Individual persistence

In [1]:

# Uncomment below to run in Google Collab
# pip install ecospat

# Uncomment below to run in Google Collab # pip install ecospat

Categorizing and visualizing individual persistence¶

In [2]:

import ecospat.ecospat as ecospat_full
from ecospat.stand_alone_functions import (
    process_species_historical_range,
    analyze_species_distribution,
    analyze_northward_shift,
    calculate_rate_of_change_first_last,
    merge_category_dataframes,
    prepare_gdf_for_rasterization,
    cat_int_mapping,
    rasterize_multiband_gdf_match,
    compute_propagule_pressure_range,
    compute_individual_persistence,
)

import ecospat.ecospat as ecospat_full from ecospat.stand_alone_functions import ( process_species_historical_range, analyze_species_distribution, analyze_northward_shift, calculate_rate_of_change_first_last, merge_category_dataframes, prepare_gdf_for_rasterization, cat_int_mapping, rasterize_multiband_gdf_match, compute_propagule_pressure_range, compute_individual_persistence, )

Historic range edges¶

In [3]:

hist_pipeline = ecospat_full.Map()
hist_range = process_species_historical_range(
    new_map=hist_pipeline, species_name="Populus angustifolia"
)

hist_pipeline = ecospat_full.Map() hist_range = process_species_historical_range( new_map=hist_pipeline, species_name="Populus angustifolia" )

No overlapping polygons found — returning original classifications.

Modern range edges and historical population data¶

In [4]:

classified_modern, classified_historic = analyze_species_distribution(
    "Populus angustifolia", record_limit=1000, continent="north_america"
)

classified_modern, classified_historic = analyze_species_distribution( "Populus angustifolia", record_limit=1000, continent="north_america" )

Modern records (>= 1976): 1000
Historic records (< 1976): 254

Northward shift¶

In [5]:

northward_rate_df = analyze_northward_shift(
    gdf_hist=hist_range,
    gdf_new=classified_modern,
    species_name="Populus angustifolia",
)
northward_rate_df = northward_rate_df[
    northward_rate_df["category"].isin(["leading", "core", "trailing"])
]

northward_rate_df["category"] = northward_rate_df["category"].str.title()

northward_rate_df = analyze_northward_shift( gdf_hist=hist_range, gdf_new=classified_modern, species_name="Populus angustifolia", ) northward_rate_df = northward_rate_df[ northward_rate_df["category"].isin(["leading", "core", "trailing"]) ] northward_rate_df["category"] = northward_rate_df["category"].str.title()

Population density change¶

In [6]:

change = calculate_rate_of_change_first_last(
    classified_historic, classified_modern, "Populus angustifolia", custom_end_year=2025
)


change = change[change["collapsed_category"].isin(["leading", "core", "trailing"])]
change = change.rename(
    columns={
        "collapsed_category": "Category",
        "rate_of_change_first_last": "Rate of Change",
        "start_time_period": "Start Years",
        "end_time_period": "End Years",
    }
)


change["Category"] = change["Category"].str.title()

change = calculate_rate_of_change_first_last( classified_historic, classified_modern, "Populus angustifolia", custom_end_year=2025 ) change = change[change["collapsed_category"].isin(["leading", "core", "trailing"])] change = change.rename( columns={ "collapsed_category": "Category", "rate_of_change_first_last": "Rate of Change", "start_time_period": "Start Years", "end_time_period": "End Years", } ) change["Category"] = change["Category"].str.title()

Prepare data for rasterization¶

In [7]:

merged = merge_category_dataframes(northward_rate_df, change)

preped_gdf = prepare_gdf_for_rasterization(classified_modern, merged)

preped_gdf_new = cat_int_mapping(preped_gdf)

preped_gdf_new.head()

merged = merge_category_dataframes(northward_rate_df, change) preped_gdf = prepare_gdf_for_rasterization(classified_modern, merged) preped_gdf_new = cat_int_mapping(preped_gdf) preped_gdf_new.head()

Out[7]:

	geometry	category	density	northward_rate_km_per_year	Rate of Change	category_int
0	POLYGON ((-112.16175 40.79402, -112.1358 40.81...	Core	0.003597	-1.736134	1.115629	1.0
188	POLYGON ((-108.48788 37.47497, -107.24964 39.5...	Core	0.001869	-1.736134	1.115629	1.0
446	POLYGON ((-112.38734 38.88478, -110.89519 38.3...	Trailing (0.05)	0.001117	0.000000	0.000000	NaN
481	POLYGON ((-111.90918 43.82033, -111.86789 43.8...	Leading (0.95)	0.005273	0.000000	0.000000	NaN
515	POLYGON ((-108.24095 32.90245, -108.271 33.225...	Relict (0.01 Latitude)	0.010924	0.000000	0.000000	NaN

In [8]:

value_columns = [
    "density",
    "northward_rate_km_per_year",
    "Rate of Change",
    "category_int",
]
raster_show, transform, show_bounds = rasterize_multiband_gdf_match(
    preped_gdf_new, value_columns
)

value_columns = [ "density", "northward_rate_km_per_year", "Rate of Change", "category_int", ] raster_show, transform, show_bounds = rasterize_multiband_gdf_match( preped_gdf_new, value_columns )

Rasterize data and compute propagule pressure¶

In [9]:

pressure_show = compute_propagule_pressure_range(raster_show)

pressure_show = compute_propagule_pressure_range(raster_show)

Compute individual persistence¶

In [10]:

points = compute_individual_persistence(
    points_gdf=classified_modern,
    raster_stack_arrays=raster_show,
    propagule_array=pressure_show,
    baseline_death=0.10,
    transform=transform,
)
points.head()

points = compute_individual_persistence( points_gdf=classified_modern, raster_stack_arrays=raster_show, propagule_array=pressure_show, baseline_death=0.10, transform=transform, ) points.head()

Out[10]:

	point_id	P_1y	P_5y	density_vals	northward_vals	abundance_change_vals	edge_vals	propagule_vals	risk_decile	baseline_death	P_1y_vs_baseline	P_5y_vs_baseline	north_south_of_category_centroid	point_geometry	geometry	geometry_id
0	0	0.867547	0.491434	0.003597	-1.736134	1.115629	core	0.169574	7	0.1	lower	lower	north	POINT (-111.840163 40.880712)	POLYGON ((-112.16175 40.79402, -112.1358 40.81...	cb869cb2640256dc655c5ffd650a009f
1	1	0.868386	0.493814	0.003597	-1.736134	1.115629	core	0.186114	6	0.1	lower	lower	north	POINT (-111.467794 40.775008)	POLYGON ((-112.16175 40.79402, -112.1358 40.81...	cb869cb2640256dc655c5ffd650a009f
2	2	0.863260	0.479412	0.003597	-1.736134	1.115629	core	0.078682	10	0.1	lower	lower	north	POINT (-110.869423 39.731437)	POLYGON ((-112.16175 40.79402, -112.1358 40.81...	cb869cb2640256dc655c5ffd650a009f
3	3	0.867735	0.491968	0.003597	-1.736134	1.115629	core	0.173321	7	0.1	lower	lower	north	POINT (-111.826781 40.765911)	POLYGON ((-112.16175 40.79402, -112.1358 40.81...	cb869cb2640256dc655c5ffd650a009f
4	4	0.867247	0.490584	0.003597	-1.736134	1.115629	core	0.163558	8	0.1	lower	lower	north	POINT (-111.830586 40.497927)	POLYGON ((-112.16175 40.79402, -112.1358 40.81...	cb869cb2640256dc655c5ffd650a009f

In [11]:

point_map_new = ecospat_full.Map()
point_map_new.add_basemap("GBIF.Classic")
point_map_new.add_point_data(points, use_gradient=True)
point_map_new

point_map_new = ecospat_full.Map() point_map_new.add_basemap("GBIF.Classic") point_map_new.add_point_data(points, use_gradient=True) point_map_new

Out[11]: