Proyecto "Determinación de especies según distribución geográfica"¶
Integrantes:
- Luis Arrieta Arrieta
- Stefany Solano González
Descripción¶
Este proyecto nace para evidenciar la falta de sistematización de biodiversidad que existe y cómo a pesar de que Costa Rica posee aproximadamente un 8% de la riqueza natural, esta no se visibiliza en bases de datos internacionales. Adicionalmente, en este trabajo queremos explorar los registros existentes en la base de datos del Catálogo de la vida, haciendo un pequeño énfasis en el grupo taxonómico Fungi. Como justificación de este proyecto, el Sistema Global de Información sobre Biodiversidad (GBIF por sus siglas en inglés) funge como una red internacional e infraestructura de datos financiada por los gobiernos del mundo para dar a cualquiera, en cualquier lugar, acceso abierto a datos sobre todas las formas de vida en la Tierra; no obstante la cuota de participacion en el depósito de estos datos evidencia otros rasgos como la participación científica de paises en estas redes. Como justificación de este proyecto, queremos explorar la distribución de los datos y ver si la cuota de participación en el deposito de estos en el Sistema Global de Información sobre Biodiversidad (GBIF por sus siglas en inglés) tiene una lata representación de países diversos, como Costa Rica ó si se encuentra dominada por algún otro factor, potencialmente relacionado a variables como financiamiento, poder adquisitivo, PIB invertido en ciencia, desarrollo científico etc.
Antecedentes¶
El conocimiento de la biodiversidad en el planeta es esencial para su aprovechamiento y protección. Entender el nicho, biología y potencial de grupos taxonómicos ha permitido que la sociedad desarrolle a partir de estos elementos de gran impacto y utilidad; con aplicación antibiótica, antiinflamatoria, biosintética, antihistamínica entre muchas otras (Pacyga et al. 2024). No obstante, existen grupos taxonómicos como los hongos (Blis & Gloer 2016) o bien ambientes de estudio donde el desconocimiento es elevado como en el caso de especies marinas (Rogers et al. 2022). Adicionalmente, en un inicio los registros de la biodiversidad eran manuales y poco personal tenia acceso a los mismos (Folk & Siniscalchi 2021) ya que se encontraban unificados en museos de paises desarrollados; sin embargo, el avance de la ciencia en sus múltiples dimensiones ha brindado un acceso masivo a la información y generación de datos; no obstante la sistematización de esta sigue siendo compleja (Kirk 2023, Alexander et al. 2024) y dificil de integrar. Aunado a esta complejidad se suma la poca participación o inclusión de países latinoamericanos con altos índices de biodiversidad, lo que dificulta visilibilizar el valor que natural que reside en estos y consecuentemente complica la implementación de politicas de protección, mitigación etc.
Descripción del problema y objetivo¶
Existe un catálogo de la vida, que unifica a todas las especies conocidas a la fecha (última actualización 26 de marzo/2024) y dada la relevancia internacional de Costa Rica como albergue del 8% de biodiversidad mundial deseamos evidenciar la cuota de participación Costarricense en este catálogo. Adicionalmente, el grupo taxonómico de los hongos es uno de los menos conocidos, explorados y categorizados, por lo que también enfocaremos nuestro estudio a este grupo con el fin de corroborar si efectivamente existe un desconocimiento real. Por lo tanto, nuestro objetivo consiste en explorar la distribución de organismos según región geográfica/país y conocer la participación costarricense y latinoamericana en estos registros; así como evidenciar el actual conocimiento existente en grupos taxonómicos específicos como el fúngico.
Instalación e importación de Bibliotecas¶
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#instalación de librerias
!pip install numpy
!pip install pandas
!pip install seaborn
!pip install scikit-learn
!pip install matplotlib
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#Librería para indices de diversidad
pip install scikit-bio
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In [ ]:
#importar librerias
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn import datasets
from ydata_profiling import ProfileReport
import matplotlib.pyplot as plt
%matplotlib inline
Importar set de Datos¶
Obtenidos del link: https://www.gbif.org/es/dataset/7ddf754f-d193-4cc9-b351-99906754a03b
Este conjunto de datos contiene 4 dataframes que recopilan datos sobre el catálogo de organismos de todas las especies conocidas en la tierra a la fecha. Este catálogo incluye especies extintas como vigentes y se cree que cubre por lo menos el 80% de las especies conocidas.
Las tablas de datos corresponden a
- Distribución
- Especies reportadas
- Taxones reportados
- Nombres vernaculares (autóctonos de cada región) para las especies indicadas
Cabe destacar que toda la información es en idioma inglés
Cita de los datos: Bánki, O., Roskov, Y., Döring, M., Ower, G., Hernández Robles, D. R., Plata Corredor, C. A., Stjernegaard Jeppesen, T., Örn, A., Vandepitte, L., Hobern, D., Schalk, P., DeWalt, R. E., Ma, K., Miller, J., Orrell, T., Aalbu, R., Abbott, J., Adlard, R., Aedo, C., et al. (2024). Catalogue of Life Checklist (Version 2024-03-26). Catalogue of Life. https://doi.org/10.48580/dfz8d
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#Cargar cada uno de los dataframes utilizando pandas
distribution = pd.read_csv('Distribution.tsv', sep='\t')
species = pd.read_csv('SpeciesProfile.tsv', sep='\t')
taxon = pd.read_csv('Taxon.tsv', sep='\t')
#Modificar encabezado de df para que sea más entendible [se elimina caracteres 'dwc']
distribution = distribution.rename(columns=lambda x: x.replace('dwc:',''))
species = species.rename(columns=lambda x: x.replace('dwc:',''))
taxon = taxon.rename(columns=lambda x: x.replace('dwc:',''))
<ipython-input-4-f448e452311d>:4: DtypeWarning: Columns (16) have mixed types. Specify dtype option on import or set low_memory=False.
taxon = pd.read_csv('Taxon.tsv', sep='\t')
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#Explorar tamaño de archivos con shape
print("distribution.shape:", distribution.shape)
print("species.shape:", species.shape)
print("taxon.shape:", taxon.shape)
distribution.shape: (104015, 6) species.shape: (473602, 5) taxon.shape: (31349, 22)
Análisis exploratorio¶
In [ ]:
distribution#Ver el dataframe
Out[ ]:
| taxonID | occurrenceStatus | locationID | locality | countryCode | dcterms:source | |
|---|---|---|---|---|---|---|
| 0 | 6L823 | native | NaN | Ecuador; Peru | NaN | NaN |
| 1 | T5NN | native | NaN | Panama | NaN | NaN |
| 2 | 7FVWC | native | mrgid:1912 | NaN | NaN | NaN |
| 3 | 7FVWC | native | mrgid:8402 | NaN | NaN | Ax, P., & Sopott-Ehlers, B. (1987). Otoplanida... |
| 4 | 3WT95 | native | tdwg:SUM | NaN | NaN | Group, S.F. (2023) SF specimen locality data f... |
| ... | ... | ... | ... | ... | ... | ... |
| 104010 | 6L7TZ | native | tdwg:PAN | NaN | NaN | NaN |
| 104011 | 6L7TZ | native | tdwg:PER | NaN | NaN | NaN |
| 104012 | 6L7TZ | native | tdwg:VEN | NaN | NaN | NaN |
| 104013 | 6BNZY | native | NaN | Congo | NaN | NaN |
| 104014 | 9M79Q | native | tdwg:ABT-OO | NaN | NaN | Newton, A.F. (2021) StaphBase: Staphyliniformi... |
104015 rows × 6 columns
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df['locality'].unique()#Ver datos de la variable
Out[ ]:
array(['Ecuador; Peru', 'Panama', nan, ...,
'Europe (AU BE BH CZ DE FI FR GB GE GR HU IT NL NR PL RO SK SL SP SV SZ UK), Russia (n+s European)',
'NE USA; USA: Indiana; USA: New York; USA: West Virginia',
'China (Guizhou, Zhejiang)'], dtype=object)
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species#ver data frame
Out[ ]:
| taxonID | gbif:isExtinct | gbif:isMarine | gbif:isFreshwater | gbif:isTerrestrial | |
|---|---|---|---|---|---|
| 0 | 8XRM | False | False | False | True |
| 1 | 6D3DF | NaN | True | False | False |
| 2 | 49V84 | NaN | True | False | False |
| 3 | BYZP2 | True | NaN | NaN | NaN |
| 4 | 3JK6Z | False | False | False | True |
| ... | ... | ... | ... | ... | ... |
| 473597 | 9QLSL | false | NaN | NaN | NaN |
| 473598 | BJBQ4 | false | NaN | NaN | NaN |
| 473599 | 6YNKM | false | NaN | NaN | NaN |
| 473600 | 6KDRH | false | False | False | True |
| 473601 | 35CZM | fals | NaN | NaN | NaN |
473602 rows × 5 columns
In [ ]:
taxon
Out[ ]:
| taxonID | parentNameUsageID | acceptedNameUsageID | originalNameUsageID | scientificNameID | datasetID | taxonomicStatus | taxonRank | scientificName | scientificNameAuthorship | ... | infragenericEpithet | specificEpithet | infraspecificEpithet | cultivarEpithet | nameAccordingTo | namePublishedIn | nomenclaturalCode | nomenclaturalStatus | taxonRemarks | dcterms:references | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 9FSLC | 92BPW | NaN | NaN | ---3nn39ZQdkDGBvoaGdR2 | 55434.0 | accepted | species | Homaloxestis australis Park, 2004 | Park, 2004 | ... | NaN | australis | NaN | NaN | NaN | Park, K.-T. (2004) Genus Homaloxestis Meyrick ... | ICZN | nomen legitimum | NaN | NaN |
| 1 | 8XRM | 8NLB3 | NaN | NaN | ---6f-YWvlv8BS-R6m-8Y | 1050.0 | accepted | species | Acanthograeffea modesta Günther, 1932 | Günther, 1932 | ... | NaN | modesta | NaN | NaN | NaN | Günther, K. (1932) Beiträge zur Systematik und... | ICZN | nomen legitimum | NaN | NaN |
| 2 | 6D3DF | 7NWBC | NaN | NaN | ---9Qo8j1JQR04niBsWYb0 | 1191.0 | accepted | species | Diarthrodes gravellicola Soyer, 1975 | Soyer, 1975 | ... | NaN | gravellicola | NaN | NaN | NaN | Soyer, J. (1975). Contribution a l’étude des C... | ICZN | nomen validum | NaN | https://www.marinespecies.org/copepoda/aphia.p... |
| 3 | 47BF5 | 63SP | NaN | NaN | ---BEZLG8WfmCKzOoARWg1 | 1141.0 | accepted | species | Neurotheca congolana De Wild. & T. Durand | De Wild. & T. Durand | ... | NaN | congolana | NaN | NaN | NaN | De Wild. & T. Durand. (1899). In: Compt. Rend.... | ICN | NaN | NaN | http://www.worldplants.de/?deeplink=Neurotheca... |
| 4 | 5BVQY | 87PB | NaN | NaN | ---D7syAmBAb7tLYVFT3L2 | 1141.0 | accepted | species | Weberbauerocereus cephalomacrostibas (Werderm.... | (Werderm. & Backeb.) F. Ritter | ... | NaN | cephalomacrostibas | NaN | NaN | NaN | Ritter, F. (1981). In: Kakteen Südamer. 4: 1353. | ICN | NaN | NaN | http://www.worldplants.de/?deeplink=Weberbauer... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 31344 | BTY38 | BTWH2 | NaN | NaN | -W4y6bo2Lksp9tq-VpszI | 2299.0 | accepted | suborder | Orthotetidina Waagen, 1884 | Waagen, 1884 | ... | NaN | NaN | NaN | NaN | NaN | Waagen, W. H. (1884). Productus Limestone Foss... | ICZN | nomen validum | NaN | https://www.marinespecies.org/aphia.php?p=taxd... |
| 31345 | 7CZ6W | 83V5 | NaN | NaN | -W519nFjr9suClYRetk6q2 | 1141.0 | accepted | species | Turnera dasytricha Pilg. | Pilg. | ... | NaN | dasytricha | NaN | NaN | NaN | Pilg. (1902). In: Bot. Jahrb. Syst. 30: 176. | ICN | NaN | NaN | http://www.worldplants.de/?deeplink=Turnera-da... |
| 31346 | 6W2JF | 6SBV | NaN | NaN | -W56CNeiw5dk2Su0z29DX2 | 1027.0 | accepted | species | Plectris luctuosa Frey, 1967 | Frey, 1967 | ... | NaN | luctuosa | NaN | NaN | NaN | Frey, G. (1967). Die Gattung Plectris (Philoch... | ICZN | NaN | NaN | NaN |
| 31347 | 9YKMW | NaN | 3R6S7 | NaN | -W5K30rzgvcP9lNsPt33i0 | 1011.0 | synonym | species | Seliza bisecta Kirby, 1891 | Kirby, 1891 | ... | NaN | bisecta | NaN | NaN | NaN | NaN | ICZN | NaN | NaN | NaN |
| 31348 | 4HQWW | 9CLPF | NaN | 0Xhs7bfwG98S5S8G63Jxw | -W5LTi_j2Ftx5OuOeipNL | 2304.0 | accepted | specie | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
31349 rows × 22 columns
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# Obtener los taxones con variables en común
common_taxonIDs = set(distribution['taxonID']).intersection(species['taxonID']).intersection(taxon['taxonID'])
print(common_taxonIDs)
{'6RBK4', 'D5ZP', '7CTDT', '7RZW5', '72P8R', 'BP7FL', '86986', '8GWQF', '8G3C5', 'GVSX', '676MZ', '6WHSF', '3DVL6', '3KVG8', '6P75C', '524CV', '9GFZD', '9BWKL', '4KQQW', '532JC', '86JNS', '5L38J', '86S4B', '9BCPC', '5FPVK', '552PQ', '7ZLYJ', '46LW8', '9F568', '8QJLB', 'LJ2C', '7BFPS', '6TSHS', '6HWHQ', '8P8WT', 'BN3LN', '4HYYF', '5KW94', '9YQM', 'PWD5', '4B8GS', '86FMH', '85YY4', '555YN', '7XMSR', '699HV', '4MLC6', '6RHNT', '47FD6', '7SX8S', '4QTDQ', '64SQ4', '7JZPT', 'JR22', '39YWT', '9J5CP', '559WJ', '8P9YT', '3H55S', '4BQMW', '4V63V', '4GD5Q', '3TQMR', '7DF9C', '7ZPML', '74KSW', '4PCM4', '64ZXD', '4WXCC', 'JWWP', '3QPR3', '6TWPK', 'D58M', '3GD7V', 'C4WM4', '8TDY9', '4MVL7', '3CGQK', '64VN3', '33HGB', '5X5GN', '3P6VT', '6KWGY', '5TX57', 'WK2C', '4KS6F', '4WWMQ', '7ZBLC', '4DJ6G', 'H66D', '79HGH', '3S468', '894CG', 'RVYX', 'B43VH', '3HDR7', '854PQ', '56V4T', 'CQ5G', 'B75BM', '7F8TJ', '46TDG', '93KL8', '6MMCC', '4VZMP', 'B7623', 'NC39', '3LGQD', '6BGKN', '67YTD', '6M566', '3L7YN', '69KXK', '7TDW6', '6SGW7', '76XPY', '7QQNJ'}
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#Unir los 3 dataframe por la columna taxonID, manteniendo las filas que no hacen match
df = distribution.merge(species, on='taxonID', how='outer').merge(taxon, on='taxonID', how='outer')
df
Out[ ]:
| taxonID | occurrenceStatus | locationID | locality | countryCode | dcterms:source | gbif:isExtinct | gbif:isMarine | gbif:isFreshwater | gbif:isTerrestrial | ... | infragenericEpithet | specificEpithet | infraspecificEpithet | cultivarEpithet | nameAccordingTo | namePublishedIn | nomenclaturalCode | nomenclaturalStatus | taxonRemarks | dcterms:references | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 6L823 | native | NaN | Ecuador; Peru | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | T5NN | native | NaN | Panama | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2 | 7FVWC | native | mrgid:1912 | NaN | NaN | NaN | NaN | True | False | False | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 3 | 7FVWC | native | mrgid:8402 | NaN | NaN | Ax, P., & Sopott-Ehlers, B. (1987). Otoplanida... | NaN | True | False | False | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | 3WT95 | native | tdwg:SUM | NaN | NaN | Group, S.F. (2023) SF specimen locality data f... | False | False | False | True | ... | NaN | luctuosa | NaN | NaN | NaN | Brunner von Wattenwyl, C. (1888) Monographie d... | ICZN | nomen legitimum | NaN | NaN |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 3115289 | 7KXQ3 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | longa | parviflora | NaN | NaN | Maire, & Weiller. (1961). In: Fl. Afrique N. 7... | ICN | NaN | NaN | NaN |
| 3115290 | 76H4K | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | sphaerica | NaN | NaN | NaN | H. Schaef., S. S. Renner. (2011). In: Taxon 60... | ICN | NaN | NaN | http://www.worldplants.de/?deeplink=Penelopeia... |
| 3115291 | 76MKV | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | nigella | NaN | NaN | NaN | Cuatrec. (1981). In: Phytologia 49(3): 248. | ICN | NaN | NaN | NaN |
| 3115292 | 3NNXQ | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | acuta | NaN | NaN | NaN | NaN | ICZN | nomen legitimum | NaN | NaN |
| 3115293 | VTHP | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
3115294 rows × 31 columns
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#Explorar columnas de cada df individualmente
print("distribution.shape:", distribution.columns)
print("species.shape:", species.columns)
print("taxon.shape:", taxon.columns)
distribution.shape: Index(['taxonID', 'occurrenceStatus', 'locationID', 'locality', 'countryCode',
'dcterms:source'],
dtype='object')
species.shape: Index(['taxonID', 'gbif:isExtinct', 'gbif:isMarine', 'gbif:isFreshwater',
'gbif:isTerrestrial'],
dtype='object')
taxon.shape: Index(['taxonID', 'parentNameUsageID', 'acceptedNameUsageID',
'originalNameUsageID', 'scientificNameID', 'datasetID',
'taxonomicStatus', 'taxonRank', 'scientificName',
'scientificNameAuthorship', 'col:notho', 'genericName',
'infragenericEpithet', 'specificEpithet', 'infraspecificEpithet',
'cultivarEpithet', 'nameAccordingTo', 'namePublishedIn',
'nomenclaturalCode', 'nomenclaturalStatus', 'taxonRemarks',
'dcterms:references'],
dtype='object')
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# Generar el informe con pandas-profiling para df SpeciesProfile
df_profile = ProfileReport(df, title="Informe Pandas Profiling - SpeciesProfile Dataset", explorative=True)
df_profile.to_file("species_report.html")
/usr/local/lib/python3.10/dist-packages/ydata_profiling/profile_report.py:363: UserWarning: Try running command: 'pip install --upgrade Pillow' to avoid ValueError warnings.warn(
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In [ ]:
df_profile