Resumen¶
El presente estudio consiste en una aproximación biogeográfica de la especie Brosimun alicastrum SW, retomando su respectiva distribución en países de América Latina. Es considerada como una especie multipropósito, adquiere su funcionalidad a partir del uso y técnicas de aprovechamiento en distintas comunidades. Se pretende análisar la distribución actual del ojoche y aproximarse al aprovechamiento que genera esta especie a nivel a las comunidades y soberanía alimentaria.¶
(Brosimum alicastrum)
Metodología¶
El estudio se basa en los datos del Sistema Global de Información sobre Biodiversidad GBIF 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.¶
Los datos son parte del trabajo colaborativo con registros de la población de las comunidades vinculada o consciente de integrar la información, para un total de 14220 de especies de ojoche encontradas.¶
Distribución geográfica¶
In [ ]:
pip install tabulate
Requirement already satisfied: tabulate in /usr/local/lib/python3.11/dist-packages (0.9.0)
Aprovechamiento del Ojoche¶
| Aprovechamiento | Estructura |
|---|---|
|
1. Follaje. 2. Medicina. 3. Alimentos. |
Hojas, semillas. Semillas, corteza,latex. Agua. |
Variedades de ojoche¶
In [ ]:
# Import the tabulate module
from tabulate import tabulate
# Sample data: list of lists
data = [
["Reino", "Plantae"],
["Filo", "Tracheophyta"],
["Clase", "Magnoliopsida"]
]
# Creating a table with headers and a grid format
table = tabulate(
data,
headers=["Clasificación", "NOmbre"],
tablefmt="grid"
)
print(table)
+-----------------+---------------+ | Clasificación | NOmbre | +=================+===============+ | Reino | Plantae | +-----------------+---------------+ | Filo | Tracheophyta | +-----------------+---------------+ | Clase | Magnoliopsida | +-----------------+---------------+
In [ ]:
import geopandas as gpd
In [ ]:
# Carga de pandas con el alias pd
import pandas as pd
# Carga de folium
import folium
# Carga de matplotlib
import matplotlib.pyplot as plt
In [ ]:
# Biblioteca requerida para mapas interactivos
!pip install mapclassify --quiet
import mapclassify
In [ ]:
# Montaje de Google Drive en Colab
from google.colab import drive
drive.mount('/content/drive')
Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
In [ ]:
# Carga de un archivo CSV desde Google Drive
ojoche = pd.read_csv(
"/content/drive/MyDrive/Colab Notebooks/ojoche.csv",
sep="\t"
)
# Despliegue de los últimos registros
ojoche.tail(10)
Out[ ]:
| gbifID | datasetKey | occurrenceID | kingdom | phylum | class | order | family | genus | species | ... | identifiedBy | dateIdentified | license | rightsHolder | recordedBy | typeStatus | establishmentMeans | lastInterpreted | mediaType | issue | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 14197 | 1038454392 | 99b1c33f-113b-4477-8075-a9bbe5df0246 | IAvH:BICUB:MAGNOLIOPHYTA:OBSERVACIONHUMANA:I2D... | Plantae | Tracheophyta | Magnoliopsida | Rosales | Moraceae | Brosimum | Brosimum alicastrum | ... | NaN | NaN | CC_BY_NC_4_0 | UTP | Dorian Ruiz Penagos | NaN | NaN | 2025-02-06T18:17:13.282Z | NaN | GEODETIC_DATUM_ASSUMED_WGS84;CONTINENT_DERIVED... |
| 14198 | 1038454387 | 99b1c33f-113b-4477-8075-a9bbe5df0246 | IAvH:BICUB:MAGNOLIOPHYTA:OBSERVACIONHUMANA:I2D... | Plantae | Tracheophyta | Magnoliopsida | Rosales | Moraceae | Brosimum | Brosimum alicastrum | ... | NaN | NaN | CC_BY_NC_4_0 | UTP | Dorian Ruiz Penagos | NaN | NaN | 2025-02-06T18:17:13.309Z | NaN | GEODETIC_DATUM_ASSUMED_WGS84;CONTINENT_DERIVED... |
| 14199 | 1038454380 | 99b1c33f-113b-4477-8075-a9bbe5df0246 | IAvH:BICUB:MAGNOLIOPHYTA:OBSERVACIONHUMANA:I2D... | Plantae | Tracheophyta | Magnoliopsida | Rosales | Moraceae | Brosimum | Brosimum alicastrum | ... | NaN | NaN | CC_BY_NC_4_0 | UTP | Dorian Ruiz Penagos | NaN | NaN | 2025-02-06T18:17:13.280Z | NaN | GEODETIC_DATUM_ASSUMED_WGS84;CONTINENT_DERIVED... |
| 14200 | 1038454309 | 99b1c33f-113b-4477-8075-a9bbe5df0246 | IAvH:BICUB:MAGNOLIOPHYTA:OBSERVACIONHUMANA:I2D... | Plantae | Tracheophyta | Magnoliopsida | Rosales | Moraceae | Brosimum | Brosimum alicastrum | ... | NaN | NaN | CC_BY_NC_4_0 | UTP | Dorian Ruiz Penagos | NaN | NaN | 2025-02-06T18:17:13.161Z | NaN | GEODETIC_DATUM_ASSUMED_WGS84;CONTINENT_DERIVED... |
| 14201 | 1038454280 | 99b1c33f-113b-4477-8075-a9bbe5df0246 | IAvH:BICUB:MAGNOLIOPHYTA:OBSERVACIONHUMANA:I2D... | Plantae | Tracheophyta | Magnoliopsida | Rosales | Moraceae | Brosimum | Brosimum alicastrum | ... | NaN | NaN | CC_BY_NC_4_0 | UTP | Dorian Ruiz Penagos | NaN | NaN | 2025-02-06T18:17:13.147Z | NaN | GEODETIC_DATUM_ASSUMED_WGS84;CONTINENT_DERIVED... |
| 14202 | 1038454244 | 99b1c33f-113b-4477-8075-a9bbe5df0246 | IAvH:BICUB:MAGNOLIOPHYTA:OBSERVACIONHUMANA:I2D... | Plantae | Tracheophyta | Magnoliopsida | Rosales | Moraceae | Brosimum | Brosimum alicastrum | ... | NaN | NaN | CC_BY_NC_4_0 | UTP | Dorian Ruiz Penagos | NaN | NaN | 2025-02-06T18:17:13.214Z | NaN | GEODETIC_DATUM_ASSUMED_WGS84;CONTINENT_DERIVED... |
| 14203 | 1038454193 | 99b1c33f-113b-4477-8075-a9bbe5df0246 | IAvH:BICUB:MAGNOLIOPHYTA:OBSERVACIONHUMANA:I2D... | Plantae | Tracheophyta | Magnoliopsida | Rosales | Moraceae | Brosimum | Brosimum alicastrum | ... | NaN | NaN | CC_BY_NC_4_0 | UTP | Dorian Ruiz Penagos | NaN | NaN | 2025-02-06T18:17:13.204Z | NaN | GEODETIC_DATUM_ASSUMED_WGS84;CONTINENT_DERIVED... |
| 14204 | 1038454180 | 99b1c33f-113b-4477-8075-a9bbe5df0246 | IAvH:BICUB:MAGNOLIOPHYTA:OBSERVACIONHUMANA:I2D... | Plantae | Tracheophyta | Magnoliopsida | Rosales | Moraceae | Brosimum | Brosimum alicastrum | ... | NaN | NaN | CC_BY_NC_4_0 | UTP | Dorian Ruiz Penagos | NaN | NaN | 2025-02-06T18:17:13.206Z | NaN | GEODETIC_DATUM_ASSUMED_WGS84;CONTINENT_DERIVED... |
| 14205 | 1038454149 | 99b1c33f-113b-4477-8075-a9bbe5df0246 | IAvH:BICUB:MAGNOLIOPHYTA:ESPECIMENPRESERVADO:I... | Plantae | Tracheophyta | Magnoliopsida | Rosales | Moraceae | Brosimum | Brosimum alicastrum | ... | NaN | NaN | CC_BY_NC_4_0 | UTP | Dorian Ruiz Penagos | NaN | NaN | 2025-02-06T18:17:13.165Z | NaN | GEODETIC_DATUM_ASSUMED_WGS84;CONTINENT_DERIVED... |
| 14206 | 1030510718 | bd910e78-7a69-44ac-9450-65bf41329604 | IAvH:BICUB:TOLIMA:PLANTAE:OBSERVACIONHUMANA:I2... | Plantae | Tracheophyta | Magnoliopsida | Rosales | Moraceae | Brosimum | Brosimum alicastrum | ... | NaN | NaN | CC_BY_NC_4_0 | UD | Roy Gonzalez-M.;Jhon Nieto;Daniel García | NaN | NaN | 2025-02-06T18:26:18.704Z | NaN | COORDINATE_ROUNDED;GEODETIC_DATUM_ASSUMED_WGS8... |
10 rows × 50 columns
In [ ]:
# Crear un GeoDataFrame a partir del DataFrame
ojoche= gpd.GeoDataFrame(
ojoche,
geometry=gpd.points_from_xy(ojoche.decimalLongitude, ojoche.decimalLatitude),
crs='EPSG:4326'
)
In [ ]:
# Información general sobre un dataframe
ojoche.info()
<class 'geopandas.geodataframe.GeoDataFrame'> RangeIndex: 14207 entries, 0 to 14206 Data columns (total 51 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 gbifID 14207 non-null int64 1 datasetKey 14207 non-null object 2 occurrenceID 14079 non-null object 3 kingdom 14207 non-null object 4 phylum 14207 non-null object 5 class 14207 non-null object 6 order 14207 non-null object 7 family 14207 non-null object 8 genus 14207 non-null object 9 species 14207 non-null object 10 infraspecificEpithet 901 non-null object 11 taxonRank 14207 non-null object 12 scientificName 14207 non-null object 13 verbatimScientificName 14207 non-null object 14 verbatimScientificNameAuthorship 13399 non-null object 15 countryCode 14086 non-null object 16 locality 13523 non-null object 17 stateProvince 13646 non-null object 18 occurrenceStatus 14207 non-null object 19 individualCount 8474 non-null float64 20 publishingOrgKey 14207 non-null object 21 decimalLatitude 11271 non-null float64 22 decimalLongitude 11271 non-null float64 23 coordinateUncertaintyInMeters 1398 non-null float64 24 coordinatePrecision 0 non-null float64 25 elevation 8933 non-null float64 26 elevationAccuracy 6251 non-null float64 27 depth 908 non-null float64 28 depthAccuracy 908 non-null float64 29 eventDate 13832 non-null object 30 day 12525 non-null float64 31 month 13175 non-null float64 32 year 13681 non-null float64 33 taxonKey 14207 non-null int64 34 speciesKey 14207 non-null int64 35 basisOfRecord 14207 non-null object 36 institutionCode 14022 non-null object 37 collectionCode 5357 non-null object 38 catalogNumber 4619 non-null object 39 recordNumber 6543 non-null object 40 identifiedBy 4747 non-null object 41 dateIdentified 2360 non-null object 42 license 14207 non-null object 43 rightsHolder 3831 non-null object 44 recordedBy 11048 non-null object 45 typeStatus 1892 non-null object 46 establishmentMeans 94 non-null object 47 lastInterpreted 14207 non-null object 48 mediaType 822 non-null object 49 issue 4963 non-null object 50 geometry 14207 non-null geometry dtypes: float64(12), geometry(1), int64(3), object(35) memory usage: 5.5+ MB
In [ ]:
# Crear un GeoDataFrame a partir del DataFrame
ojoche= gpd.GeoDataFrame(
ojoche,
geometry=gpd.points_from_xy(ojoche.decimalLongitude, ojoche.decimalLatitude),
crs='EPSG:4326'
)
# Mostrar los primeros registros del GeoDataFrame,
# incluyendo la columna de geometría
ojoche[['gbifID', 'species', 'decimalLongitude', 'decimalLatitude', 'countryCode', 'locality']].head()
Out[ ]:
| gbifID | species | decimalLongitude | decimalLatitude | countryCode | locality | |
|---|---|---|---|---|---|---|
| 0 | 931000349 | Brosimum alicastrum | -74.911667 | 5.503333 | CO | Vereda Sasaima. predios de ISAGEN. cerca de la... |
| 1 | 930996512 | Brosimum alicastrum | -72.777000 | 8.283120 | CO | Km 49 |
| 2 | 930994646 | Brosimum alicastrum | -76.115710 | 4.878020 | CO | Corregimiento Morelia |
| 3 | 930994477 | Brosimum alicastrum | -75.578083 | 3.810389 | CO | Vereda Agua Bonita |
| 4 | 930992926 | Brosimum alicastrum | -76.662140 | 3.762820 | CO | Corregimiento Loboguerrero |
Distributions
Categorical distributions
2-d distributions
Values
Faceted distributions
<string>:5: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.
<string>:5: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.
<string>:5: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.
In [ ]:
from matplotlib import pyplot as plt
_df_64['decimalLatitude'].plot(kind='line', figsize=(8, 4), title='decimalLatitude')
plt.gca().spines[['top', 'right']].set_visible(False)
In [ ]:
from matplotlib import pyplot as plt
import seaborn as sns
_df_59.groupby('locality').size().plot(kind='barh', color=sns.palettes.mpl_palette('Dark2'))
plt.gca().spines[['top', 'right',]].set_visible(False)
In [ ]:
from matplotlib import pyplot as plt
_df_26['gbifID'].plot(kind='line', figsize=(8, 4), title='gbifID')
plt.gca().spines[['top', 'right']].set_visible(False)
In [ ]:
# Carga del módulo pyplot de matplotlib con el alias plt
import matplotlib.pyplot as plt
Se crea un mapa estático simple para visualizar la distribución geográfica del árbol de ojoche (Brosimum alicastrum), también llamado "Ramón" o "ramón maya". Utiliza los datos almacenados en una variable llamada ojoche.¶
In [ ]:
# Mapa estático de registros de ojoche
ojoche.plot()
Out[ ]:
<Axes: >
In [ ]:
!pip install cartopy
import cartopy
Requirement already satisfied: cartopy in /usr/local/lib/python3.11/dist-packages (0.24.1) Requirement already satisfied: numpy>=1.23 in /usr/local/lib/python3.11/dist-packages (from cartopy) (1.26.4) Requirement already satisfied: matplotlib>=3.6 in /usr/local/lib/python3.11/dist-packages (from cartopy) (3.10.0) Requirement already satisfied: shapely>=1.8 in /usr/local/lib/python3.11/dist-packages (from cartopy) (2.0.7) Requirement already satisfied: packaging>=21 in /usr/local/lib/python3.11/dist-packages (from cartopy) (24.2) Requirement already satisfied: pyshp>=2.3 in /usr/local/lib/python3.11/dist-packages (from cartopy) (2.3.1) Requirement already satisfied: pyproj>=3.3.1 in /usr/local/lib/python3.11/dist-packages (from cartopy) (3.7.1) Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.6->cartopy) (1.3.1) Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.6->cartopy) (0.12.1) Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.6->cartopy) (4.56.0) Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.6->cartopy) (1.4.8) Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.6->cartopy) (11.1.0) Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.6->cartopy) (3.2.1) Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.6->cartopy) (2.8.2) Requirement already satisfied: certifi in /usr/local/lib/python3.11/dist-packages (from pyproj>=3.3.1->cartopy) (2025.1.31) Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.7->matplotlib>=3.6->cartopy) (1.17.0)
In [ ]:
# Mapa de coropletas
new_var = ojoche.plot(
column='countryCode',
cmap='YlOrRd', # https://matplotlib.org/stable/gallery/color/colormap_reference.html
legend=True,
legend_kwds={
'label': "Población estimada",
'orientation': "horizontal"
},
figsize=(12, 8)
)
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-169-df1eaeaaf000> in <cell line: 0>() 1 # Mapa de coropletas ----> 2 new_var = ojoche.plot( 3 column='countryCode', 4 cmap='YlOrRd', # https://matplotlib.org/stable/gallery/color/colormap_reference.html 5 legend=True, /usr/local/lib/python3.11/dist-packages/geopandas/plotting.py in __call__(self, *args, **kwargs) 966 kind = kwargs.pop("kind", "geo") 967 if kind == "geo": --> 968 return plot_dataframe(data, *args, **kwargs) 969 if kind in self._pandas_kinds: 970 # Access pandas plots /usr/local/lib/python3.11/dist-packages/geopandas/plotting.py in plot_dataframe(df, column, cmap, color, ax, cax, categorical, legend, scheme, k, vmin, vmax, markersize, figsize, legend_kwds, categories, classification_kwds, missing_kwds, aspect, autolim, **style_kwds) 944 legend_kwds.setdefault("handles", patches) 945 legend_kwds.setdefault("labels", categories) --> 946 ax.legend(**legend_kwds) 947 else: 948 if cax is not None: /usr/local/lib/python3.11/dist-packages/matplotlib/axes/_axes.py in legend(self, *args, **kwargs) 335 """ 336 handles, labels, kwargs = mlegend._parse_legend_args([self], *args, **kwargs) --> 337 self.legend_ = mlegend.Legend(self, handles, labels, **kwargs) 338 self.legend_._remove_method = self._remove_legend 339 return self.legend_ TypeError: Legend.__init__() got an unexpected keyword argument 'label'
In [ ]:
# Carga de plotly.express con el alias px
import plotly.express as px
# Carga de pandas
import pandas as pd
In [ ]:
# Carga de pandas
import pandas as pd
In [ ]:
# Borrado de filas que no tengan año
df_anio_no_nulo = ojoche.dropna(subset=['year'])
# Filtrado de los años entre 2005 y 2025
df_filtrado = df_anio_no_nulo[(df_anio_no_nulo['year'] >= 2005) & (df_anio_no_nulo['year'] <= 2015)]
# Conteo de registros por año
registros_por_anio = df_filtrado.groupby('year')['gbifID'].count().reset_index()
# Creación del gráfico de barras
fig = px.bar(
registros_por_anio,
x='year',
y='gbifID',
title='Cantidad de registros (años 2005 - 2015)'
)
# Personalización
fig.update_layout(
xaxis_title='Año',
yaxis_title='Cantidad de registros',
width=1000, # Ancho de la figura
height=600 # Alto de la figura
)
(Brosimum alicastrum)
In [ ]:
# Borrado de filas que no tengan año
df_anio_no_nulo = ojoche.dropna(subset=['year'])
# Filtrado de los años entre 2015 y 2025
df_filtrado = df_anio_no_nulo[(df_anio_no_nulo['year'] >= 2015) & (df_anio_no_nulo['year'] <= 2025)]
# Conteo de registros por año
registros_por_anio = df_filtrado.groupby('year')['locality'].count().reset_index()
# Creación del gráfico de barras
fig = px.bar(
registros_por_anio,
x='year',
y='locality',
title='Cantidad de registros (años 20020 - 2025)'
)
# Personalización
fig.update_layout(
xaxis_title='Año',
yaxis_title='Cantidad de registros',
width=800, # Ancho de la figura
height=600 # Alto de la figura
)
In [ ]:
# Instalación de rasterio
!pip install rasterio --quiet
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 22.2/22.2 MB 71.2 MB/s eta 0:00:00
In [ ]:
# Carga de rasterio
import rasterio
# Carga de rasterio.plot (para graficar datos raster)
import rasterio.plot
# Carga de numpy (para álgebra lineal)
import numpy as np
# Carga de matplotlib.pyplot
import matplotlib.pyplot as plt
In [ ]:
import rasterio
import rasterio.plot
import geopandas as gpd
import numpy as np
import matplotlib.pyplot as plt
El ojoche se extiende en medio del tropico, principalmente por todo centroamérica, en bosques, bajo temperaturas más altas.¶
In [ ]:
# Mapa de temperatura media anual
rasterio.plot.show(temperatura_media_anual)
/usr/local/lib/python3.11/dist-packages/matplotlib/colors.py:777: RuntimeWarning: overflow encountered in multiply
Out[ ]:
<Axes: >