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
In [ ]:
#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')
In [ ]:
#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
In [ ]:
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)
In [ ]:
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
In [ ]:
# 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'}
In [ ]:
#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
In [ ]:
#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')
In [ ]:
# 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(
Summarize dataset: 0%| | 0/5 [00:00<?, ?it/s]
Generate report structure: 0%| | 0/1 [00:00<?, ?it/s]
Render HTML: 0%| | 0/1 [00:00<?, ?it/s]
Export report to file: 0%| | 0/1 [00:00<?, ?it/s]
In [ ]:
df_profile
Out[ ]:
Según el informe de Pandas para el data frame Species (el cuál lista el tipo de especie en cuestión - extinta, marina, terrestre, etc), esta presenta un 30.6% de datos faltantes. Se contabiliza un total de 1,665,788 de registros; de estos el 12.3% se encuentran extintos y de los remanentes la mayoría se clasifican como terrestres ( 47%).
Por otro lado, según el informe de Pandas para el data frame Distribution (el cuál detalla las categorías de las especies como nativas, domesticadas, alien y desconocidas; así como la localidad del registro) presenta un 46.2% de datos faltantes. La variable de especies nativas se encuentra altamente desbalanceada pues cerca del 99% están anotadas como nativas.
En cuanto al data frame Taxon, de manera exploratoria el informe de Pandas presenta un 43% de datos faltantes. La categoría de clasificación dominante corresponde a especies (75% de los datos), con 33 especies únicas.
In [ ]:
# Retornar el número de valores únicos en cada columna del dataframe
print('Valores únicos para df Distribution', df.nunique(),'\n')
Valores únicos para df Distribution taxonID 528630 occurrenceStatus 4 locationID 2776 locality 9715 countryCode 196 dcterms:source 3816 gbif:isExtinct 5 gbif:isMarine 2 gbif:isFreshwater 2 gbif:isTerrestrial 2 parentNameUsageID 11880 acceptedNameUsageID 15256 originalNameUsageID 4385 scientificNameID 31349 datasetID 107 taxonomicStatus 5 taxonRank 32 scientificName 31345 scientificNameAuthorship 22855 col:notho 1 genericName 17960 infragenericEpithet 1472 specificEpithet 19940 infraspecificEpithet 3314 cultivarEpithet 0 nameAccordingTo 1 namePublishedIn 19086 nomenclaturalCode 4 nomenclaturalStatus 8 taxonRemarks 552 dcterms:references 9814 dtype: int64
Las dataframes para los análisis más detallados corresponden a Distribution y Taxon, que serán las que se utilizaran subsecuentemente.
Gráfico de países con más accesos en Catálogo de la Vida¶
In [ ]:
locality_counts = df_loc['Country'].value_counts()
top40 = locality_counts.head(n=40)
tail4040 = locality_counts.tail(n= 40)
##GRAFICO TOP40
# Tamaño del gráfico
plt.figure(figsize=(6, 6)) #primera "capa de la figura"
# Contar total por país
for value, count in top40.items():
print(f'{value}: {count}')
# Crear gráfico de barras
#grafico de barra vertical
top40.plot(kind='barh')
# Título del gráfico
plt.title('Top 40 de países con más especies')
# Etiqueta del eje X
plt.xlabel('Cantidad de especies')
# Etiqueta del eje Y
plt.ylabel('Región Geográfica')
# Inclinación de las etiquetas del eje X
#rota el texto
plt.xticks(rotation=40)
plt.grid()
plt.show()
China: 5064 Brazil: 4145 Indonesia: 3254 South Africa: 2877 United States: 2257 Mexico: 2254 Argentina: 2202 India: 2155 Australia: 2004 Bolivia, Plurinational State of: 1768 Bolivia: 1768 Malaysia: 1736 Congo, the Democratic Republic of the: 1695 Philippines: 1578 Peru: 1479 Papua New Guinea: 1462 Viet Nam: 1422 Vietnam: 1422 Japan: 1331 Madagascar: 1304 Turkey: 1303 Colombia: 1266 Russian Federation: 1257 Russia: 1257 Canada: 1199 Italy: 1152 Lao People's Democratic Republic: 1149 Tanzania, United Republic of: 1145 Chile: 1109 Ecuador: 1104 Costa Rica: 1071 Panama: 1021 Kazakhstan: 1000 Cameroon: 951 Greece: 914 French Guiana: 902 Taiwan: 891 Thailand: 888 Kenya: 849 Guatemala: 828
Gráfico de países con menos accesos en Catálogo de la Vida¶
In [ ]:
locality_counts = df_loc['Country'].value_counts()
top40 = locality_counts.head(n=40)
tail40 = locality_counts.tail(n= 40)
#GRAFICO TAIL40
# Tamaño del gráfico
plt.figure(figsize=(6, 6)) #primera "capa de la figura"
# Crear gráfico de barras
#grafico de barra vertical
tail40.plot(kind='barh')
# Título del gráfico
plt.title('Top 40 de países con menos especies')
# Etiqueta del eje X
plt.xlabel('Cantidad de especies')
# Etiqueta del eje Y
plt.ylabel('Región Geográfica')
# Inclinación de las etiquetas del eje X
#rota el texto
plt.xticks(rotation=40)
plt.grid()
plt.show()
In [ ]:
taxon.columns#Ver columnas del dataframe
Out[ ]:
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')
In [ ]:
taxon_counts = taxon['genericName'].value_counts()#Realizar conteo por genero de individuos
head_taxon40 = taxon_counts.head(n=40)
tail_taxon40 = taxon_counts.tail(n= 40)
head_taxon40
Out[ ]:
genericName Hieracium 365 Rubus 218 Astragalus 112 Helix 111 Senecio 103 Rosa 95 Solanum 84 Ranunculus 77 Agaricus 77 Ficus 73 Simulium 70 Zygaena 70 Drosophila 64 Potentilla 63 Viola 63 Tabanus 63 Bryum 62 Piper 62 Unio 62 Eugenia 60 Cortinarius 60 Salix 57 Taraxacum 55 Acacia 55 Peperomia 53 Centaurea 53 Otiorhynchus 53 Polypodium 51 Puccinia 51 Asplenium 51 Andrena 51 Sphagnum 50 Megaselia 50 Conus 50 Lecidea 50 Hypnum 49 Aphis 48 Onthophagus 48 Rhododendron 48 Eupithecia 48 Name: count, dtype: int64
Gráfico de las especies más comunes globalmente en Dataframe Taxon¶
In [ ]:
taxon_counts = taxon['genericName'].value_counts()
head_taxon40 = taxon_counts.head(n=40)
tail_taxon40 = taxon_counts.tail(n= 40)
##GRAFICO TOP40
# Tamaño del gráfico
plt.figure(figsize=(6, 6)) #primera "capa de la figura"
# Crear gráfico de barras
# sepal_mean.plot(kind='bar') #grafico de barra vertical
head_taxon40.plot(kind='barh') #barh, es horizontal
# Título del gráfico
plt.title('Top 40 de especies más comunes globalmente')
# Etiqueta del eje X
plt.xlabel('Cantidad de especies')
# Etiqueta del eje Y
plt.ylabel('Nombre común')
# Inclinación de las etiquetas del eje X
#para que quepa las letras del titulo
#rota el texto
plt.xticks(rotation=40)
plt.grid()
plt.show()
Mapas¶
Agregar coordenadas por pais
In [ ]:
countries_code = pd.read_csv("countries.csv")
countries_code
Out[ ]:
| Country | Alpha-2 | Alpha-3 | Numeric code | Latitude | Longitude | |
|---|---|---|---|---|---|---|
| 0 | Afghanistan | AF | AFG | 4 | 33.000000 | 65.0 |
| 1 | Åland Islands | AX | ALA | 248 | 60.116667 | 19.9 |
| 2 | Albania | AL | ALB | 8 | 41.000000 | 20.0 |
| 3 | Algeria | DZ | DZA | 12 | 28.000000 | 3.0 |
| 4 | American Samoa | AS | ASM | 16 | -14.333300 | -170.0 |
| ... | ... | ... | ... | ... | ... | ... |
| 257 | Wallis and Futuna | WF | WLF | 876 | -13.300000 | -176.2 |
| 258 | Western Sahara | EH | ESH | 732 | 24.500000 | -13.0 |
| 259 | Yemen | YE | YEM | 887 | 15.000000 | 48.0 |
| 260 | Zambia | ZM | ZMB | 894 | -15.000000 | 30.0 |
| 261 | Zimbabwe | ZW | ZWE | 716 | -20.000000 | 30.0 |
262 rows × 6 columns
In [ ]:
df_loc = df.dropna(subset=['countryCode'])#Eliminar NAs
countries_code.rename(columns={'Alpha-2': 'countryCode'}, inplace=True)#Cambiar nombre de la columna para hacer el merge
# Fusionar los DataFrames en base a la columna 'countryCode'
df_loc = pd.merge(df_loc, countries_code, on='countryCode', how='left')
In [ ]:
df_loc
Out[ ]:
| taxonID | occurrenceStatus | locationID | locality | countryCode | dcterms:source | gbif:isExtinct | gbif:isMarine | gbif:isFreshwater | gbif:isTerrestrial | ... | namePublishedIn | nomenclaturalCode | nomenclaturalStatus | taxonRemarks | dcterms:references | Country | Alpha-3 | Numeric code | Latitude | Longitude | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | QK5N | native | NaN | NaN | US | NaN | True | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | United States | USA | 840 | 38.0 | -97.0 |
| 1 | 9KSLK | native | NaN | NaN | BR | Zanol, K.M.R. (2000a) Scaphytopius Ball (Homop... | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | Brazil | BRA | 76 | -10.0 | -55.0 |
| 2 | 34HD8 | native | NaN | NaN | AO | Stiller, M. (2001a) The Afrotropical leafhoppe... | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | Angola | AGO | 24 | -12.5 | 18.5 |
| 3 | 34HD8 | native | NaN | NaN | NG | Stiller, M. (2001a) The Afrotropical leafhoppe... | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | Nigeria | NGA | 566 | 10.0 | 8.0 |
| 4 | 34HD8 | native | NaN | NaN | CG | Stiller, M. (2001a) The Afrotropical leafhoppe... | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | Congo | COG | 178 | -1.0 | 15.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 109769 | 5XXLB | native | NaN | NaN | CN | NaN | False | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | China | CHN | 156 | 35.0 | 105.0 |
| 109770 | 5XXLB | native | NaN | NaN | VN | NaN | False | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | Viet Nam | VNM | 704 | 16.0 | 106.0 |
| 109771 | 5XXLB | native | NaN | NaN | VN | NaN | False | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | Vietnam | VNM | 704 | 16.0 | 106.0 |
| 109772 | 6PKLW | native | NaN | NaN | CN | NaN | False | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | China | CHN | 156 | 35.0 | 105.0 |
| 109773 | BS95R | native | NaN | NaN | CN | NaN | False | NaN | NaN | NaN | ... | VIKTORA Petr. (2023). New species of Demonax T... | ICZN | NaN | NaN | http://titan.gbif.fr/sel_genann1.php?numero=41709 | China | CHN | 156 | 35.0 | 105.0 |
109774 rows × 36 columns
In [ ]:
df_loc['Country'].unique()#Ver informacion de la columna country
Out[ ]:
array(['United States', 'Brazil', 'Angola', 'Nigeria', 'Congo', 'Uganda',
'Ghana', 'Madagascar', 'Australia', 'Philippines', 'Albania',
'Gabon', 'Chile', 'Panama', 'India', 'Papua New Guinea', 'Tunisia',
'Hungary', 'Morocco', 'Afghanistan', 'Iran, Islamic Republic of',
'Uzbekistan', 'China', 'Belgium', 'Luxembourg', 'Netherlands',
'Switzerland', 'Slovenia', 'Germany', 'Croatia', 'Israel',
'Jordan', 'Kyrgyzstan', 'Greece', 'Ukraine', 'Turkmenistan',
'Kazakhstan', 'Tajikistan', 'Iraq', 'Turkey', 'Armenia', 'Canada',
'United Kingdom', 'Italy', 'Mexico', 'Serbia', 'Malaysia',
'Austria', 'Japan', 'Pakistan', 'Ethiopia', 'Indonesia',
'Thailand', 'Guatemala', 'Algeria',
'Bolivia, Plurinational State of', 'Bolivia', 'Denmark', 'Poland',
'Sweden', 'Czech Republic', 'Puerto Rico', 'New Zealand', 'France',
'Guinea', 'Viet Nam', 'Vietnam', 'Nicaragua', 'Liberia', 'Ecuador',
'Peru', 'Argentina', 'Russia', 'Russian Federation',
'Venezuela, Bolivarian Republic of', 'Venezuela', 'Saudi Arabia',
'Fiji', 'Burma', 'Myanmar', 'Hong Kong', 'Taiwan', 'Cambodia',
"Lao People's Democratic Republic", 'Bulgaria', 'Romania',
'South Africa', 'Paraguay', 'Azerbaijan', 'Estonia', 'Finland',
'Latvia', 'Moldova, Republic of', 'Norway', 'Slovakia', 'Ireland',
'Lithuania', 'Belarus', 'Mongolia',
'Congo, the Democratic Republic of the', 'Korea, Republic of',
'South Korea', 'Nepal', 'Cayman Islands', 'Spain', 'Georgia',
'Colombia', 'Cameroon', 'Micronesia, Federated States of', 'Palau',
'Mauritius', 'Kenya', 'Sudan', 'Lebanon', 'Libya',
'Libyan Arab Jamahiriya', 'Tanzania, United Republic of',
'Sri Lanka', 'Solomon Islands', 'Bhutan', 'Zambia',
"Côte d'Ivoire", 'Ivory Coast', 'Malawi', 'Bosnia and Herzegovina',
'Macedonia, the former Yugoslav Republic of', 'Sierra Leone',
'Singapore', 'Central African Republic', 'New Caledonia',
'Equatorial Guinea', 'Mozambique', 'Rwanda', 'Togo',
'Sao Tome and Principe', 'Costa Rica', 'Guyana', 'French Guiana',
'Suriname', 'Honduras', 'Belize', 'Trinidad and Tobago', 'Somalia',
'Syrian Arab Republic', 'Mali', 'Oman', 'Uruguay', 'Greenland',
'Brunei Darussalam', 'Brunei', 'Dominican Republic', 'Bangladesh',
'Yemen', 'Montenegro', 'Christmas Island', 'Dominica', 'Jamaica',
'Cuba', 'Egypt', 'Eritrea', 'Senegal', 'Chad', 'Malta',
'Virgin Islands, U.S.', 'Marshall Islands',
'Northern Mariana Islands', 'Guam', 'Samoa', 'Zimbabwe',
'Portugal', 'Barbados', 'Bahamas', 'Bermuda', 'Grenada',
'Guadeloupe', 'Martinique', 'Montserrat',
'Saint Vincent & the Grenadines',
'Saint Vincent and the Grenadines',
'St. Vincent and the Grenadines', 'Saint Lucia', 'Botswana',
'Gambia', 'Comoros', 'Guinea-Bissau', 'Burundi', 'Benin',
'French Polynesia', 'Burkina Faso', 'Niger', 'Réunion', 'Cyprus',
'El Salvador', 'Saint Helena, Ascension and Tristan da Cunha',
'Haiti', 'Western Sahara', 'United Arab Emirates',
'Antigua and Barbuda', 'Saint Kitts and Nevis', 'Seychelles',
'Palestinian Territory, Occupied', 'Svalbard and Jan Mayen',
'Timor-Leste', 'Swaziland', 'Aruba', 'Vanuatu', 'Djibouti'],
dtype=object)
Mapa 1
In [ ]:
import plotly.express as px
import pandas as pd
# Create scatter map
fig = px.scatter_geo(df_loc, lat='Latitude ', lon='Longitude ', #color='genericName',
hover_name='Country', #size='mag',
title='Distribution Species Around the World')
fig.show()
In [ ]:
df_loc['Type'] = 'Unknown'#Crear columna llamada Type y que todas las filas digan desconocido
df_loc.loc[df['gbif:isTerrestrial'] == True, 'Type'] = 'Terrestrial'#Poner en la columna Type los que son terrestres
df_loc.loc[df['gbif:isMarine'] == True, 'Type'] = 'Marine'#Poner en la columna Type lo que son marine
df_loc.loc[df['gbif:isFreshwater'] == True, 'Type'] = 'Freshwater'#Poner en la columna los que son Freshwater
df_loc.loc[df['gbif:isExtinct'] == True, 'Type'] = 'Extinct'#Poner en la columna Type los que son Extinct
In [ ]:
df_loc['Type'].unique()#Revisar que se realizaran los cambios en la columna Type
Out[ ]:
array(['Unknown', 'Terrestrial', 'Freshwater', 'Marine', 'Extinct'],
dtype=object)
In [ ]:
fig = px.scatter_geo(df_loc, lat='Latitude ', lon='Longitude ', color='Type',
hover_name='Country', #size='mag',
title='Distribution Species Around the World')
fig.show()
In [ ]:
df_loc.loc[df_loc['genericName'] == 'Na', 'genericName'] = 'Unknown'
In [ ]:
import plotly.express as px
fig = px.scatter_geo(df_loc, lat='Latitude ', lon='Longitude ', color='genericName',
hover_name='genericName', title='Distribution Species Around the World',
color_discrete_sequence=px.colors.qualitative.Light24) # Usando una paleta de colores más amplia
fig.show()
Community Diversity¶
Preparar la matriz de ausencia-presencia¶
In [ ]:
df_cleaned = df_loc.dropna(subset=['genericName', 'Country'])# Vamos a filtrar el df para quitar los Na de estas columnas de interes
df_cleaned = df_cleaned[['genericName', 'Country']]
df_cleaned
Out[ ]:
| genericName | Country | |
|---|---|---|
| 33 | Euscelidius | Afghanistan |
| 34 | Euscelidius | Iran, Islamic Republic of |
| 35 | Euscelidius | Israel |
| 36 | Euscelidius | Jordan |
| 37 | Euscelidius | Kyrgyzstan |
| ... | ... | ... |
| 108605 | Atheta | Chile |
| 108606 | Brasiliosoma | Brazil |
| 108611 | Stenus | Indonesia |
| 108614 | Emeopedus | Papua New Guinea |
| 108622 | Ropica | Solomon Islands |
16298 rows × 2 columns
In [ ]:
df_cleaned['genericName'].nunique()# Contabilizar cantidad de variables diferentes en esta columna
Out[ ]:
4053
In [ ]:
df_cleaned['Country'].nunique()# Contabilizar cantidad de variables diferentes en esta columna
Out[ ]:
220
In [ ]:
taxon_countss = df_cleaned['genericName'].value_counts()#Crean un dataframe con las especies segun las veces qe aparece
head_taxon40 = taxon_countss.head(n=40)#Primeros 40
tail_taxon40 = taxon_countss.tail(n= 40)#Ultimos 40
head_taxon40
Out[ ]:
genericName Pterolophia 301 Glenea 296 Phytoecia 237 Dorcadion 204 Prosopocera 183 Otiorhynchus 176 Demonax 166 Eunidia 165 Exocentrus 147 Agapanthia 146 Nupserha 138 Monochamus 119 Chlorophorus 113 Eupteryx 102 Oberea 98 Acalolepta 96 Sybra 95 Xylotrechus 92 Pogonocherus 87 Cortodera 87 Xystrocera 85 Rhaphuma 78 Hyllisia 76 Eburodacrys 75 Psammotettix 75 Sophronica 74 Crossotus 73 Purpuricenus 70 Pidonia 69 Oncideres 69 Tmesisternus 66 Apomecyna 65 Niphona 63 Ceresium 61 Tetropium 60 Megacyllene 56 Aegomorphus 55 Colobothea 53 Serixia 53 Lepturges 53 Name: count, dtype: int64
In [ ]:
pivoted_df = df_cleaned.pivot_table(index='Country', columns='genericName', aggfunc=len, fill_value=0)#Convertir el dataframe a una matriz de presencia ausencia
In [ ]:
pivoted_df
Out[ ]:
| genericName | Abana | Abanycha | Abaraeus | Abauba | Abichites | Abimwa | Abraeomorphus | Abroma | Abryna | Acabanga | ... | Zorilispe | Zorilispiella | Zorion | Zotalemimon | Zyginidia | Zygocera | Zyras | Zyrcosa | Zyrcosoides | Zyzzogeton |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Country | |||||||||||||||||||||
| Afghanistan | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Albania | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Algeria | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Andorra | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Angola | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| Virgin Islands, U.S. | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Western Sahara | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Yemen | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Zambia | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Zimbabwe | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
220 rows × 4053 columns
In [ ]:
pivoted_df['Pterolophia'].sum()# Confirmar que el pivot longer fue exitoso, si es el caso porque anteriormente dio 301 en el top 40
Out[ ]:
301
In [ ]:
data = pivoted_df.values.tolist()# Convertir pivoted_df a una lista de listas
In [ ]:
print(data[:6])
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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0, 0, 0, 0, 0, 0, 0, 0, 0]]
In [ ]:
ids = list(pivoted_df.index)#Indice del dataframe contiene los nombres de paises se convierte en una lista
ids
Out[ ]:
['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola', 'Anguilla', 'Argentina', 'Armenia', 'Aruba', 'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bermuda', 'Bhutan', 'Bolivia', 'Bolivia, Plurinational State of', 'Bosnia and Herzegovina', 'Botswana', 'Brazil', 'Brunei', 'Brunei Darussalam', 'Bulgaria', 'Burkina Faso', 'Burma', 'Burundi', 'Cambodia', 'Cameroon', 'Canada', 'Cape Verde', 'Cayman Islands', 'Central African Republic', 'Chad', 'Chile', 'China', 'Christmas Island', 'Colombia', 'Comoros', 'Congo', 'Congo, the Democratic Republic of the', 'Costa Rica', 'Croatia', 'Cuba', 'Cyprus', 'Czech Republic', "Côte d'Ivoire", 'Denmark', 'Djibouti', 'Dominican Republic', 'Ecuador', 'Egypt', 'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Estonia', 'Ethiopia', 'Faroe Islands', 'Fiji', 'Finland', 'France', 'French Guiana', 'French Polynesia', 'Gabon', 'Gambia', 'Georgia', 'Germany', 'Ghana', 'Greece', 'Greenland', 'Grenada', 'Guadeloupe', 'Guam', 'Guatemala', 'Guinea', 'Guinea-Bissau', 'Guyana', 'Haiti', 'Honduras', 'Hong Kong', 'Hungary', 'Iceland', 'India', 'Indonesia', 'Iran, Islamic Republic of', 'Iraq', 'Ireland', 'Israel', 'Italy', 'Ivory Coast', 'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya', "Korea, Democratic People's Republic of", 'Korea, Republic of', 'Kuwait', 'Kyrgyzstan', "Lao People's Democratic Republic", 'Latvia', 'Lebanon', 'Liberia', 'Libya', 'Libyan Arab Jamahiriya', 'Liechtenstein', 'Lithuania', 'Luxembourg', 'Macao', 'Macedonia, the former Yugoslav Republic of', 'Madagascar', 'Malawi', 'Malaysia', 'Mali', 'Malta', 'Marshall Islands', 'Martinique', 'Mauritania', 'Mauritius', 'Mexico', 'Micronesia, Federated States of', 'Moldova, Republic of', 'Mongolia', 'Montenegro', 'Montserrat', 'Morocco', 'Mozambique', 'Myanmar', 'Nepal', 'Netherlands', 'New Caledonia', 'New Zealand', 'Nicaragua', 'Niger', 'Nigeria', 'Northern Mariana Islands', 'Norway', 'Oman', 'Pakistan', 'Palau', 'Palestinian Territory, Occupied', 'Panama', 'Papua New Guinea', 'Paraguay', 'Peru', 'Philippines', 'Poland', 'Portugal', 'Puerto Rico', 'Qatar', 'Romania', 'Russia', 'Russian Federation', 'Rwanda', 'Réunion', 'Saint Kitts and Nevis', 'Saint Lucia', 'Saint Vincent & the Grenadines', 'Saint Vincent and the Grenadines', 'Samoa', 'Sao Tome and Principe', 'Saudi Arabia', 'Senegal', 'Serbia', 'Seychelles', 'Sierra Leone', 'Singapore', 'Slovakia', 'Slovenia', 'Solomon Islands', 'Somalia', 'South Africa', 'South Korea', 'Spain', 'Sri Lanka', 'St. Vincent and the Grenadines', 'Sudan', 'Suriname', 'Svalbard and Jan Mayen', 'Swaziland', 'Sweden', 'Switzerland', 'Syrian Arab Republic', 'Taiwan', 'Tajikistan', 'Tanzania, United Republic of', 'Thailand', 'Togo', 'Tonga', 'Trinidad and Tobago', 'Tunisia', 'Turkey', 'Turkmenistan', 'Uganda', 'Ukraine', 'United Arab Emirates', 'United Kingdom', 'United States', 'Uruguay', 'Uzbekistan', 'Vanuatu', 'Venezuela', 'Venezuela, Bolivarian Republic of', 'Viet Nam', 'Vietnam', 'Virgin Islands, U.S.', 'Western Sahara', 'Yemen', 'Zambia', 'Zimbabwe']
In [ ]:
from skbio.diversity import alpha_diversity
adiv_sobs = alpha_diversity('sobs', data, ids)# Cuenta el número de especies diferentes observadas en una muestra sin tener en cuenta sus abundancias relativas
adiv_sobs
Out[ ]:
Afghanistan 20
Albania 27
Algeria 43
Andorra 2
Angola 58
..
Virgin Islands, U.S. 4
Western Sahara 3
Yemen 7
Zambia 36
Zimbabwe 43
Length: 216, dtype: int64
In [ ]:
adiv_sobs_sorted = adiv_sobs.sort_values() #organizar por nivel de indice
# Muestra las ultimas 40 filas de la Serie ordenada
print(adiv_sobs_sorted.tail(40))
Honduras 98 Canada 98 Nicaragua 99 Russia 103 Kenya 103 Russian Federation 103 Papua New Guinea 108 Taiwan 110 Cameroon 111 Venezuela 113 Venezuela, Bolivarian Republic of 113 Paraguay 119 Guatemala 121 Madagascar 122 Tanzania, United Republic of 132 Chile 134 Philippines 136 Panama 142 French Guiana 143 Japan 144 Lao People's Democratic Republic 146 Ecuador 151 Costa Rica 151 Colombia 160 Vietnam 160 Viet Nam 160 Australia 162 Peru 169 Congo, the Democratic Republic of the 169 United States 205 India 214 Malaysia 216 Bolivia 218 Bolivia, Plurinational State of 218 Mexico 236 Argentina 281 South Africa 285 Indonesia 296 China 420 Brazil 426 dtype: int64
In [ ]:
plt.figure(figsize=(10, 6))
adiv_sobs_sorted.tail(60).plot(kind='bar', color='skyblue')
plt.xlabel('Países')
plt.ylabel('Índice de diversidad Sobs')
plt.title('alpha diversity metric- Número de especies observadas(Top 60)')
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()
beta diversity metric¶
In [ ]:
topSob = adiv_sobs_sorted.tail(20)#Filtrar solo los primeros 20 segun su indice de diversidad alpha
indices = topSob.index#Guardar los paises que es el indice
In [ ]:
resultado = pivoted_df[pivoted_df.index.isin(indices)]
resultado
Out[ ]:
| genericName | Abana | Abanycha | Abaraeus | Abauba | Abichites | Abimwa | Abroma | Abryna | Acabanga | Acacimenus | ... | Zoodes | Zorilispe | Zorilispiella | Zorion | Zotalemimon | Zyginidia | Zygocera | Zyras | Zyrcosa | Zyrcosoides |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Country | |||||||||||||||||||||
| Argentina | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 |
| Australia | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| Bolivia | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Bolivia, Plurinational State of | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Brazil | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| China | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Colombia | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Congo, the Democratic Republic of the | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Costa Rica | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Ecuador | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| India | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| Indonesia | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | ... | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Lao People's Democratic Republic | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 |
| Malaysia | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Mexico | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Peru | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| South Africa | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 0 | 0 |
| United States | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Viet Nam | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Vietnam | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
20 rows × 3488 columns
In [ ]:
data = resultado.values.tolist()# Convertir pivoted_df a una lista de listas
ids = list(resultado.index)#Guardar paises en lista
In [ ]:
from skbio.diversity import beta_diversity
bc_dm = beta_diversity("braycurtis", data, ids)#Calcular Disimilitud de Bray-Curtis
print(bc_dm)
20x20 distance matrix IDs: 'Argentina', 'Australia', 'Bolivia', 'Bolivia, Plurinational State of', ... Data: [[0. 0.99074074 0.73692078 0.73692078 0.78422484 0.99644444 0.87062937 0.99698341 0.90328152 0.89417989 0.98290598 0.99000999 0.99347471 0.97755961 0.91020408 0.83445946 0.90189521 0.96142433 1. 1. ] [0.99074074 0. 0.99648506 0.99648506 0.99401795 0.95902439 0.99152542 0.97513321 0.99164927 0.99571734 0.93355482 0.93118757 0.95711501 0.94127243 0.98110236 0.99593496 0.97992472 0.97560976 0.95530726 0.95530726] [0.73692078 0.99648506 0. 0. 0.54882812 0.99617591 0.663286 0.99657534 0.728 0.65983607 0.98715891 0.9978308 1. 1. 0.82012195 0.56335283 1. 0.9394958 1. 1. ] [0.73692078 0.99648506 0. 0. 0.54882812 0.99617591 0.663286 0.99657534 0.728 0.65983607 0.98715891 0.9978308 1. 1. 0.82012195 0.56335283 1. 0.9394958 1. 1. ] [0.78422484 0.99401795 0.54882812 0.54882812 0. 0.99324324 0.76699029 0.99803536 0.8137045 0.75704989 0.99053926 0.99852507 1. 1. 0.86788991 0.67898627 0.99680511 0.9552964 1. 1. ] [0.99644444 0.95902439 0.99617591 0.99617591 0.99324324 0. 0.99578504 0.92115385 0.9958159 1. 0.67933272 0.73730044 0.63838384 0.75045872 0.98021583 0.99380805 0.97645212 0.92388202 0.61932939 0.61932939] [0.87062937 0.99152542 0.663286 0.663286 0.76699029 0.99578504 0. 0.99589322 0.69727047 0.62148338 0.97718631 0.99757576 1. 1. 0.82826476 0.65384615 0.99167822 0.92771084 1. 1. ] [0.99698341 0.97513321 0.99657534 0.99657534 0.99803536 0.92115385 0.99589322 0. 0.99595142 0.99585062 0.87358185 0.930131 0.90151515 0.91401274 0.99692308 0.99605523 0.8546798 0.97962649 0.89855072 0.89855072] [0.90328152 0.99164927 0.728 0.728 0.8137045 0.9958159 0.69727047 0.99595142 0. 0.74874372 0.98499062 1. 0.9954955 0.99632353 0.63250883 0.78250591 0.99725275 0.84158416 1. 1. ] [0.89417989 0.99571734 0.65983607 0.65983607 0.75704989 1. 0.62148338 0.99585062 0.74874372 0. 0.99232246 1. 1. 1. 0.85198556 0.55717762 1. 0.95537525 1. 1. ] [0.98290598 0.93355482 0.98715891 0.98715891 0.99053926 0.67933272 0.97718631 0.87358185 0.98499062 0.99232246 0. 0.72356021 0.63315697 0.69115442 0.96806967 0.98901099 0.95769683 0.97452229 0.59052453 0.59052453] [0.99000999 0.93118757 0.9978308 0.9978308 0.99852507 0.73730044 0.99757576 0.930131 1. 1. 0.72356021 0. 0.72286374 0.48033126 0.99797571 1. 0.97391304 0.98921251 0.7258427 0.7258427 ] [0.99347471 0.95711501 1. 1. 1. 0.63838384 1. 0.90151515 0.9954955 1. 0.63315697 0.72286374 0. 0.67820069 0.99333333 1. 0.97375328 0.94434137 0.45816733 0.45816733] [0.97755961 0.94127243 1. 1. 1. 0.75045872 1. 0.91401274 0.99632353 1. 0.69115442 0.48033126 0.67820069 0. 0.99428571 0.99640934 0.95591647 0.97809077 0.6744186 0.6744186 ] [0.91020408 0.98110236 0.82012195 0.82012195 0.86788991 0.98021583 0.82826476 0.99692308 0.63250883 0.85198556 0.96806967 0.99797571 0.99333333 0.99428571 0. 0.85492228 0.98642534 0.75794251 1. 1. ] [0.83445946 0.99593496 0.56335283 0.56335283 0.67898627 0.99380805 0.65384615 0.99605523 0.78250591 0.55717762 0.98901099 1. 1. 0.99640934 0.85492228 0. 0.99730094 0.95366795 1. 1. ] [0.90189521 0.97992472 1. 1. 0.99680511 0.97645212 0.99167822 0.8546798 0.99725275 1. 0.95769683 0.97391304 0.97375328 0.95591647 0.98642534 0.99730094 0. 0.9854192 0.97964377 0.97964377] [0.96142433 0.97560976 0.9394958 0.9394958 0.9552964 0.92388202 0.92771084 0.97962649 0.84158416 0.95537525 0.97452229 0.98921251 0.94434137 0.97809077 0.75794251 0.95366795 0.9854192 0. 0.96092362 0.96092362] [1. 0.95530726 1. 1. 1. 0.61932939 1. 0.89855072 1. 1. 0.59052453 0.7258427 0.45816733 0.6744186 1. 1. 0.97964377 0.96092362 0. 0. ] [1. 0.95530726 1. 1. 1. 0.61932939 1. 0.89855072 1. 1. 0.59052453 0.7258427 0.45816733 0.6744186 1. 1. 0.97964377 0.96092362 0. 0. ]]
In [ ]:
bc_dm
Out[ ]:
Análisis de correlaciones¶
In [ ]:
diversity = adiv_sobs.reset_index()
# Renombrando las columnas
diversity.columns = ['Country', 'Alpha_diversity']
diversity
Out[ ]:
| Country | Alpha_diversity | |
|---|---|---|
| 0 | Afghanistan | 20 |
| 1 | Albania | 27 |
| 2 | Algeria | 43 |
| 3 | Andorra | 2 |
| 4 | Angola | 58 |
| ... | ... | ... |
| 211 | Virgin Islands, U.S. | 4 |
| 212 | Western Sahara | 3 |
| 213 | Yemen | 7 |
| 214 | Zambia | 36 |
| 215 | Zimbabwe | 43 |
216 rows × 2 columns
In [ ]:
div = pd.merge(diversity, countries_code, on='Country', how='left')
div
Out[ ]:
| Country | Alpha_diversity | countryCode | Alpha-3 | Numeric code | Latitude | Longitude | |
|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | 20 | AF | AFG | 4 | 33.0000 | 65.0000 |
| 1 | Albania | 27 | AL | ALB | 8 | 41.0000 | 20.0000 |
| 2 | Algeria | 43 | DZ | DZA | 12 | 28.0000 | 3.0000 |
| 3 | Andorra | 2 | AD | AND | 20 | 42.5000 | 1.6000 |
| 4 | Angola | 58 | AO | AGO | 24 | -12.5000 | 18.5000 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 211 | Virgin Islands, U.S. | 4 | VI | VIR | 850 | 18.3333 | -64.8333 |
| 212 | Western Sahara | 3 | EH | ESH | 732 | 24.5000 | -13.0000 |
| 213 | Yemen | 7 | YE | YEM | 887 | 15.0000 | 48.0000 |
| 214 | Zambia | 36 | ZM | ZMB | 894 | -15.0000 | 30.0000 |
| 215 | Zimbabwe | 43 | ZW | ZWE | 716 | -20.0000 | 30.0000 |
216 rows × 7 columns
In [ ]:
PIB = pd.read_csv("PIB.csv")#Agregar dataframe con datos del PIB 2021 de paises
PIB
Out[ ]:
| Countryname | Alpha-3 | 2021 | |
|---|---|---|---|
| 0 | Aruba | ABW | 29127.759384 |
| 1 | Africa Eastern and Southern | AFE | 1545.613215 |
| 2 | Afghanistan | AFG | 355.777826 |
| 3 | Africa Western and Central | AFW | 1766.943618 |
| 4 | Angola | AGO | 1927.474078 |
| ... | ... | ... | ... |
| 251 | Kosovo | XKX | 5269.783901 |
| 252 | Yemen, Rep. | YEM | 543.637538 |
| 253 | South Africa | ZAF | 7073.612754 |
| 254 | Zambia | ZMB | 1134.713454 |
| 255 | Zimbabwe | ZWE | 1773.920411 |
256 rows × 3 columns
In [ ]:
div = pd.merge(div, PIB, on='Alpha-3', how='left')#Agregar columna del PIB al dataframe div
div
Out[ ]:
| Country | Alpha_diversity | countryCode | Alpha-3 | Numeric code | Latitude | Longitude | Countryname | 2021 | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | 20 | AF | AFG | 4 | 33.0000 | 65.0000 | Afghanistan | 355.777826 |
| 1 | Albania | 27 | AL | ALB | 8 | 41.0000 | 20.0000 | Albania | 6377.203096 |
| 2 | Algeria | 43 | DZ | DZA | 12 | 28.0000 | 3.0000 | Algeria | 3700.314697 |
| 3 | Andorra | 2 | AD | AND | 20 | 42.5000 | 1.6000 | Andorra | 42072.319423 |
| 4 | Angola | 58 | AO | AGO | 24 | -12.5000 | 18.5000 | Angola | 1927.474078 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 211 | Virgin Islands, U.S. | 4 | VI | VIR | 850 | 18.3333 | -64.8333 | Virgin Islands (U.S.) | 41976.008312 |
| 212 | Western Sahara | 3 | EH | ESH | 732 | 24.5000 | -13.0000 | NaN | NaN |
| 213 | Yemen | 7 | YE | YEM | 887 | 15.0000 | 48.0000 | Yemen, Rep. | 543.637538 |
| 214 | Zambia | 36 | ZM | ZMB | 894 | -15.0000 | 30.0000 | Zambia | 1134.713454 |
| 215 | Zimbabwe | 43 | ZW | ZWE | 716 | -20.0000 | 30.0000 | Zimbabwe | 1773.920411 |
216 rows × 9 columns
In [ ]:
div = div.set_index('Country')# Convertir Country en indice
In [ ]:
columnas_a_eliminar = ['countryCode', 'Alpha-3', 'Numeric code', 'Countryname']
# Eliminar las columnas
div = div.drop(columnas_a_eliminar, axis=1)
div = div.dropna()
div
Out[ ]:
| Alpha_diversity | Latitude | Longitude | 2021 | count | |
|---|---|---|---|---|---|
| Country | |||||
| Afghanistan | 20 | 33.0000 | 65.0000 | 355.777826 | 191 |
| Albania | 27 | 41.0000 | 20.0000 | 6377.203096 | 347 |
| Algeria | 43 | 28.0000 | 3.0000 | 3700.314697 | 439 |
| Andorra | 2 | 42.5000 | 1.6000 | 42072.319423 | 20 |
| Angola | 58 | -12.5000 | 18.5000 | 1927.474078 | 460 |
| ... | ... | ... | ... | ... | ... |
| Vietnam | 160 | 16.0000 | 106.0000 | 3756.488901 | 1422 |
| Virgin Islands, U.S. | 4 | 18.3333 | -64.8333 | 41976.008312 | 33 |
| Yemen | 7 | 15.0000 | 48.0000 | 543.637538 | 75 |
| Zambia | 36 | -15.0000 | 30.0000 | 1134.713454 | 287 |
| Zimbabwe | 43 | -20.0000 | 30.0000 | 1773.920411 | 376 |
200 rows × 5 columns
In [ ]:
div = div.dropna()#Quitar NAs
div
Out[ ]:
| Alpha_diversity | Latitude | Longitude | 2021 | |
|---|---|---|---|---|
| Country | ||||
| Afghanistan | 20 | 33.0000 | 65.0000 | 355.777826 |
| Albania | 27 | 41.0000 | 20.0000 | 6377.203096 |
| Algeria | 43 | 28.0000 | 3.0000 | 3700.314697 |
| Andorra | 2 | 42.5000 | 1.6000 | 42072.319423 |
| Angola | 58 | -12.5000 | 18.5000 | 1927.474078 |
| ... | ... | ... | ... | ... |
| Vietnam | 160 | 16.0000 | 106.0000 | 3756.488901 |
| Virgin Islands, U.S. | 4 | 18.3333 | -64.8333 | 41976.008312 |
| Yemen | 7 | 15.0000 | 48.0000 | 543.637538 |
| Zambia | 36 | -15.0000 | 30.0000 | 1134.713454 |
| Zimbabwe | 43 | -20.0000 | 30.0000 | 1773.920411 |
200 rows × 4 columns
In [ ]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
In [ ]:
matrix=div.corr()# Correlación de pearson entre variables de div
plt.figure(figsize=(14,9))
sns.heatmap(matrix,cmap='Reds',annot=True)
plt.show()
In [ ]:
# Grafico comparando Latitud vs Diversidad Alpha
plt.figure(figsize=(10, 6))
plt.scatter(div['Latitude'], div['Alpha_diversity'], color='blue', alpha=0.5)
plt.title('Alpha Diversity vs Latitude')
plt.xlabel('Latitude')
plt.ylabel('Alpha Diversity')
plt.grid(True)
plt.show()
In [ ]:
div = pd.merge(div, locality_counts, on='Country', how='left')# Agregar columna counts a div
div
Out[ ]:
| Country | Alpha_diversity | countryCode | Alpha-3 | Numeric code | Latitude | Longitude | Countryname | 2021 | count | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | 20 | AF | AFG | 4 | 33.0000 | 65.0000 | Afghanistan | 355.777826 | 191 |
| 1 | Albania | 27 | AL | ALB | 8 | 41.0000 | 20.0000 | Albania | 6377.203096 | 347 |
| 2 | Algeria | 43 | DZ | DZA | 12 | 28.0000 | 3.0000 | Algeria | 3700.314697 | 439 |
| 3 | Andorra | 2 | AD | AND | 20 | 42.5000 | 1.6000 | Andorra | 42072.319423 | 20 |
| 4 | Angola | 58 | AO | AGO | 24 | -12.5000 | 18.5000 | Angola | 1927.474078 | 460 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 211 | Virgin Islands, U.S. | 4 | VI | VIR | 850 | 18.3333 | -64.8333 | Virgin Islands (U.S.) | 41976.008312 | 33 |
| 212 | Western Sahara | 3 | EH | ESH | 732 | 24.5000 | -13.0000 | NaN | NaN | 9 |
| 213 | Yemen | 7 | YE | YEM | 887 | 15.0000 | 48.0000 | Yemen, Rep. | 543.637538 | 75 |
| 214 | Zambia | 36 | ZM | ZMB | 894 | -15.0000 | 30.0000 | Zambia | 1134.713454 | 287 |
| 215 | Zimbabwe | 43 | ZW | ZWE | 716 | -20.0000 | 30.0000 | Zimbabwe | 1773.920411 | 376 |
216 rows × 10 columns
Descripción de resultados¶
Se generaron análisis exploratorios para los dataframe importados, correspondientes a Taxon, Distribución y Especies. Esto permitió indentificar el set de datos en cada DF (eg. cantidad de datos nulos, faltantes, desbalances, repetidos, tendencias, máximos, mínimos).
Se obtuvieron métricas generales de los dataframe empleando comandos vistos en clase como shape, unique, etc
Se unificaron estos tres DF para trabajar con un archivo consolidado y se graficaron el top 40 y tail 40 de los países con más y menos especies reportadas.
Se generaron mapas de distribución geográfica para las especies reportadas, según categoría (marina, terrestre, desconocida, etc) y para las especies más representativas.
Conclusiones generales¶
- El catálogo de la vida tiene un alto componente de datos faltantes o desconocidos; lo que evidencia el vacío de información existente en varios grupos taxonómicos.
- Los países con mayor concentración de reportes el el catálogo de la vida, no necesariamente alinean con los de mayor diversidad biológica y Costa Rica si figura entre el top 40.
- La mayor representación de especies se concentra en las plantas, lo que evidencia de forma urgente la necesidad de cubrir otros grupos taxonómicos con el fúngico.
- La categoría de agual dulce posee pocos registros a nivel global y de forma interesante Cuba, Haití, Puerto Rico, Jamaica e islas aledañas reportan la mayor cantidad de especies bajo la categoría "desconocida".
Referencias¶
Pacyga, K., Pacyga, P., Topola, E., Viscardi, S., & Duda-Madej, A. (2024). Bioactive Compounds from Plant Origin as Natural Antimicrobial Agents for the Treatment of Wound Infections. International journal of molecular sciences, 25(4), 2100. https://doi.org/10.3390/ijms25042100
Rogers AD, Appeltans W, Assis J, Ballance LT, Cury P, Duarte C, Favoretto F, Hynes LA, Kumagai JA, Lovelock CE, Miloslavich P, Niamir A, Obura D, O'Leary BC, Ramirez-Llodra E, Reygondeau G, Roberts C, Sadovy Y, Steeds O, Sutton T, Tittensor DP, Velarde E, Woodall L, Aburto-Oropeza O. Discovering marine biodiversity in the 21st century. Adv Mar Biol. 2022;93:23-115. doi: 10.1016/bs.amb.2022.09.002. Epub 2022 Nov 7. PMID: 36435592.
Folk, R. A., & Siniscalchi, C. M. (2021). Biodiversity at the global scale: the synthesis continues. American journal of botany, 108(6), 912–924. https://doi.org/10.1002/ajb2.1694
Bills, G. F., & Gloer, J. B. (2016). Biologically Active Secondary Metabolites from the Fungi. Microbiology spectrum, 4(6), 10.1128/microbiolspec.FUNK-0009-2016. https://doi.org/10.1128/microbiolspec.FUNK-0009-2016
Kirk, P. (2023). Species Fungorum Plus. In O. Bánki, Y. Roskov, M. Döring, G. Ower, D. R. Hernández Robles, C. A. Plata Corredor, T. Stjernegaard Jeppesen, A. Örn, L. Vandepitte, D. Hobern, P. Schalk, R. E. DeWalt, K. Ma, J. Miller, T. Orrell, R. Aalbu, J. Abbott, R. Adlard, C. Aedo, et al., Catalogue of Life Checklist (Jan 2023). Royal Botanic Gardens, Kew. https://doi.org/10.48580/dfrdl-4hj
Alexander, S., Hodson, A., Mitchell, D., Nicolson, D., Orrell, T., & Perez-Gelabert, D. (2024). The Integrated Taxonomic Information System. In O. Bánki, Y. Roskov, M. Döring, G. Ower, D. R. Hernández Robles, C. A. Plata Corredor, T. Stjernegaard Jeppesen, A. Örn, L. Vandepitte, D. Hobern, P. Schalk, R. E. DeWalt, K. Ma, J. Miller, T. Orrell, R. Aalbu, J. Abbott, R. Adlard, C. Aedo, et al., Catalogue of Life Checklist (Version 2024-02-28). ITIS. https://doi.org/10.48580/dfz6w-4ky