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Fig. 11 in Reproductive characteristics and the weight-length relationship in Anableps anableps (Linnaeus, 1758) (Cyprinodontiformes: Anablepidae) from the Amazon Estuary

Fig. 11. Proportion of sexually mature Anableps anableps females by body size (standard length) collected in the mouth of the Maracanã River, Pará State.

Fig. 3 in Reproductive characteristics and the weight-length relationship in Anableps anableps (Linnaeus, 1758) (Cyprinodontiformes: Anablepidae) from the Amazon Estuary

Fig. 3. Between-sexes variation in (a) standard length and (b) weight in Anableps anableps from the Maracanã River, Pará State.

Fig. 7 in Reproductive characteristics and the weight-length relationship in Anableps anableps (Linnaeus, 1758) (Cyprinodontiformes: Anablepidae) from the Amazon Estuary

Fig. 7. Proportion of Anableps anableps females by stage of gonadal maturity in the mouth of the Maracanã River, Pará Sttate.

Fig. 4 in Reproductive characteristics and the weight-length relationship in Anableps anableps (Linnaeus, 1758) (Cyprinodontiformes: Anablepidae) from the Amazon Estuary

Fig. 4. Weight-length relationship (a) and the distribution of the proportional residuals (b) in Anableps anableps females collected at the mouth of the Maracanã River, Pará State.

Fig. 2 in Reproductive characteristics and the weight-length relationship in Anableps anableps (Linnaeus, 1758) (Cyprinodontiformes: Anablepidae) from the Amazon Estuary

Fig. 2. Monthly variation in the sex ratio (females to males) of Anableps anableps in the mouth of the Maracanã River, Pará State.

Fig. 1 in Reproductive characteristics and the weight-length relationship in Anableps anableps (Linnaeus, 1758) (Cyprinodontiformes: Anablepidae) from the Amazon Estuary

Fig. 1. Study area showing the Maracanã River and the collecting locality (white circle) in the Brazilian state of Pará.

Fig. 8 in Reproductive characteristics and the weight-length relationship in Anableps anableps (Linnaeus, 1758) (Cyprinodontiformes: Anablepidae) from the Amazon Estuary

Fig. 8. Monthly variation in the gonadosomatic index (GSI) in Anableps anableps females collected at the mouth of the Maracanã River, Pará State.

Fig. 10 in Reproductive characteristics and the weight-length relationship in Anableps anableps (Linnaeus, 1758) (Cyprinodontiformes: Anablepidae) from the Amazon Estuary

Fig. 10. Embryo length (a) and weight (b) in relation to the number of stage V embryos in Anableps anableps from the Maracanã River, Pará State.

opencc-by-4.0Jan 2022View details →

Amazon Rainforest Resilience Data

Dataset and code to accompany 'Pronounced loss of Amazon rainforest resilience since the early 2000s'.

Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon (Open)

This work was carried out in the scope and with the support of the project: Climate Services Through Knowledge Co-Production: A Euro-South American Initiative for Strengthening Societal Adaptation Response to Extreme Events (CLIMAX). The project consortium includes the following institutions: Centre National de la Recherche Scientifique CNRS/Instituto Franco-Argentino sobre Estudios de Clima y sus Impactos (UMI-IFAECI) (Argentina-France); General Coordination of Earth Sciences /National Institute  for Space Research (INPE) (Brazil); Institut de Recherche pour le Développement (IRD)/ Unité Mixte de Recherche (UMR 245) (France);  Le Laboratoire des Sciences du Climat et de l'Environnement (LSCE) (France); Potsdam Institute for Climate Impact Research (PIK) (Germany); Technical University of Munich (TUM) (Germany) and Wageningen University and Research (WUR) Netherlands). The project is sponsored by the Collaborative Research Action (CRA) on “Climate Predictability and Inter-Regional Linkages” of the Belmont Forum, launched in 2015. Climate variability patterns linking the South American Monsoon region, including Amazonia, with southeastern South America influence climate extremes and impact several societal sectors. More than 200 million people live in the study region, which is also one of the largest agricultural production regions of the world and home to the world’s second largest hydroelectric power plant. The objectives of CLIMAX  include  better understanding the combined role of remote and local drivers on South American climate variability from sub-seasonal to decadal timescales, and its impact on the occurrence and intensity of extreme events. Special focus is given to an improved understanding of the effects of land use changes from the Amazon to the subtropics and their impact on climate. <ol> <li> EXPERIMENT DESIGN </li> </ol> We used four models that are classified as Dynamic Global Vegetation Models (DGVMs) (Prentice et al., 2007; Rezende et al., 2015): Integrated Model of Land Surface Processes (INLAND) (Tourigny, 2014); Lund-Potsdam-Jena managed Land model version 4 (LPJmL4) (Schaphoff et al., 2018), Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) (Smith et al. 2001, Hickler et al., 2012), and Organising Carbon and Hydrology In Dynamic Ecosystems model (ORCHIDEE) (Krinner et al., 2005). We used three forcings with climate data (GLDAS, GSWP3, and WATCH+WFDEI), Land Use Change (LUC) data and validation data (FLUXCOM (Remote sensor+meteorological data+artificial neural network approach), FLUXCOM (eddy covariance), MODIS (Light Use Efficiency), GLEAM, and TerraClimate (Rezende et al., 2022). We conducted two sets of simulation experiments with different values of CO2: 1) increasing CO2 from the pre-industrial period to 2010 named historical CO2 (hist CO2); 2) constant concentration of 278 ppm of (pre-industrial) atmospheric CO2 named constant CO2 (const CO2). We ran both CO2 experiments under Land Use Change (LUC) and Potential Natural Vegetation (PNV) conditions. All combinations of CO2 and land use change resulted in four sets of simulation experiments per climate input: 1. LUC historical CO2; 2. LUC constant CO2; 3. PNV historical CO2; 4. PNV constant CO2  (Rezende et al., 2022). 2. DATA DESCRIPTION The complete description of the data, including the climate forcing, LUC, the validation datasets, methodology, simulations, discussion and conclusion is in Rezende et al. (2022). This archive contains only the data description from the simulations (outputs) by the DGVMs. 2.1 SOFTWARE The data were manipulated, worked, standardized, converted using the software: Climate Data Operators (cdo) version 1.7.0, Grid Analysis and Display System (Grads) (<a href="https://web.archive.org/web/20150407042441/http://www.iges.org/grads/gadoc/">Documentation of GrADS</a>) version 2.0.2, and RStudio Desktop version 1.3.1093 (R Core Team, 2020), through command lines and several scripts developed for this purpose. The figures were generated  with Grads, and RStudio, and some images were enhanced  with Gimp version 2.8.22. All the software used is freeware.    2.2 PRIMARY DATA FROM SIMULATIONS Data originating from the simulations are in monthly resolution, covering South America, with all the forcings. Despite data spanning  over 1948-2010 or 1950-2010 our study focuses on the period 1981-2010. Variables: Gross Primary Productivity (GPP) (kg m-2 month-1), evaporation  (mm month-1) and transpiration (mm month-1), and Net Primary Productivity (NPP) (kg m-2 month-1) (not used in our experiment). The naming of the files is according to the following rules: DGVM_forcing_vegetation cover_CO2 concentration_attribute DGVMs;     InLand (INLAND)     LPJ-G (LPJ-GUESS)     LPJmL (LPJmL4)     ORCHI (ORCHIDEE) forcings:      gld - GLDAS     gsw – GSWP3     wat – WATCH+WFDEI   vegetation cover:     LU – Land Use Change     PNV – Potential Natural Vegetation   CO2 concentration:     CO2 – historical CO2     noCO2 – constant CO2 = 278 ppm   variables:     E – evaporation     Et – transpiration     gpp – Gross Primary Productivity     npp - Net Primary Productivity (not used in the experiment)   Example:     inLand_gld_LU_noCO2_E.nc    2.3 SUPPLEMENTARY DATA These interception loss data (mm month-1) were requested by a reviewer to complement the analysis and are available only for the LUC CO2 scenario and for the study region: southern Amazon (70S and 140S of latitude and 660W and 510W of longitude). Files are named  according to the following rules: variable_season_forcing_DGVM_vegetation cover CO2 concentration_region variable:     inter – loss by interception   season:     D – dry season     R – rainy season   forcings:                 gl - GLDAS                gs – GSWP3                wa – WATCH+WFDEI   DGVMs:     in - INLAND      lg - LPJ-GUESS             lm - LPJmL4             or – ORCHIDEE   vegetation cover      L – Land Use Change     P – Potential Natural Vegetation   CO2 concentration     C – historical CO2     N – constant CO2 = 278 ppm   region     SA – southern Amazon   Example:     inter_D_gl_in_LC_SA.nc 2.4 PROCESSED DATA Processed data cover all scenarios and input data sets and are restricted to the study area: southern Amazon (70S and 140S of latitude and 660W and 510W of longitude).They are in seasonal resolution with averages for January-February-March-April (JFMA) (rainy season) and averages for June-July-August-September (JJAS). Each of the files contains the Gross Primary Productivity variables (GPP) (kg m-2 month-1), evaporation  (mm month-1) and transpiration (mm month-1), and Net Primary Productivity (NPP) (kg m-2 month-1) (not used in our experiment).   Files are named according to the following rules:   season_DGVM_forcing_vegetation cover CO2 concentration_region season     D – dry season     R – rainy season   DGVMs:     in - INLAND      lg - LPJ-GUESS            lm - LPJmL4            or - ORCHIDEE   forcings:             Gl - GLDAS            Gs – GSWP3            Wa – WATCH+WFDEI      vegetation cover      L – Land Use Change     P – Potential Natural Vegetation   CO2 concentration     C – historical CO2     N – constant CO2 = 278 ppm   Example: D_in_Gs_LN.nc 2.5 DERIVED DATA   The variable Water Use Efficiency (WUE) (kg m-2 mm-1 month-1) results from rate: GPP / Tr (transpiration) (Eq. 1).    <table> <tbody> <tr> <td>                                   WUE = GPP / Tr </td> <td> (Eq. 1) </td> </tr> </tbody> </table>   These data refer to the study region: southern Amazon and apply to only one scenario:  Land Use Change and historic CO2. Files are named  naming of according to the following rules:   Variable:     WUE – Water Use Efficiency   season     D – dry season     R – rainy season   DGVMs:     in - INLAND      lg - LPJ-GUESS            lm - LPJmL4            or - ORCHIDEE   forcings:                 Gl - GLDAS                Gs – GSWP3                Wa – WATCH+WFDEI      vegetation cover      L – Land Use Change     P – Potential Natural Vegetation   CO2 concentration     C – historical CO2     N – constant CO2 = 278 ppm   region     SA – southern Amazon   Example: wue_D_lm_Gl_LC_SA.nc   How to cite this work: Rezende, Luiz F. C., Aline Castro, Celso Von Randow, Romina Ruscica, Boris Sakschewski, Phillip Papastefanou, Nicolas Viovy, Kirsten Thonicke, Anna Sörensson, Anja Rammig, Iracema F. A. Cavalcanti. Impacts of Land Use Change and atmospheric CO2 on  Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon. Journal of Geophysical Research Atmospheres (JGRA) - doi: 10.1029/2021JD034608. 2022. References <a href="https://web.archive.org/web/20150407042441/http://www.iges.org/grads/gadoc/">Documentation of GrADS</a>. Center for Ocean-Land-Atmosphere Studies, Institute of Global Environment and Society,<a href="https://en.wikipedia.org/wiki/George_Mason_University"> George Mason University</a>. Archived from<a href="http://www.iges.org/grads/gadoc/"> the original</a> on 7 April 2015. Retrieved 14 March 2015. Hickler T. et al., 2012. Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species based dynamic vegetation model. Glob Ecol Biogeograp 21:50–63, <a href="https://doi.org/10.1111/j.1466-8238.2010.00613.x">https://doi.org/10.1111/j.1466-8238.2010.00613.x</a> Krinner, G.et al., 2005. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochemical Cycles, 19, GB1015, doi:10.1029/2003GB002199. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.               Prentice IC (2007) Dynamic global vegetation modeling: quantifying terrestrial ecosystem responses to large-scale environmental change. In: Canadell J, Pataki D, Pitelka L (eds) Terrestrial ecosystems in a changing world. Springer, Berlin Heidelberg. Rezende, Luiz F. C. et al., 2015. Evolution and challenges of dynamic global vegetation models for some aspects of plant physiology and elevated atmospheric CO2. Int J Biometeorol., 2015, doi: 10.1007/s00484-015-1087-6. Rezende, Luiz F. C. et al., 2022. Impacts of Land Use Change and atmospheric CO2 on  Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon. Journal of Geophysical Research - Atmospheres (JGRA) - doi: 10.1029/2021JD034608. 2022. Schaphoff, S. et al., 2018. LPJmL4 – a dynamic global vegetation model with managed land –Part 1: Model description. Geosci. Model Dev., 11, 1343–1375, 2018, <a href="https://doi.org/10.5194/gmd-11-1343-2018">https://doi.org/10.5194/gmd-11-1343-2018</a>. Smith B. et al (2001) Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space. Glob Ecol Biogeograp 10:621–637 Tourigny, E. (2014). Multi-scale fire modeling in the neotropics: coupling a land surface model to a high resolution fire spread model, considering land cover heterogeneity. Phd dissertation, Meteorology. INPE. Retrieved from http://urlib.net/sid.inpe.br/mtc-m21b/2014/05.30.00.36

Figs 7–8 in Platygonia Melichar, 1925 (Insecta: Hemiptera: Cicadellidae: Cicadellini): a new species from the Brazilian Amazon Rainforest, key to species of the genus, and notes on P. undecimmaculata (Fowler, 1899)

Figs 7–8. Platygonia undecimmaculata (Fowler, 1899), holotype, ♀. 7. Original illustration provided by Fowler (1899: tab. xvi, fig. 21). 8. Body, in dorsal view, from Wilson et al. (2009).

Figs 3–6 in Platygonia Melichar, 1925 (Insecta: Hemiptera: Cicadellidae: Cicadellini): a new species from the Brazilian Amazon Rainforest, key to species of the genus, and notes on P. undecimmaculata (Fowler, 1899)

Figs 3–6. Platygonia nigra sp. nov., holotype, ♂ (INPA). 3. Pygofer, lateral view. 4. Subgenital plate, ventral view. 5. Connective and style, dorsal view. 6. Aedeagus, lateral view. Scale bars: 3 = 0.5 mm; 4–6 = 0.25 mm.

Figs 1–2 in Platygonia Melichar, 1925 (Insecta: Hemiptera: Cicadellidae: Cicadellini): a new species from the Brazilian Amazon Rainforest, key to species of the genus, and notes on P. undecimmaculata (Fowler, 1899)

Figs 1–2. Platygonia nigra sp. nov., holotype, ♂ (INPA). 1. Body, dorsal view. 2. Body, lateral view. Scale bars = 2 mm.

Fig. 9 in Platygonia Melichar, 1925 (Insecta: Hemiptera: Cicadellidae: Cicadellini): a new species from the Brazilian Amazon Rainforest, key to species of the genus, and notes on P. undecimmaculata (Fowler, 1899)

Fig. 9. Known distribution of species of Platygonia Melichar, 1925. Countries: BR = Brazil; CO = Colombia; CR = Costa Rica; EC = Ecuador; PA = Panama; PE = Peru.

opencc-by-4.0Jun 2022View details →

Fig. 1 in Igaponera curiosa, a new ponerine genus (Hymenoptera: Formicidae) from the Amazon

Fig. 1. Morphology-based consensus phylogenies depicting the placement of Igaponera gen. nov. within the Ponerinae (Formicidae). The trees were inferred through: A. maximum likelihood in IQ-TREE, and B. maximum parsimony in TNT, using a data set composed of 36 characters and 38 terminals: 36 ingroup taxa, and two outgroups (branches in grey). The numbers next to nodes represent statistical support (SHaLRT/Bootstrap/aBayes) and are only provided in the maximum likelihood tree (see Material and methods).

opencc-by-4.0Jun 2022View details →

Fig. 2 in Igaponera curiosa, a new ponerine genus (Hymenoptera: Formicidae) from the Amazon

Fig. 2. Strict consensus phylogeny, identical to that in Fig. 1B, obtained through maximum parsimony. The tree shows homoplastic (white square marks) and non-homoplastic (black square marks) apomorphies. Numbers above the marks represent the characters examined, and numbers below the marks represent character states (see also Supp. file 1).

opencc-by-4.0Aug 2022View details →

Database of Rural Technological Trajectories of the Legal Amazon delimited by the Method of Differentiation and Structural Signification of Rural Production

This database contains selected variables associated with the rural economic sector of the Brazilian Legal Amazon distributed at municipal level by technological trajectories (TT) – techno-productive trajectories and their technological variants (TTP) -, as defined and theoretically justified by Costa (2021, p. 217-219). The TTs are designed by a method that combines differentiation and structural signification of rural production in a given territory – hereafter, Method of Differentiation and Structural Signification of Rural Production (M-DESTRU). Structural differentiation (Phase 1) is necessary because production systems activities play different roles, depending on the systems  production modes and their territorial context: cattle ranching, for example, performs very different economic functions when practiced in family structures (peasants) in the municipalities of the Lower Amazonas, in comparison with wage-based farms in Southeast Pará; the roles played by temporary crops in the peasant systems of the Lower Tocantins are also quite different from those that are observed among employers' establishments in the Lower Amazon; and so on. This phase of the methodology qualifies these differences and has its procedures described on pages 441 and 442 of Costa (2021). In phase 2, M-DESTRU verifies how these structurally dissimilar activities, combine with others linked to the practices of the agents of each production mode, conforming convergences that result in distinct patterns. These patterns are semantically associated with TTs or TTPs structures that are in movement, and these structures all together make up for the region's rural economic system. This Phase's procedures are detailed on pages 441 and 442 of the aforementioned work. The territory of the Brazilian Legal Amazon encompasses 772 municipalities: all from eight states (Acre, Amapá, Amazonas, Mato Grosso, Pará, Rondônia, Roraima and Tocantins) and part of the State of Maranhão (west of the 44ºW meridian). The base data are from the Brazilian Institute of Geography and Statistics (IBGE), from the 1995, 2006 and 2017 Agricultural Censuses. The credit data for 2017 are from the Central Bank of Brazil. The dataset is organized as: Zen1995_LegalAmazon_Inicial.csv; Zen2006_LegalAmazon_Inicial.csv and Zen2017_LegalAmazon_Inicial.csv. In each table the column names are self-explanatory.   Reference: <ul> <li>Costa FA. 2021. Structural diversity and change in rural Amazonia: A comparative assessment of the technological trajectories based on agricultural censuses (1995, 2006 and 2017). Nova Economia 31(2).       </li> </ul>

opencc-by-4.0Apr 2024View details →

Fig. 15 in New species and records of Zebragryllus Desutter-Grandcolas & Cadena-Castañeda, 2014 (Orthoptera: Gryllidae: Gryllinae) from the Brazilian Amazon rainforest

Fig. 15. Zebragryllus nouragui Desutter-Grandcolas, 2014. A–B. ♂ (MPEG.HEX 05050471), habitus. A. Lateral view. B. Dorsal view. C–D. ♀ (MPEG.HEX 05050473), habitus. C. Lateral view. D. Dorsal view. E–H. ♂ (MPEG.HEX 05050472), phallic complex. E. Dorsal view. F. Ventral view. G. Axial view. H. Lateral view.

opencc-by-4.0Apr 2024View details →

Fig. 6 in New species and records of Zebragryllus Desutter-Grandcolas & Cadena-Castañeda, 2014 (Orthoptera: Gryllidae: Gryllinae) from the Brazilian Amazon rainforest

Fig. 6. Zebragryllus mebengokre Tavares, Oya & Cadena-Castañeda sp. nov., holotype, ♂ (MPEG. HEX 05050458). A–B. Habitus. A. Lateral view. B. Dorsal view. C. Frons. D. Lateral view of head and thorax. E. Tegmina, arrow = angle of the forewing mirror. F. Sternum. G. Maxillary palpi, outer view. H. Supra-anal plate. I. Terminalia, lateral view. J. Subgenital plate. Abbreviations: see Material and methods.