Pedro Luiz Silva de Miranda

Pedro Luiz Silva de Miranda
University of Liège | ulg · Gembloux Agro-Bio Tech



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Publications (11)
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Significance Our full-scale comparison of Africa and South America’s lowland tropical tree floras shows that both Africa and South America’s moist and dry tree floras are organized similarly: plant families that are rich in tree species on one continent are also rich in tree species on the other continent, and these patterns hold across moist and d...
Full-text available
Abstract Aim We used a phylogenetic approach to group assemblages of woody plant into major vegetation units in the Atlantic Forest, thus for the first time incorporating information on species evolutionary relationships into a bioregionalization of this critical hotspot. A phylogenetic regionalization will provide a spatially explicit framework fo...
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Aim: To test whether species distribution models (SDMs) can reproduce major macroecological patterns in a species-rich, tropical region and provide recommendations for using SDMs in areas with sparse biotic inventory data. Location: Northeast Brazil, including Minas Gerais. Time period: Present. Major taxa studied: Flowering plants. Methods: Specie...
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Tropical moist forests and savannas are iconic biomes. There is, however, a third principal biome in the lowland tropics that is less well known: tropical dry forest. Discussions on responses of vegetation in the tropics to climate and land-use change often focus on shifts between forests and savannas, but ignore dry forests. Tropical dry forests a...
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The rocky montane savannas of South America, known as campos rupestres in Brazil, where they largely occur, represent a megadiverse habitat housing c.15% of the Brazilian vascular flora in less than 1% of the Brazilian territory. Amongst other factors, the remarkable plant diversity in campos rupestres has been attributed to its occurrence as many...
Supplementary Information for 'Neves D.M. et al. (2017) Dissecting a biodiversity hotspot: The importance of environmentally marginal habitats in the Atlantic Forest Domain of South America. Diversity and Distributions 23, DOI10.1111/ddi.12581'
Full-text available
Aim: We aimed to assess the contribution of marginal habitats to the tree species richness of the Mata Atlântica (Atlantic Forest) biodiversity hotspot. In addition, we aimed to determine which environmental factors drive the occurrence and distribution of these marginal habitats. Location: The whole extension of the South American Atlantic Forest...
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Altitude is one of the major environmental variables influencing the distribution of tree taxa around the world, and can be a useful parameter for the development of conservation strategies. Our objectives were to obtain an overview of the conservation status of taxa from the Atlantic semideciduous seasonal forests of southeastern Brazil and check,...
The loss in forest area due to human occupancy is not the only threat to the remaining biodiversity: forest fragments are susceptible to additional human impact. Our aim was to investigate the effect of human impact on tree community features (species composition and abundance, and structural descriptors) and check if there was a decrease in the nu...
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We aimed to review the ecological role of the trails and insert this subject into restoration ecology projects. For a comprehensive understanding of this ecological role, we addressed the applicability of trails to Environmental Education (EE) projects and their impacts on vegetation. We showed that trails are suitable places to practice EE program...


Questions (3)
Hello everyone,
I have recently started looking for estimates of tree species richness for different biomes/ecosystems in South America and it's been hard to find estimates for the drier biomes (a.k.a Dry Forests, Gran Chaco and Savannas/Brazilian Cerrado). And it's been even more difficult to find estimates for the Gran Chaco.
Does anyone know of references mentioning estimates of tree species richness for the Gran Chaco? I have found references mentioning estimates of how many plant families and genera are there and a few very good lists on the dominant tree species encountered in the area, but I'm still lacking tree species richness estimates.
Any sort of estimate is much appreciated.
Thank you,
Hello ResearchGate community,
I have been working on a series of SEMs as a part of my PhD. All of them seem to be running smoothly, but I am not being able to obtain decent measures of model fitness (I'm focusing on chisquare, CFI, BIC e RMSEA) for some of them. I copied one of these models bellow. So far, I have tried all of the resources I could think of: I increased and decreased the number of indicators on my latent variables, I removed latent variables and used the indicators directly in the regressions, I removed outliers and I also changed the order of indicators in each one of my latent variables. Would anyone around here be able to spot something that might be triggering these low values of model fitness?
Some extra information on the model: response variable is categorical and has two levels only. All other variables are continuous and have been put in the same scale.
A big thank you to you all,
Pedro Miranda
model1 <- '#latent variables
soil =~ pH + SandLOG + P.totalLOG + Al.cmol. + SB
climate =~ DroughtLOG + mapLOG + cwdLOG + MinTempColdMonthLOG
soil ~ climate
FireLOG ~ climate
Biome ~ climate + soil + FireLOG
SB ~~ FireLOG
SB ~ cwdLOG
SB ~ Al.cmol.
SB ~ DroughtLOG
SB ~~ SandLOG
DroughtLOG ~~ Al.cmol.
DroughtLOG ~~ SandLOG
DroughtLOG ~~ FireLOG
DroughtLOG ~~ cwdLOG
Summary generated by Lavaan:
lavaan (0.5-23.1097) converged normally after 66 iterations
Number of observations 68
Estimator ML
Minimum Function Test Statistic 96.188
Degrees of freedom 32
P-value (Chi-square) 0.000
Model test baseline model:
Minimum Function Test Statistic 447.864
Degrees of freedom 55
P-value 0.000
User model versus baseline model:
Comparative Fit Index (CFI) 0.837
Tucker-Lewis Index (TLI) 0.719
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) NA
Loglikelihood unrestricted model (H1) NA
Number of free parameters 34
Akaike (AIC) NA
Bayesian (BIC) NA
Root Mean Square Error of Approximation:
RMSEA 0.172
90 Percent Confidence Interval 0.133 0.212
P-value RMSEA <= 0.05 0.000
Standardized Root Mean Square Residual:
SRMR 0.168
Parameter Estimates:
Information Expected
Standard Errors Standard
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.all
soil =~
pH 1.000 0.920 0.900
SandLOG 0.137 0.134 1.018 0.309 0.126 0.124
P.totalLOG 0.074 0.080 0.929 0.353 0.068 0.116
Al.cmol. -0.919 0.153 -5.998 0.000 -0.845 -0.759
SB 3.011 0.933 3.226 0.001 2.769 0.941
climate =~
DroughtLOG 1.000 0.908 0.338
mapLOG -0.429 0.127 -3.387 0.001 -0.389 -1.078
cwdLOG 0.929 0.265 3.502 0.000 0.843 0.776
MnTmpCldMntLOG -0.098 0.032 -3.096 0.002 -0.089 -0.605
Estimate Std.Err z-value P(>|z|) Std.all
soil ~
climate 0.529 0.187 2.828 0.005 0.522 0.522
FireLOG ~
climate 0.446 0.156 2.862 0.004 0.405 0.355
Biome ~
climate 0.198 0.075 2.623 0.009 0.179 0.470
soil 0.034 0.053 0.643 0.520 0.031 0.082
FireLOG -0.085 0.037 -2.295 0.022 -0.085 -0.253
SB ~
cwdLOG -0.085 0.241 -0.353 0.724 -0.085 -0.031
Al.cmol. 0.200 0.579 0.345 0.730 0.200 0.076
DroughtLOG -0.160 0.092 -1.740 0.082 -0.160 -0.146
Estimate Std.Err z-value P(>|z|) Std.all
.SB ~~
.FireLOG 0.310 0.200 1.553 0.120 0.310 0.195
.SandLOG ~~
.SB -0.939 0.236 -3.986 0.000 -0.939 -0.624
.Al.cmol. ~~
.DroughtLOG -0.204 0.215 -0.949 0.343 -0.204 -0.111
.SandLOG ~~
.DroughtLOG 1.059 0.320 3.307 0.001 1.059 0.415
.DroughtLOG ~~
.FireLOG 0.657 0.297 2.216 0.027 0.657 0.244
.cwdLOG 0.310 0.166 1.864 0.062 0.310 0.178
Estimate Std.Err z-value P(>|z|) Std.all
.pH 0.198 0.101 1.966 0.049 0.198 0.190
.SandLOG 1.016 0.175 5.819 0.000 1.016 0.985
.P.totalLOG 0.338 0.058 5.825 0.000 0.338 0.987
.Al.cmol. 0.526 0.134 3.937 0.000 0.526 0.424
.SB 2.231 1.018 2.191 0.028 2.231 0.258
.DroughtLOG 6.407 1.060 6.046 0.000 6.407 0.886
.mapLOG -0.021 0.009 -2.386 0.017 -0.021 -0.161
.cwdLOG 0.471 0.085 5.531 0.000 0.471 0.398
.MnTmpCldMntLOG 0.014 0.002 6.055 0.000 0.014 0.634
.FireLOG 1.135 0.190 5.979 0.000 1.135 0.874
.Biome 0.110 0.018 6.069 0.000 0.110 0.760
.soil 0.616 0.146 4.226 0.000 0.728 0.728
climate 0.824 0.503 1.640 0.101 1.000 1.000
I am currently working on a database for my PhD thesis and in order to add the soil results I have for some of my areas (retrieved from the literature), I need to convert the variables reported in these papers from cmol/dm3 to cmol/kg. Does anyone know if it is possible to do that? I am, of course, aware that I need to know how much space one kilogram of soil can occupy and that will change from soil to soil. I'm looking for an alternative as I don't want to let data go to west just because of problems in the units.
Does anyone know how to deal with this issue?
Pedro Miranda


Cited By


Project (1)
The CARE Forest is life aims to study forest ecosystems and, more generally, landscape structures with a low degree of anthropisation, whether in temperate or tropical regions.