A preliminary study carried across 10 sites scattered across the Amazon Basin revealed a consistent and uniform relationship between logging intensity (i.e. the percentage of initial biomass loss) and the time to recover intial biomass stocks (Rutishauser et al. 2015). We now aim at understandgin how do timber volume recover after logging and what are the main drivers. Several research questions and a general framework were set up among TmFO partners during a 3 days workshop held in Belem in March 2016.
Timber is broadly defined as the volume of trunk of trees >= 50 cm. This volume (cubic meter) was estimated using site-specific volumetric equations. Summing all timber at plot scale provide the timber volume (TV), expressed in m3/ha. TV was further divided into three groups:
The main research questions are:
How long does it take to recover harvested timber volumes (3 levels of analysis, see below)?
What are the main drivers of TV recovery post-logging?
Only plots that were logged (i.e. no control) or were free from major fire disturbances were selected. On 215 plots, 144 were chosen. Harvest intensity ranged from 1.79 and 102.58 m3 ha-1.
Figure 1: TV at each plot and census. Red dot=year of logging & red line = linear regression of which the slope gives the TV recovery rate.
A straightforward definition of logging intensity is to sum all trees harvested within a plot. Plots that experienced post-logging treatments at Paracou, Tapajos and Ecosilva were discarded. For sake of comparison, logging intensity is expressed as a fraction of initial timber volume present before logging (%). Timbre volume (TV) recovery rates (m3 ha-1 y-1) are further computed as linear functions between TV stocks and time since logging.
Figure 2: Volume harvested by initial timber volume per plot
plot-ID | Logging date | TV harvested | Initial TV | % TV harv. | Pre-logging stem density | Site |
---|---|---|---|---|---|---|
chi-1 | 2004 | 27.452179 | 111.41369 | 0.2463986 | 30 | ChicoBocao |
chi-2 | 2004 | 2.983902 | 75.63877 | 0.0394494 | 25 | ChicoBocao |
chi-3 | 2004 | 3.106371 | 99.07330 | 0.0313543 | 34 | ChicoBocao |
chi-5 | 2004 | 18.852582 | 128.78453 | 0.1463886 | 32 | ChicoBocao |
chi-7 | 2004 | 25.717796 | 70.10773 | 0.3668325 | 18 | ChicoBocao |
cum-2 | 2007 | 3.944873 | 72.65489 | 0.0542960 | 24 | Cumaru |
cum-3 | 2007 | 64.944506 | 166.76745 | 0.3894315 | 33 | Cumaru |
cum-4 | 2007 | 22.315112 | 103.22454 | 0.2161803 | 21 | Cumaru |
cum-6 | 2007 | 9.792339 | 107.24055 | 0.0913119 | 28 | Cumaru |
eco-X1 | 2005 | 36.148768 | 96.63825 | 0.3740627 | 26 | Ecosilva |
eco-X10 | 2005 | 102.580911 | 192.28002 | 0.5334975 | 35 | Ecosilva |
eco-X11 | 2005 | 19.563292 | 130.28788 | 0.1501544 | 35 | Ecosilva |
eco-X12 | 2005 | 35.993045 | 84.53886 | 0.4257574 | 23 | Ecosilva |
eco-X13 | 2005 | 20.454547 | 84.05106 | 0.2433586 | 22 | Ecosilva |
eco-X14 | 2005 | 35.554555 | 127.57503 | 0.2786952 | 40 | Ecosilva |
eco-X15 | 2005 | 31.576301 | 122.10023 | 0.2586097 | 28 | Ecosilva |
eco-X16 | 2005 | 25.509817 | 130.02637 | 0.1961896 | 34 | Ecosilva |
eco-X17 | 2005 | 50.843147 | 118.15151 | 0.4303216 | 37 | Ecosilva |
eco-X18 | 2005 | 4.974913 | 127.86599 | 0.0389072 | 29 | Ecosilva |
eco-X2 | 2005 | 16.329594 | 88.17447 | 0.1851964 | 20 | Ecosilva |
eco-X3 | 2005 | 44.586666 | 115.71796 | 0.3853046 | 32 | Ecosilva |
eco-X4 | 2005 | 31.549139 | 103.53018 | 0.3047337 | 29 | Ecosilva |
eco-X5 | 2005 | 42.594313 | 116.00002 | 0.3671923 | 32 | Ecosilva |
eco-X6 | 2005 | 21.077340 | 101.11611 | 0.2084469 | 28 | Ecosilva |
eco-X7 | 2005 | 33.040448 | 88.54397 | 0.3731530 | 26 | Ecosilva |
eco-X8 | 2005 | 9.963002 | 56.98787 | 0.1748267 | 18 | Ecosilva |
eco-X9 | 2005 | 30.893274 | 130.59006 | 0.2365668 | 35 | Ecosilva |
ira-10 | 2004 | 28.936614 | 51.38456 | 0.5631383 | 12 | Iracema |
ira-11 | 2004 | 6.013837 | 37.93177 | 0.1585435 | 14 | Iracema |
ira-13 | 2004 | 12.229531 | 61.07040 | 0.2002530 | 18 | Iracema |
ira-14 | 2004 | 10.649455 | 51.44328 | 0.2070135 | 15 | Iracema |
ira-15 | 2004 | 8.430066 | 46.65775 | 0.1806788 | 16 | Iracema |
ira-16 | 2004 | 11.268172 | 59.73868 | 0.1886244 | 16 | Iracema |
ira-17 | 2004 | 16.341662 | 58.47762 | 0.2794516 | 18 | Iracema |
ira-18 | 2004 | 4.104867 | 71.82236 | 0.0571530 | 22 | Iracema |
ira-19 | 2004 | 3.523023 | 93.41676 | 0.0377130 | 26 | Iracema |
ira-2 | 2004 | 6.015316 | 60.50047 | 0.0994259 | 17 | Iracema |
ira-20 | 2004 | 7.659879 | 39.05037 | 0.1961538 | 12 | Iracema |
ira-22 | 2004 | 6.162740 | 103.89010 | 0.0593198 | 25 | Iracema |
ira-25 | 2004 | 10.630246 | 43.96495 | 0.2417891 | 12 | Iracema |
ira-26 | 2004 | 4.656273 | 65.86486 | 0.0706943 | 21 | Iracema |
ira-27 | 2004 | 11.130617 | 80.10165 | 0.1389561 | 19 | Iracema |
ira-29 | 2004 | 5.694724 | 65.03477 | 0.0875643 | 18 | Iracema |
ira-3 | 2004 | 5.340486 | 62.06409 | 0.0860479 | 17 | Iracema |
ira-30 | 2004 | 10.271264 | 71.43515 | 0.1437845 | 20 | Iracema |
ira-4 | 2004 | 9.299168 | 77.88491 | 0.1193963 | 16 | Iracema |
ira-5 | 2004 | 19.461608 | 114.13859 | 0.1705086 | 24 | Iracema |
ira-6 | 2004 | 6.420440 | 72.65411 | 0.0883700 | 16 | Iracema |
ira-7 | 2004 | 3.024427 | 51.22692 | 0.0590398 | 17 | Iracema |
itab-1 | 1996 | 15.195549 | 106.98033 | 0.1420406 | 38 | Itacoatiara B |
itab-10 | 1996 | 23.531854 | 96.26492 | 0.2444489 | 35 | Itacoatiara B |
itab-11 | 1996 | 30.867834 | 96.02404 | 0.3214594 | 33 | Itacoatiara B |
itab-12 | 1996 | 34.592513 | 104.53683 | 0.3309122 | 35 | Itacoatiara B |
itab-13 | 1996 | 44.692951 | 148.40279 | 0.3011598 | 41 | Itacoatiara B |
itab-14 | 1996 | 4.467986 | 80.63414 | 0.0554106 | 22 | Itacoatiara B |
itab-2 | 1996 | 31.052926 | 133.28167 | 0.2329872 | 45 | Itacoatiara B |
itab-3 | 1996 | 20.819199 | 92.70594 | 0.2245724 | 26 | Itacoatiara B |
itab-5 | 1996 | 34.493212 | 122.39915 | 0.2818092 | 31 | Itacoatiara B |
itab-6 | 1996 | 34.329086 | 94.16960 | 0.3645453 | 32 | Itacoatiara B |
itab-7 | 1996 | 5.593218 | 106.00656 | 0.0527629 | 33 | Itacoatiara B |
itab-8 | 1996 | 33.359417 | 104.39135 | 0.3195611 | 38 | Itacoatiara B |
itab-9 | 1996 | 5.266368 | 46.27985 | 0.1137940 | 17 | Itacoatiara B |
itac-1 | 1997 | 13.400004 | 82.10561 | 0.1632045 | 28 | Itacoatiara C |
itac-10 | 1997 | 8.611107 | 59.88961 | 0.1437830 | 20 | Itacoatiara C |
itac-11 | 1997 | 2.785741 | 80.25420 | 0.0347115 | 24 | Itacoatiara C |
itac-13 | 1997 | 6.846188 | 66.05611 | 0.1036420 | 12 | Itacoatiara C |
itac-2 | 1997 | 10.193673 | 65.57412 | 0.1554527 | 21 | Itacoatiara C |
itac-4 | 1997 | 14.197146 | 88.92517 | 0.1596527 | 22 | Itacoatiara C |
itac-6 | 1997 | 14.475289 | 131.08110 | 0.1104300 | 32 | Itacoatiara C |
itac-7 | 1997 | 1.976824 | 70.40800 | 0.0280767 | 24 | Itacoatiara C |
itac-8 | 1997 | 32.125217 | 111.15498 | 0.2890129 | 32 | Itacoatiara C |
itac-9 | 1997 | 8.252058 | 27.96598 | 0.2950749 | 13 | Itacoatiara C |
itad-1 | 1998 | 21.173490 | 127.58390 | 0.1659574 | 39 | Itacoatiara D |
itad-11 | 1998 | 4.620159 | 139.41658 | 0.0331392 | 35 | Itacoatiara D |
itad-12 | 1998 | 30.779182 | 114.44401 | 0.2689453 | 36 | Itacoatiara D |
itad-13 | 1998 | 8.117371 | 87.49357 | 0.0927768 | 28 | Itacoatiara D |
itad-14 | 1998 | 43.444763 | 176.94851 | 0.2455221 | 50 | Itacoatiara D |
itad-2 | 1998 | 8.924915 | 133.57555 | 0.0668155 | 38 | Itacoatiara D |
itad-3 | 1998 | 15.236311 | 120.49003 | 0.1264529 | 39 | Itacoatiara D |
itad-4 | 1998 | 34.224032 | 143.27283 | 0.2388731 | 36 | Itacoatiara D |
itad-5 | 1998 | 25.555566 | 152.81034 | 0.1672372 | 40 | Itacoatiara D |
itad-6 | 1998 | 11.599127 | 119.29894 | 0.0972274 | 37 | Itacoatiara D |
itad-7 | 1998 | 23.267348 | 167.74809 | 0.1387041 | 38 | Itacoatiara D |
itad-8 | 1998 | 7.597272 | 105.23348 | 0.0721944 | 27 | Itacoatiara D |
itad-9 | 1998 | 14.748177 | 101.06605 | 0.1459261 | 32 | Itacoatiara D |
jar-101 | 1986 | 28.006961 | 139.44948 | 0.2008395 | 32 | Jari |
jar-102 | 1986 | 69.673088 | 168.34552 | 0.4138696 | 36 | Jari |
jar-103 | 1986 | 15.050889 | 100.46717 | 0.1498090 | 32 | Jari |
jar-104 | 1986 | 31.542695 | 110.16755 | 0.2863157 | 30 | Jari |
jar-105 | 1986 | 37.880698 | 99.99041 | 0.3788433 | 30 | Jari |
jar-106 | 1986 | 13.765716 | 113.25966 | 0.1215412 | 29 | Jari |
jar-107 | 1986 | 18.972694 | 92.18202 | 0.2058177 | 28 | Jari |
jar-108 | 1986 | 26.817577 | 108.20638 | 0.2478373 | 34 | Jari |
jar-109 | 1986 | 5.729426 | 108.40359 | 0.0528527 | 29 | Jari |
jar-110 | 1986 | 25.497145 | 151.32935 | 0.1684878 | 46 | Jari |
jar-111 | 1986 | 27.336089 | 115.56600 | 0.2365409 | 35 | Jari |
jar-112 | 1986 | 20.147254 | 148.84919 | 0.1353535 | 29 | Jari |
jar-201 | 1986 | 9.886241 | 142.54580 | 0.0693548 | 45 | Jari |
jar-202 | 1986 | 33.850733 | 113.39700 | 0.2985152 | 33 | Jari |
jar-203 | 1986 | 19.319679 | 126.29126 | 0.1529772 | 41 | Jari |
jar-204 | 1986 | 25.729300 | 138.55720 | 0.1856944 | 34 | Jari |
jar-205 | 1986 | 38.942339 | 152.87001 | 0.2547415 | 35 | Jari |
jar-206 | 1986 | 7.779658 | 79.73304 | 0.0975713 | 28 | Jari |
jar-207 | 1986 | 21.387511 | 75.89608 | 0.2817999 | 21 | Jari |
jar-208 | 1986 | 35.918791 | 120.03841 | 0.2992275 | 39 | Jari |
jar-209 | 1986 | 45.005265 | 138.94824 | 0.3238995 | 34 | Jari |
jar-210 | 1986 | 10.810684 | 141.91705 | 0.0761761 | 42 | Jari |
jar-211 | 1986 | 14.276482 | 93.66656 | 0.1524181 | 29 | Jari |
jar-212 | 1986 | 36.494251 | 108.35283 | 0.3368094 | 31 | Jari |
jar-301 | 1986 | 30.243143 | 111.40384 | 0.2714731 | 35 | Jari |
jar-302 | 1986 | 26.665473 | 91.86113 | 0.2902803 | 30 | Jari |
jar-303 | 1986 | 25.673819 | 74.70678 | 0.3436612 | 22 | Jari |
jar-304 | 1986 | 41.564297 | 99.58306 | 0.4173832 | 25 | Jari |
jar-305 | 1986 | 8.975882 | 92.60434 | 0.0969272 | 27 | Jari |
jar-306 | 1986 | 12.502798 | 122.32393 | 0.1022106 | 37 | Jari |
jar-307 | 1986 | 9.696712 | 96.80833 | 0.1001640 | 27 | Jari |
jar-308 | 1986 | 22.369825 | 116.27704 | 0.1923839 | 35 | Jari |
jar-309 | 1986 | 22.001872 | 103.65395 | 0.2122627 | 29 | Jari |
jar-310 | 1986 | 40.980410 | 111.24313 | 0.3683860 | 32 | Jari |
jar-311 | 1986 | 21.680037 | 114.73715 | 0.1889539 | 34 | Jari |
jar-312 | 1986 | 25.373064 | 90.76530 | 0.2795459 | 27 | Jari |
pag-CL | 1993 | 14.687860 | 50.91820 | 0.2884600 | 362 | Paragominas |
pag-RIL | 1993 | 12.337652 | 50.00440 | 0.2467313 | 346 | Paragominas |
par-2 | 1987 | 31.150818 | 83.23657 | 0.3742444 | 175 | Paracou |
par-7 | 1987 | 27.064622 | 80.52307 | 0.3361102 | 183 | Paracou |
par-9 | 1987 | 26.305911 | 65.21304 | 0.4033842 | 143 | Paracou |
pet-4 | 2003 | 18.023931 | 83.16657 | 0.2167209 | 26 | Peteco |
pet-5 | 2003 | 18.152080 | 125.79247 | 0.1443018 | 34 | Peteco |
pet-6 | 2003 | 45.052938 | 103.45480 | 0.4354843 | 27 | Peteco |
pet-7 | 2003 | 22.572429 | 80.08924 | 0.2818410 | 20 | Peteco |
pet-9 | 2003 | 4.782163 | 116.77622 | 0.0409515 | 31 | Peteco |
tab-1 | 2001 | 14.083986 | 66.60211 | 0.2114646 | 21 | Tabocal |
tab-2 | 2001 | 4.886323 | 58.89183 | 0.0829712 | 22 | Tabocal |
tab-3 | 2001 | 3.793691 | 52.76877 | 0.0718927 | 15 | Tabocal |
tab-4 | 2001 | 1.788432 | 37.68834 | 0.0474532 | 16 | Tabocal |
tab-5 | 2001 | 10.018218 | 38.97698 | 0.2570291 | 9 | Tabocal |
tab-6 | 2001 | 5.925447 | 42.93508 | 0.1380095 | 12 | Tabocal |
tap-2 | 1983 | 31.986567 | 80.49897 | 0.3973537 | 24 | Tapajos |
tap-3 | 1983 | 26.451640 | 124.80372 | 0.2119459 | 40 | Tapajos |
tap-4 | 1983 | 43.110006 | 153.39405 | 0.2810409 | 34 | Tapajos |
tap-5 | 1983 | 44.636167 | 119.47554 | 0.3736009 | 35 | Tapajos |
tap-6 | 1983 | 27.160839 | 122.27577 | 0.2221277 | 36 | Tapajos |
tap-7 | 1983 | 15.870821 | 52.17835 | 0.3041649 | 17 | Tapajos |
tap-8 | 1983 | 56.564566 | 106.45033 | 0.5313705 | 29 | Tapajos |
All site leaders were asked to provide a list of the main commercial species (compiled below). In total, there is 265 species considered.
label | Nsp | prop |
---|---|---|
ChicoBocao | 9 | 12.9 |
Cumaru | 9 | 15.3 |
Ecosilva | 28 | 30.4 |
Iracema | 11 | 10.3 |
Itacoatiara B | 31 | 32.3 |
Itacoatiara C | 14 | 18.7 |
Itacoatiara D | 32 | 32.7 |
Jari | 25 | 15.6 |
Paragominas | 28 | 24.8 |
Paracou | 33 | 31.4 |
Peteco | 15 | 19.5 |
Tabocal | 4 | 8.0 |
Tapajos | 20 | 22.2 |
## Warning in matrix(unique(list$nameV[order(list$nameV)]), ncol = 3): data
## length [265] is not a sub-multiple or multiple of the number of rows [89]
Scientific name | Scientific name | Scientific name |
---|---|---|
Agonandra brasiliensis | Enterolobium schomburkii | Ocotea tomentella |
Albizia pedicellaris | Eperua falcata | Ormosia coutinhoi |
Alchornea triplinervia | Eperua grandiflora | Paramachaerium ormosioides |
Alexa grandiflora | Eperua grandifolia | Parinari campestris |
Amanoa guianensis | Eperua rubiginosa | Parkia decussata |
Anacardium giganteum | Eriotheca longipedicellata | Parkia gigantocarpa |
Anacardium parvifolium | Erisma uncinatum | Parkia multijuga |
Andira laurifolia | Eschweilera coriacea | Parkia paraensis |
Andira parviflora | Eschweilera odorata | Parkia pendula |
Andira unifolialata | Eschweilera ovata | Parkia ulei |
Aniba burchellii | Eschweilera paniculata | Parkia velutina |
Aniba canelilla | Eschweilera sagotiana | Peltogyne catingae |
Antonia ovata | Euxylophora paraensis | Persea laevigata |
Apeiba albiflora | Ficus nymphaeifolia | Phyllocarpus riedellii |
Apeiba petoumo | Ficus piresiana | Piptadenia suaveolens |
Apuleia leiocarpa | Glycydendron amazonicum | Pithecellobium incuriale |
Apuleia leocarpa | Goupia glabra | Pithecellobium racemosum |
Apuleia molaris | Guazuma ulmifolia | Platonia insignis |
Astronium gracile | Handroanthus impetiginosus | Platymiscium filipes |
Astronium lecointei | Handroanthus serratifolia | Poelcilanthe effusa |
Astronium leicoitei | Handroanthus serratifolius | Pouteria anomala |
Bagassa guianensis | Humiria balsamifera | Pouteria bilocularis |
Balizia pedicellaris | Hymenaea courbaril | Pouteria engleri |
Bertholletia excelsa | Hymenaea oblongifolia | Pouteria eugeniifolia |
Bocoa prouacensis | Hymenaea parvifolia | Pouteria flavilatex |
Bombacopsis nervosa | Hymeneaea courbaril | Pouteria guianensis |
Bombax globosum | Hymeneaea oblongifolia | Pouteria laevigata |
Bowdichia nitida | Hymenolobium excelsum | Pouteria opposita |
Brosimum acutifolium | Hymenolobium heterocarpum | Pouteria oppositifolia |
Brosimum guianense | Hymenolobium modestum | Pouteria platyphylla |
Brosimum lactescens | Hymenolobium nitidum | Pouteria rodriguesiana |
Brosimum lanciferum | Hymenolobium petraeum | Pradosia cochlearia |
Brosimum parinarioides | Hymenolobium pulcherrimum | Protium paniculatum |
Brosimum potabile | Hymenolobium sericeum | Protium puncticulatum |
Brosimum rubescens | Ilex inundata | Pseudopiptadenia psilostachya |
Brosimum uleanum | Inga alba | Pseudopiptadenia suaveolens |
Brosimum utile | Iryanthera crassifolia | Pterocarpus officinalis |
Buchenavia capitata | Iryanthera grandis | Qualea paraensis |
Buchenavia guianensis | Iryanthera paraensis | Qualea rosea |
Buchenavia nitidissima | Jacaranda copaia | Qualea tesmannii |
Buchenavia parvifolia | Laetia procera | Roupala montana |
Buchenavia viridiflora | Lecythis lurida | Ruizterania albiflora |
Byrsonima laevigata | Lecythis pisonis | Sacoglottis guianensis |
Calophyllum brasiliense | Lecythis poiteaui | Schefflera morototoni |
Candolleodendron brachystachyum | Lecythis prancei | Scleronema micranthum |
Capirona huberiana | Lecythis zabucajo | Sextonia rubra |
Carapa guianensis | Licania alba | Simarouba amara |
Carapa guiansensis | Licania ovalifolia | Sterculia pruriens |
Carapa surinamensis | Licaria aritu | Stryphnodendron adstringens |
Cariniana micrantha | Licaria brasiliensis | Stryphnodendron pulcherrimum |
Caryocar glabrum | Licaria cannella | Swartzia grandifolia |
Caryocar microcarpum | Licaria crassifolia | Swartzia panacoco |
Caryocar villosum | Licaria rigida | Swartzia polyphylla |
Cedrela fissilis | Luehea grandiflora | Symphonia globulifera |
Cedrela odorata | Lueheopsis rugosa | Tabebuia impetiginosa |
Ceiba pentandra | Manilkara bidentata | Tabebuia insignis |
Chaetocarpus schomburgkianus | Manilkara cavalcantei | Tabebuia serratifolia |
Chimarrhis turbinata | Manilkara huberi | Tachigali goeldianum |
Chrysophyllum lucentifolium | Manilkara paraensis | Tachigali melinonii |
Chrysophyllum pachycarpa | Manilkara surinamensis | Tachigali myrmecophyla |
Chrysophyllum pomiferum | Mezilaurus duckei | Tachigali myrmecophylla |
Chrysophyllum prieurii | Mezilaurus itauba | Tachigali paraensis |
Chrysophyllum sanguinolentum | Mezilaurus sinandra | Tapura capitulifera |
Chrysophyllum venezuelanense | Mezilaurus synandra | Tetragastris altissima |
Clarisia racemosa | Micrandropsis scleroxylon | Torresea acreana |
Copaifera multijuga | Micropholis egensis | Trattinnickia rhoifolia |
Cordia bicolor | Micropholis guyanensis | Vantanea guianensis |
Cordia goeldiana | Micropholis melioniana | Vantanea parviflora |
Couma guianensis | Micropholis venulosa | Vatairea eritrocarpa |
Couratari guianensis | Minquartia guianensis | Vatairea guianensis |
Couratari multiflora | Monopteryx inpae | Vatairea paraensis |
Couratari oblongifolia | Moronobea coccinea | Vatairea sericea |
Couratari stellata | Myroxylon balsamo | Virola duckei |
Dendrobangia boliviana | Nectandra cissiflora | Virola kwatae |
Dialium guianense | Nectandra micranthera | Virola michelii |
Diatenopteryx sorbifolia | Nectandra purusensis | Virola michellii |
Dicorynia guianensis | Ocotea acutangula | Virola mickelii |
Dinizia excelsa | Ocotea caudata | Virola multinervia |
Diplotropis purpurea | Ocotea cernua | Virola surinamensis |
Diplotropis racemosa | Ocotea costulata | Vochysia guianensis |
Diplotropis triloba | Ocotea fragantissima | Vochysia maxima |
Dipteryx odorata | Ocotea fragrantissima | Vochysia neyratii |
Dipteryx punctata | Ocotea glomerata | Vochysia surinamensis |
Ecclinusa guianensis | Ocotea longifolia | Vochysia tomentosa |
Endlicheria bracteata | Ocotea neesiana | Vouacapoua americana |
Endopleura uchi | Ocotea opifera | Zollernia paraensis |
Enterolobium maximum | Ocotea petalanthera | Zygia racemosa |
Enterolobium oldemanii | Ocotea puberula | Agonandra brasiliensis |
Enterolobium schomburgkii | Ocotea rubra | Albizia pedicellaris |
Here, we check whether recovery rates (Figure 3) or the amount of timber cummulated over the study period (Figure 4) are somehow related to logging intensity.
Figure 4: Cumulated TV vs logging intenstiy (% of initial volume removed) for site-specific (A), regional (B) and all (C) timber species.
Recovery times (year) are obtained by dividing the volume of timber harvested in each plot by the post-logging TV recovery rates. Effects of different biometric response variables on recovery times were further explored through linear mixed effects models.
Figure 5: Range of TV recovery rates accounting for site-specific (A), regional (B) and all (C) timber species
As for the biomass study, we used linear mixed-effects models, where sites are regarded as “random” effects. Plots were weighted in function of their size and lenght between initial and final censuses. The best models are found through an exhaustive screening and ranking using Bayesian Information Criterion (BIC) (package lmerTest). To reduce residual heteroscedasticity, recovery time was log-transformed.
Bioclimatic variables were extracted from WorldClim: Mean Temperature of Warmest Quarter(TWQ), Mean Temperature of Coldest Quarter (TCQ), Annual Precipitation (Precip),Precipitation of Wettest Month (PWeM), Precipitation of Driest Month (PDM), Pre-cipitation Seasonality (Coefficient of Variation) (Season), Precipitation of Wettest Quarter (PWeQ), Precipitation of Driest Quarter (PDQ). Soil properties were extracted from the Harmonized World Soil raster at a resolution of 30 arc-seconds. Information on top soil (0-30 cm) was extracted at each site: texture, drainage, available water content (range), clay,silt and sand content (%), cation-exchange capacity (CEC,cmol.kg-1) and bulk density (bulk.dens,kg.dm-3).
Clay content was well correlated (cor = -0.98) with the firs PCA axis and bulk density with the second PCA axis (cor = 0.81). Only both variables were further used as soil proxy. Precipitation of the wettest quartile (PWQ) was well correlated (cor = 0.98) with the firs PCA axis and Precipitation of the driest quartile (PDQ) with the second PCA axis (cor = 0.88), and both were used as bioclimatic proxy. Initial forest structures, such as initial TV and forest development stage (i.e. difference between plot-TV and site averaged TV), and logging intensity were included, as explanatory variables.
Here, only the commercial species logged at a site are accounted for. Recovery rates and times depend on a small pool of species.
# Variable selection recovery time
require(glmulti)
require(lme4)
require(MuMIn)
GLM1 <- glmulti(log(rec1) ~ ratioV + vol.ini + vol.rel + bulk.dens + clay + PWQ + PDQ -1 , data=REL[!is.na(REL$rec1),], random="+ (1 | site)", fitfunc =lmer.glmulti,report=F, level = 1,crit="bic",method="g",plotty=F)
## TASK: Genetic algorithm in the candidate set.
## Initialization...
## Algorithm started...
## Improvements in best and average IC have bebingo en below the specified goals.
## Algorithm is declared to have converged.
## Completed.
print(GLM1)
## glmulti.analysis
## Method: g / Fitting: lmer.glmulti / IC used: bic
## Level: 1 / Marginality: FALSE
## From 100 models:
## Best IC: 238.710602396773
## Best model:
## [1] "log(rec1) ~ 1 + ratioV"
## Evidence weight: 0.301462205597357
## Worst IC: 267.132871897282
## 2 models within 2 IC units.
## 23 models to reach 95% of evidence weight.
## Convergence after 200 generations.
## Time elapsed: 7.40018391609192 minutes.
tmp<- weightable(GLM1)
tmp <- tmp[tmp$bic <= min(tmp$bic) + 5,]
tmp
## model bic weights
## 1 log(rec1) ~ 1 + ratioV 238.7106 0.30146221
## 2 log(rec1) ~ 1 + ratioV + vol.ini 239.6410 0.18931703
## 3 log(rec1) ~ 1 + ratioV + vol.rel 241.1553 0.08879106
## 4 log(rec1) ~ 1 + ratioV + PDQ 242.6081 0.04294442
## 5 log(rec1) ~ 1 + ratioV + vol.ini + bulk.dens 242.6410 0.04224382
## 6 log(rec1) ~ 1 + ratioV + bulk.dens 242.8867 0.03736061
## 7 log(rec1) ~ 1 + ratioV + PWQ 243.0724 0.03404724
## 8 log(rec1) ~ 1 + ratioV + clay 243.0800 0.03391878
## 9 log(rec1) ~ 1 + ratioV + vol.ini + PDQ 243.6436 0.02558865
plot(GLM1,type="s")
summary(GLM1@objects[[1]])
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: log(rec1) ~ 1 + ratioV + (1 | site)
## Data: data
## Weights: dat$wght
##
## AIC BIC logLik deviance df.resid
## 229.2 238.7 -110.6 221.2 76
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3761 -0.5951 -0.0208 0.5536 3.4694
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.2040 0.4517
## Residual 0.8239 0.9077
## Number of obs: 80, groups: site, 10
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 3.6731 0.2950 12.452
## ratioV 4.9019 0.9622 5.094
##
## Correlation of Fixed Effects:
## (Intr)
## ratioV -0.769
r.squaredGLMM(GLM1@objects[[1]])
## R2m R2c
## 0.2471778 0.3966079
Here, timber species harvested across all study sites were used to compute TV.
# Variable selection recovery time
GLM2 <- glmulti(log(rec2) ~ ratioV + vol.ini + vol.rel + bulk.dens + clay + PWQ + PDQ -1 , data=REL[!is.na(REL$rec2),], random="+ (1 | site)", fitfunc =lmer.glmulti,report=F, level = 1,crit="bic",method="g",plotty=F)
## TASK: Genetic algorithm in the candidate set.
## Initialization...
## Algorithm started...
## Improvements in best and average IC have bebingo en below the specified goals.
## Algorithm is declared to have converged.
## Completed.
print(GLM2)
## glmulti.analysis
## Method: g / Fitting: lmer.glmulti / IC used: bic
## Level: 1 / Marginality: FALSE
## From 100 models:
## Best IC: 228.765404568569
## Best model:
## [1] "log(rec2) ~ 1 + ratioV + vol.ini"
## Evidence weight: 0.236906670596473
## Worst IC: 246.785994052981
## 2 models within 2 IC units.
## 34 models to reach 95% of evidence weight.
## Convergence after 210 generations.
## Time elapsed: 8.64338612556458 minutes.
tmp<- weightable(GLM2)
tmp <- tmp[tmp$bic <= min(tmp$bic) + 5,]
tmp
## model bic weights
## 1 log(rec2) ~ 1 + ratioV + vol.ini 228.7654 0.23690667
## 2 log(rec2) ~ 1 + ratioV + vol.ini + clay + PWQ + PDQ 230.6550 0.09209887
## 3 log(rec2) ~ 1 + ratioV + vol.ini + clay + PWQ 231.0558 0.07537619
## 4 log(rec2) ~ 1 + ratioV + vol.rel 231.7110 0.05431875
## 5 log(rec2) ~ 1 + ratioV + vol.rel + clay + PWQ 231.7704 0.05273020
## 6 log(rec2) ~ 1 + ratioV + vol.ini + PWQ 232.1905 0.04274027
## 7 log(rec2) ~ 1 + ratioV + vol.ini + clay 232.2192 0.04213036
## 8 log(rec2) ~ 1 + ratioV + vol.ini + PDQ 232.6493 0.03397819
## 9 log(rec2) ~ 1 + ratioV + vol.ini + vol.rel 233.0768 0.02743900
## 10 log(rec2) ~ 1 + ratioV + vol.rel + clay + PWQ + PDQ 233.0779 0.02742364
## 11 log(rec2) ~ 1 + ratioV + vol.ini + bulk.dens 233.0901 0.02725665
## 12 log(rec2) ~ 1 + ratioV + vol.rel + clay 233.1521 0.02642525
## 13 log(rec2) ~ 1 + ratioV 233.4267 0.02303471
plot(GLM2,type="s")
summary(GLM2@objects[[1]])
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: log(rec2) ~ 1 + ratioV + vol.ini + (1 | site)
## Data: data
## Weights: dat$wght
##
## AIC BIC logLik deviance df.resid
## 217.0 228.8 -103.5 207.0 72
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4479 -0.6432 -0.2955 0.6052 3.1554
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.06645 0.2578
## Residual 0.81332 0.9018
## Number of obs: 77, groups: site, 10
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1.850072 0.430916 4.293
## ratioV 3.356719 0.894955 3.751
## vol.ini 0.011789 0.003809 3.096
##
## Correlation of Fixed Effects:
## (Intr) ratioV
## ratioV -0.424
## vol.ini -0.805 -0.085
r.squaredGLMM(GLM2@objects[[1]])
## R2m R2c
## 0.2678426 0.3231444
Figure 7: Recovery time (log scale) of timber volume of regional timber species in function of harvested TV (% of initial TV).
Here, all trees DBH > 50 cm were lumped together to compute TV.
## TASK: Genetic algorithm in the candidate set.
## Initialization...
## Algorithm started...
## Improvements in best and average IC have bebingo en below the specified goals.
## Algorithm is declared to have converged.
## Completed.
## glmulti.analysis
## Method: g / Fitting: lmer.glmulti / IC used: bic
## Level: 1 / Marginality: FALSE
## From 100 models:
## Best IC: 219.746546823389
## Best model:
## [1] "log(rec3) ~ 1 + ratioV + vol.ini"
## Evidence weight: 0.213971699976682
## Worst IC: 274.12893462338
## 3 models within 2 IC units.
## 34 models to reach 95% of evidence weight.
## Convergence after 250 generations.
## Time elapsed: 8.28837203979492 minutes.
## model bic weights
## 1 log(rec3) ~ 1 + ratioV + vol.ini 219.7465 0.21397170
## 2 log(rec3) ~ 1 + ratioV + vol.ini + clay 220.8099 0.12573473
## 3 log(rec3) ~ 1 + ratioV + vol.ini + clay + PWQ 221.0795 0.10987696
## 4 log(rec3) ~ 1 + ratioV + clay 222.7325 0.04807929
## 5 log(rec3) ~ 1 + ratioV + clay + PWQ 222.9611 0.04288783
## 6 log(rec3) ~ 1 + ratioV 223.1801 0.03843829
## 7 log(rec3) ~ 1 + ratioV + vol.ini + vol.rel 223.5488 0.03196685
## 8 log(rec3) ~ 1 + ratioV + vol.ini + PDQ 223.6631 0.03019231
## 9 log(rec3) ~ 1 + ratioV + vol.ini + PWQ 223.9445 0.02622927
## 10 log(rec3) ~ 1 + ratioV + vol.ini + bulk.dens 224.0114 0.02536576
## 11 log(rec3) ~ 1 + ratioV + vol.rel + clay 224.0889 0.02440180
## 12 log(rec3) ~ 1 + ratioV + vol.ini + bulk.dens + clay 224.4065 0.02081890
## 13 log(rec3) ~ 1 + ratioV + vol.ini + clay + PDQ 224.4278 0.02059829
## 14 log(rec3) ~ 1 + ratioV + vol.rel + clay + PWQ 224.4970 0.01989806
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: log(rec3) ~ 1 + ratioV + vol.ini + (1 | site)
## Data: data
## Weights: dat$wght
##
## AIC BIC logLik deviance df.resid
## 208.2 219.7 -99.1 198.2 69
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4558 -0.6995 -0.0726 0.4825 2.2454
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.02137 0.1462
## Residual 0.83386 0.9132
## Number of obs: 74, groups: site, 10
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.256994 0.424342 0.606
## ratioV 8.121137 0.930693 8.726
## vol.ini 0.011520 0.003881 2.968
##
## Correlation of Fixed Effects:
## (Intr) ratioV
## ratioV -0.446
## vol.ini -0.810 -0.082
## R2m R2c
## 0.5621281 0.5730705
Figure 8: Recovery time (log scale) of timber volume of all species in function of harvested TV (% of initial TV).