mortality rate of seeds
MDj Size dependent mortality rate (MD =MD0−MD0/MD1·d) W Probability of a dying tree to fall
Tree physiognomic parameters
HM Maximum height
cp Crown length fraction
τj Site dependent fraction of stemwood biomass to total aboveground biomass (τ =τ1+τ2·h(d= 120cm))
h0 and h1 Height = f(diameter) (h=d/(1/h0+d/h1)) γj Form factor = f(diameter) (γ =γ0·exp(γ1·dγ2)) fj Crown diameter = f(diameter) (dc = (f0+f1·df2)·d) lj Leaf area = f(diameter) (l=l1·d+l2·d2+l3·d3) LAIM Maximal leaf area index of single tree
Biomass production parameters
PM,α Photosynthetic capacity and efficiency in light response curve (Pi(Ii) =
αs·Ii
1+Pαs
MsIi)
ρ Stem wood density
r1l Maintenance respiration = f(biomass) (Rm(Bi) =r1l·Bi) RG Growth respiration as part of biomass
m Leaf transmittance
g Conversation factor gCO2 to godm
Table 8.8: Parametrisation for French Guiana.Short description of parameters in Table 8.7.
Parameters with subindex vary with successional status (s), potential height (h) (corresponding to SS and HG in Table 8.2, respectively), or different functional coefficients (j).
Name Special Units Values
Environmental parameters
k [-] 0.7
I0y wet dry [µmol(p) m−2 s−1] a 642.0 694.0
SDy wet dry [h] 12.0 8.0
SSy wet dry [-] 0.75 0.25
Establishment parameters
DS [m] 0.01
ISs s=0-3 [fraction ofI0y] 0.2 0.1 0.04 0.01
NSs s=0-3 [ha−1 y−1)] 25 125 500 100
NRs s=0-3 [ha−1 y−1)] 50 100 15 10
XRs s=0-3 [m] 100 50 40 20
DRh h=1-5 [m] 0.028 0.103 0.20 0.35 0.56
Mortality parameters
MBs,h s=0; h=1-5 [y−1] 0.18 0.16 0.12 0.10 0.00
MBs,h s=1; h=1-5 [y−1] 0.16 0.12 0.10 0.08 0.06
MBs,h s=2; h=1-5 [y−1] 0.07 0.06 0.05 0.04 0.03
MBs,h s=3; h=1-5 [y−1] 0.06 0.05 0.04 0.03 0.02
MSs s=0-3 [y−1] 0.01 0.1 0.5 1.0
MDj j=0-1 [y−1, cm] 0.2 0.1
W [-] 0.40
Tree physignomic parameters
HMs,h h=1-5 [m] 5.0 15.0 25.0 36.0 40.0
cp [-] 0.358
τ [-] 0.7
h0 [cm m−1] 1.96
h1 [m−1] 49.0
γj j=0-2 [-, cm−1, -] 2.575 -1.409 0.0358
fj j=0-2 [m cm−1, m cm−2, -] 0.132 0.933 -0.6615 lj j=1-3 [m cm−1, m cm−2, m cm−3] 3.197 0.0684 -0.000379
LAIM [-] 2
Biomass production parameters
PMs s=1-4 [µmol(c) m−2 s−1]a 27.7 27.7 11.3 6.8
αs s=1-4 [µmol(c)µmol(p)−1]a 0.043 0.043 0.043 0.043
ρs s=1-4 [todmm−3] 0.83 0.62 0.75 0.81
r1s s=1-4 [-] 0.08 0.08 0.04 0.03
RG [-] 0.25
m [-] 0.1
g [godmg−1CO2] 0.63
ap: photons; c: CO2
Summary
Background
Deforestation and degradation of tropical rain forests threaten these ecosystems all over the world. Rain forests, which were heavily disturbed through timber extraction or fragmentation, are endangered further if exposed to hurricanes or forest fires.
Currently, these human impacts on trop-ical forests are unsustainable and will cer-tainly continue for the near future. Field studies in various research activities try to analyse short term impacts on forests, but cannot address questions of forest develop-ment and the threat of species loss in the long term.