load_trawl_data¶

Author: Hayley

Load and vizualize the trawl data.

In [4]:

import sdm
import pandas as pd
import matplotlib.pyplot as plt
import cartopy
import numpy as np

import sdm import pandas as pd import matplotlib.pyplot as plt import cartopy import numpy as np

In [2]:

df = sdm.load_trawl_data()
df

df = sdm.load_trawl_data() df

Out[2]:

	TOWDATETIME_EST	LAT	LON	MEAN_DEPTH	SWEPT_AREA_km	acadian redfish	alewife	alligatorfish	american lobster	american plaice	...	Rrs_707	Rrs_708	Rrs_709	Rrs_711	Rrs_712	Rrs_713	Rrs_714	Rrs_717	Rrs_719	Rrs_brightness
0	2024-03-07 10:58:00	38.659194	-74.828083	24	0.021282	0.0	0.0	0.0	0.0	0.0	...	0.000851	0.000825	0.000797	0.000765	0.000738	0.000717	0.000689	0.000552	0.000540	1.090205
1	2024-03-07 14:32:00	38.498596	-74.477395	40	0.024311	0.0	0.0	0.0	0.0	0.0	...	0.000386	0.000378	0.000364	0.000344	0.000333	0.000325	0.000315	0.000229	0.000261	0.907649
2	2024-03-07 17:44:00	38.411974	-74.502885	39	0.023835	0.0	0.0	0.0	0.0	0.0	...	0.000372	0.000364	0.000350	0.000330	0.000318	0.000310	0.000292	0.000210	0.000254	0.935354
3	2024-03-07 20:29:00	38.505238	-74.832046	25	0.021510	0.0	0.0	0.0	1.0	0.0	...	0.000674	0.000653	0.000632	0.000606	0.000588	0.000574	0.000553	0.000430	0.000434	1.050127
4	2024-03-07 22:45:00	38.556784	-74.897495	21	0.019174	0.0	0.0	0.0	27.0	0.0	...	0.001309	0.001268	0.001221	0.001175	0.001134	0.001093	0.001046	0.000848	0.000791	1.327782
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
363	2024-05-12 12:30:00	41.305711	-70.636614	23	0.018435	0.0	0.0	0.0	0.0	0.0	...	0.000283	0.000275	0.000266	0.000253	0.000246	0.000243	0.000237	0.000181	0.000218	0.529729
364	2024-05-12 18:45:00	40.467061	-71.381195	73	0.022568	0.0	2.0	0.0	2.0	0.0	...	0.000159	0.000155	0.000150	0.000142	0.000140	0.000137	0.000138	0.000097	0.000150	0.446814
365	2024-05-13 01:19:00	39.618178	-72.027289	219	0.024705	0.0	0.0	0.0	0.0	0.0	...	0.000108	0.000104	0.000102	0.000094	0.000093	0.000094	0.000095	0.000057	0.000104	0.450264
366	2024-05-13 03:42:00	39.762143	-72.213598	98	0.020737	0.0	1.0	0.0	0.0	0.0	...	0.000146	0.000143	0.000141	0.000134	0.000134	0.000133	0.000130	0.000068	0.000124	0.446797
367	2024-05-13 07:15:00	40.175585	-71.934953	76	0.023215	0.0	4.0	0.0	1.0	0.0	...	0.000162	0.000159	0.000155	0.000148	0.000147	0.000145	0.000142	0.000082	0.000137	0.421705

368 rows × 372 columns

Most common species¶

Let's get species with abuncance > 50000.

In [6]:

matched=[]
for item in df.columns:
    if 'Rrs' in item:  # Using the 'in' operator for substring check
        pass
    else: 
        matched.append(item)

sub = df[list(matched)].copy() #subset (remove RRS columns) 
sub['station'] = np.arange(1,len(sub)+1) #add station col

tot_sum = {}
for s in sub.columns[5:-2]:
   tot_sum.update({s:int(sub[s].sum())})  #get total sum of all values 

sorted_tot_sum = sorted(tot_sum.items(), key=lambda item: item[1],reverse=True) #sort by max-->min


abu=[]
for val in sorted_tot_sum:
    if val[1]>50000: #if more than 50000 observations 
        abu.append(val[0])
abu

matched=[] for item in df.columns: if 'Rrs' in item: # Using the 'in' operator for substring check pass else: matched.append(item) sub = df[list(matched)].copy() #subset (remove RRS columns) sub['station'] = np.arange(1,len(sub)+1) #add station col tot_sum = {} for s in sub.columns[5:-2]: tot_sum.update({s:int(sub[s].sum())}) #get total sum of all values sorted_tot_sum = sorted(tot_sum.items(), key=lambda item: item[1],reverse=True) #sort by max-->min abu=[] for val in sorted_tot_sum: if val[1]>50000: #if more than 50000 observations abu.append(val[0]) abu

Out[6]:

['butterfish',
 'silver hake',
 'longfin squid',
 'spot',
 'northern searobin',
 'atlantic croaker']

This is a single survey in 2024.

In [13]:

df['TOWDATETIME_EST'] = pd.to_datetime(df['TOWDATETIME_EST'])
df['TOWDATETIME_EST'].agg(['min', 'max'])

df['TOWDATETIME_EST'] = pd.to_datetime(df['TOWDATETIME_EST']) df['TOWDATETIME_EST'].agg(['min', 'max'])

Out[13]:

min   2024-03-07 10:58:00
max   2024-05-13 07:15:00
Name: TOWDATETIME_EST, dtype: datetime64[ns]

Each station appears once.

In [10]:

station_counts = sub['station'].value_counts().sort_index()
station_counts = station_counts.rename("count").reset_index()
station_counts.columns = ["station", "count"]
station_counts['count'].unique()

station_counts = sub['station'].value_counts().sort_index() station_counts = station_counts.rename("count").reset_index() station_counts.columns = ["station", "count"] station_counts['count'].unique()

Out[10]:

array([1])

Make a plot¶

Look at the abundances by station.

In [15]:

for s in sub[abu].columns:
    plt.plot(sub.station, sub[s],label=s)
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', )
plt.xlabel('Station #')
plt.ylabel('Count')
plt.title('Species with more than 50000 observations')
plt.show()

for s in sub[abu].columns: plt.plot(sub.station, sub[s],label=s) plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', ) plt.xlabel('Station #') plt.ylabel('Count') plt.title('Species with more than 50000 observations') plt.show()

Map the species presence/absence by station location.

In [22]:

import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature

fig, axs = plt.subplots(2, 3, figsize=(12, 8), subplot_kw={'projection': ccrs.PlateCarree()})
axs = axs.flatten()

absent_color = 'gray'
present_color = 'orange'

for i, ax in enumerate(axs):
    species = sub[abu].columns[i]
    absent = sub[sub[species] == 0]
    present = sub[sub[species] != 0]

    ax.scatter(absent.LON, absent.LAT, c=absent_color, s=8, label='Absent')
    ax.scatter(present.LON, present.LAT, c=present_color, s=8, label='Present')

    ax.add_feature(cfeature.COASTLINE, linewidth=1)
    ax.add_feature(cfeature.LAND, zorder=100, facecolor='lightgrey')
    ax.set_title(species)

    # Add legend only to the first subplot to avoid repetition
    ax.legend(loc='lower right')

fig.suptitle('Species Presence by Station', fontsize=14)
plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt import cartopy.crs as ccrs import cartopy.feature as cfeature fig, axs = plt.subplots(2, 3, figsize=(12, 8), subplot_kw={'projection': ccrs.PlateCarree()}) axs = axs.flatten() absent_color = 'gray' present_color = 'orange' for i, ax in enumerate(axs): species = sub[abu].columns[i] absent = sub[sub[species] == 0] present = sub[sub[species] != 0] ax.scatter(absent.LON, absent.LAT, c=absent_color, s=8, label='Absent') ax.scatter(present.LON, present.LAT, c=present_color, s=8, label='Present') ax.add_feature(cfeature.COASTLINE, linewidth=1) ax.add_feature(cfeature.LAND, zorder=100, facecolor='lightgrey') ax.set_title(species) # Add legend only to the first subplot to avoid repetition ax.legend(loc='lower right') fig.suptitle('Species Presence by Station', fontsize=14) plt.tight_layout() plt.show()

In [ ]: