sproc is a Python package with the objective of visualizing and quantifying the overlap between the ranges of two taxa as determined by latitude/longitude occurrence data.

The list of dependencies required by sproc are:

• pygbif
• numpy
• pandas
• scipy
• geopandas
• matplotlib
• contextily
• seaborn
• shapely
• libpysal

The following command in conda installs these packages if not already installed:

conda install pygbif numpy pandas scipy geopandas matplotlib contextily seaborn shapely libpysal -c conda-forge

Currently, sproc can only be installed locally. Use the following commands to clone this repository to your local machine:

git clone https://github.com/HenryLandis/sproc.git
cd ./sproc
pip install -e .

The class object Sproc handles the formatting of queries to the Global Biodiversity Information Facility (GBIF) database. At minumum, the user must supply a scientific species name. By default, the parameter basis = True constrains the query to only retrieve live observations of the species.

The function Sproc.run() submits the query to the GBIF REST API and returns a CSV file with three columns: species name, latitude and longitude. This file can be used as input for downstream utility functions (or alternatively, the user may supply pre-formatted latitude and longitude data from a different preferred source).

Optional parameters for the class object include:

• continent: constrain the query to observations on a continent or list of continents. This can help to filter observations of species that have been introduced to areas outside their native range.
• lat_range and lon_range: define latitude and longitude bounds for the query.
• outdir: specify a directory for the CSV file formatted from the query output. If left blank, defaults to the user's current directory.

The following example demonstrates retreving live occurrence data from GBIF for Quercus rubra, the red oak, within a subset of its native range in eastern North America.

from sproc import Sproc
QR = Sproc("Quercus rubra", basis = True, lat_range = [40, 45], lon_range = [-100, -80],
           outdir = "/home/henrylandis/sproc")
QR.run()

....... Found 1783 records.

Inspecting the output:

import pandas as pd
df = pd.read_csv("/home/henrylandis/sproc/Quercus_rubra.csv")
df.head()

The utility functions provide a variety of options for visualizing occurrence data and geometric ranges. These include:

• hexmap: a hex-based grid map with adjustable hex size.
• recmap: a rectangle (or square) grid map with adjustable box size.
• kdemap: a Kernel Density Estimation (KDE) of occurrence data, showing occurrence data as a probability density function.
• plot_polygons_intersection: determine and plot the geometric polygon representing the intersection of two species ranges.
• plot_polygons_separate: plot two species ranges either side-by-side or overlaid on one figure.
• world_plot: plot occurrence points for one or two taxa on a world map.

In the following example, we perform a second query on Quercus palustris, the pin oak, also native to eastern North America; we then use plot_polygons_separate to visualize the ranges of Q. rubra and Q. palustris separately, as well as overlaid on one figure.

QP = Sproc("Quercus palustris", basis = True, lat_range = [40, 45], lon_range = [-100, -80],
           outdir = "/home/henrylandis/sproc")
QP.run()

.. Found 467 records.

from sproc import utils
utils.plot_polygons_separate("/home/henrylandis/sproc/Quercus_rubra.csv", 
                             "/home/henrylandis/sproc/Quercus_palustris.csv",
                            label1 = "Quercus rubra", label2 = "Quercus palustris", sep = True, legend = True, 
                            figsize = (50, 50), fontsize = 40, markerscale = 25)

utils.plot_polygons_separate("/home/henrylandis/sproc/Quercus_rubra.csv", 
                             "/home/henrylandis/sproc/Quercus_palustris.csv",
                            label1 = "Quercus rubra", label2 = "Quercus palustris", sep = False, legend = True, 
                            figsize = (50, 50), fontsize = 40, markerscale = 25)