Range movement

In [1]:

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

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

Classifying the range movement patterns¶

In [2]:

import ecospat.ecospat as ecospat_full
from ecospat.stand_alone_functions import (
    process_species_historical_range,
    analyze_species_distribution,
    analyze_northward_shift,
    categorize_species,
    analyze_species_distribution_south,
    categorize_species_south,
)

import ecospat.ecospat as ecospat_full from ecospat.stand_alone_functions import ( process_species_historical_range, analyze_species_distribution, analyze_northward_shift, categorize_species, analyze_species_distribution_south, categorize_species_south, )

Classify historical range edges.¶

In [3]:

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

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

No overlapping polygons found — returning original classifications.

Out[3]:

	AREA	PERIMETER	POPUANGU_	POPUANGU_I	CODE	geometry	cluster	category
0	13.195793	57.362450	225.0	244.0	2	POLYGON ((-105.01138 39.68628, -104.99014 39.6...	0	core
1	8.995920	25.194981	52.0	49.0	2	MULTIPOLYGON (((-111.83473 43.34059, -111.8263...	1	core
2	3.241327	15.342440	79.0	78.0	1	POLYGON ((-112.46986 35.16809, -112.45182 35.1...	2	core
3	3.137328	19.063100	41.0	38.0	1	POLYGON ((-111.4987 42.28642, -111.48959 42.35...	3	core
4	0.851035	6.108692	60.0	57.0	1	POLYGON ((-111.88084 39.45175, -111.83646 39.4...	3	trailing (0.05)

Classify modern range edges.¶

In [4]:

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

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

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

Out[4]:

	point_geometry	year	eventDate	geometry	geometry_id	cluster	AREA	category	density
0	POINT (-111.840163 40.880712)	2025	2025-01-05	POLYGON ((-112.16175 40.79402, -112.1358 40.81...	cb869cb2640256dc655c5ffd650a009f	1	52264.281507	core	0.003597
1	POINT (-111.467794 40.775008)	2025	2025-02-07	POLYGON ((-112.16175 40.79402, -112.1358 40.81...	cb869cb2640256dc655c5ffd650a009f	1	52264.281507	core	0.003597
2	POINT (-110.869423 39.731437)	2025	2025-03-29	POLYGON ((-112.16175 40.79402, -112.1358 40.81...	cb869cb2640256dc655c5ffd650a009f	1	52264.281507	core	0.003597
3	POINT (-111.826781 40.765911)	2025	2025-04-27	POLYGON ((-112.16175 40.79402, -112.1358 40.81...	cb869cb2640256dc655c5ffd650a009f	1	52264.281507	core	0.003597
4	POINT (-111.830586 40.497927)	2025	2025-05-25	POLYGON ((-112.16175 40.79402, -112.1358 40.81...	cb869cb2640256dc655c5ffd650a009f	1	52264.281507	core	0.003597

Calculate northward rate of movement.¶

In [5]:

# It's important to note that although relict populations are given a northward movement rate - this rate is only biologically relevant for leading, core, and trailing populations. Relict populations are not considered part of the noncontiguous, moving range.
northward_rate_df = analyze_northward_shift(
    gdf_hist=hist_range,
    gdf_new=classified_modern,
    species_name="Populus angustifolia",
)

northward_rate_df

# It's important to note that although relict populations are given a northward movement rate - this rate is only biologically relevant for leading, core, and trailing populations. Relict populations are not considered part of the noncontiguous, moving range. northward_rate_df = analyze_northward_shift( gdf_hist=hist_range, gdf_new=classified_modern, species_name="Populus angustifolia", ) northward_rate_df

Out[5]:

	species	category	northward_change_km	northward_rate_km_per_year
0	Populus angustifolia	core	-85.070546	-1.736134
1	Populus angustifolia	leading	-20.567392	-0.419743
2	Populus angustifolia	relict	-723.304175	-14.761310
3	Populus angustifolia	trailing	-84.163976	-1.717632

Classify range movement pattern¶

In [6]:

# If the northward rate of movement is categorized for leading, core, and trailing edges then it will be classified as one of the following: Moving together (positive or negative), Stability, Pull Apart, Reabsorption. If the northward rate of movement is only categorized for 2 of the 3 range edges then all patterns are "likely".

# If the northward rate of movement is categorized for leading, core, and trailing edges then it will be classified as one of the following: Moving together (positive or negative), Stability, Pull Apart, Reabsorption. If the northward rate of movement is only categorized for 2 of the 3 range edges then all patterns are "likely".

In [7]:

# Populus angustifolia's range is stable
range_pattern = categorize_species(northward_rate_df)
print(range_pattern)

# Populus angustifolia's range is stable range_pattern = categorize_species(northward_rate_df) print(range_pattern)

                species   leading      core  trailing   category
0  Populus angustifolia -0.419743 -1.736134 -1.717632  stability

In [8]:

# Acer rubrum's range is reabsorbing (core moving into edges or vice-versa)
acer_map = ecospat_full.Map()
acer_range = process_species_historical_range(
    new_map=hist_pipeline, species_name="Acer saccharum"
)
modern_acer, historic_acer = analyze_species_distribution(
    "Acer saccharum", record_limit=1000, continent="north_america"
)
northward_rate_acer = analyze_northward_shift(
    gdf_hist=acer_range,
    gdf_new=modern_acer,
    species_name="Acer saccharum",
)
range_pattern_acer = categorize_species(northward_rate_acer)
print(range_pattern_acer)

# Acer rubrum's range is reabsorbing (core moving into edges or vice-versa) acer_map = ecospat_full.Map() acer_range = process_species_historical_range( new_map=hist_pipeline, species_name="Acer saccharum" ) modern_acer, historic_acer = analyze_species_distribution( "Acer saccharum", record_limit=1000, continent="north_america" ) northward_rate_acer = analyze_northward_shift( gdf_hist=acer_range, gdf_new=modern_acer, species_name="Acer saccharum", ) range_pattern_acer = categorize_species(northward_rate_acer) print(range_pattern_acer)

Modern records (>= 1971): 1000
Historic records (< 1971): 548

          species   leading      core  trailing      category
0  Acer saccharum -1.297308  7.282416  9.815578  reabsorption

Species in the Global South¶

In [9]:

# Eucalyptus globulus's range is likely reabsorption

euc_modern, euc_historic = analyze_species_distribution_south(
    "Eucalyptus globulus", record_limit=1000, continent="oceania", user_start_year=1980
)

northward_rate_euc = analyze_northward_shift(
    gdf_hist=euc_historic,
    gdf_new=euc_modern,
    species_name="Eucalyptus globulus",
    user_start_year=1980,
)

euc_range_pattern = categorize_species_south(northward_rate_euc)
print(euc_range_pattern)

# Eucalyptus globulus's range is likely reabsorption euc_modern, euc_historic = analyze_species_distribution_south( "Eucalyptus globulus", record_limit=1000, continent="oceania", user_start_year=1980 ) northward_rate_euc = analyze_northward_shift( gdf_hist=euc_historic, gdf_new=euc_modern, species_name="Eucalyptus globulus", user_start_year=1980, ) euc_range_pattern = categorize_species_south(northward_rate_euc) print(euc_range_pattern)

Modern records (>= 1980): 1000
Historic records (< 1980): 1000

               species leading      core  trailing             category
0  Eucalyptus globulus    None  0.385176 -3.823545  likely reabsorption