Introduction¶

calcofi_cruise Source: CalCOFI

The California Cooperative Oceanic Fisheries Investigations (CalCOFI) is an organization that focuses on studying the marine environment surrounding the California coast in order to investigate the relationships between climate, fish population, and other human activities.

Our project initially focused on two separate datasets:

Fish Larvae Catch Data¶

This data was collected from CalCOFI cruises and was used to measure quantities and catch amounts of 6 different fish species.

# rename columns to non-scientific names for readability
larval = larval_df.rename(columns={'Engraulis.mordax':'Anchovy, Northern','Trachurus.symmetricus':'Mackerel, Jack', 'Scomber.japonicus':'Mackerel, Pacific','Lampridiformes1':'Opah','Sardinops.sagax':'Sardine','Seriola.lalandi':'Yellowtail'})

# select columns to show
cols_to_display_larval = ['longitude','latitude','year','season','Anchovy, Northern','Mackerel, Jack',
'Mackerel, Pacific','Opah','Sardine','Yellowtail']

# print head of selected columns
larval[cols_to_display_larval].head(10)

	longitude	latitude	year	season	Anchovy, Northern
0	-120.776667	35.086667	2019	spring	0.000000
1	-120.915000	35.021667	2019	spring	0.000000
2	-121.196667	34.890000	2019	spring	36.666667
3	-121.551667	34.720000	2019	spring	40.720000
4	-122.245000	34.390000	2019	spring	4.850000
5	-122.958333	34.048333	2019	spring	4.340000
6	-120.495000	34.465000	2019	spring	0.000000
7	-120.528333	34.453333	2019	spring	4.170000
8	-120.790000	34.328333	2019	spring	1801.358491
9	-121.153333	34.150000	2019	spring	351.909091

The larvae catch dataset contains information on time (year),location (latitude/longitude), and the amount (pounds) of larvae found. It is sorted by species type.

The dataset is very sparse, since the majority of cruises caught zero pounds of a given larvae species. Scroll through and see just how many types of fish there are, and how few are caught per cruise!

Commercial Fish Data¶

This data was collected from various California fisheries and was used to supplement the fish catch data by providing additional information on amounts of fish caught.

# remove NAs from fishery data
cleaned_fishery = fishery_updated.dropna(how='any')
cleaned_fishery = cleaned_fishery[cleaned_fishery['Total Price'] != ' ']

# print data
cleaned_fishery.head(10)

	Year	Quarter	GroupBlocks	Species ID	Species Name	Pounds	Total Price
0	1987	Qtr 1	1032.0	40.0	Yellowtail	256.0	$272
3	1987	Qtr 2	1032.0	40.0	Yellowtail	1710.0	$1,511
5	1987	Qtr 2	1034.0	40.0	Yellowtail	1417.0	$1,177
6	1987	Qtr 2	1035.0	40.0	Yellowtail	6457.0	$6,267
8	1987	Qtr 2	600.0	40.0	Yellowtail	3071.0	$2,644
9	1987	Qtr 2	700.0	40.0	Yellowtail	2622.0	$2,549
10	1987	Qtr 2	800.0	40.0	Yellowtail	2815.0	$2,704
11	1987	Qtr 3	1032.0	40.0	Yellowtail	7335.0	$8,019
13	1987	Qtr 3	1034.0	40.0	Yellowtail	3645.0	$3,127
14	1987	Qtr 3	1035.0	40.0	Yellowtail	4458.0	$4,397

The commercial fishery dataset contains information on time (year/month), location (latitude/longitude), amount of fish caught (pounds), and price-per-pound (dollars) of each fish in every given catch year.

Due to data privacy rules, some of the information relating to price-per-pound and pounds caught are redacted.

In the beginning, we explored these two datasets with the intention of finding a relationship between the amount of fish larvae detected and the amount of adult fish caught. Over time, our workflow and goal became more developed, leading us to incorporate a third dataset into our project:

Bottle and Cast Data¶

This dataset was used to incorporate additional environmental factors with our larvae and commercial fish data.

# rename columns for readability
bottle_preview = bottle.rename(
    columns={
        'Cst_Cnt':'Cast Count',
        'Ocean degC':'Water Temperature (C)',
        'Salnty':'Water Salinity'}
    )

# select columns to keep
bottle_preview[['Year','Cast Count','Water Temperature (C)','Water Salinity']].head(10)

	Year	Cast Count	Water Temperature (C)	Water Salinity
0	1949	462.984846	9.008097	33.875273
1	1950	1481.434568	8.670928	33.884616
2	1951	2938.570895	8.544260	33.874098
3	1952	4422.839220	9.408110	33.753253
4	1953	5845.616774	9.794919	33.796447
5	1954	6875.716300	9.762641	33.773640
6	1955	7645.020691	9.516349	33.854505
7	1956	8329.149738	9.246048	33.844304
8	1957	9043.949093	10.520039	33.798667
9	1958	9883.231321	10.693563	33.767853

The bottle and cast data contains information on cast counts, water temperature (celcius) during a given cast, and water salinity (salinity scale).

Cast refers to information about the cruise itself, while bottle refers to information regarding the water quality, such as temperature and salinity.

After incorporating the bottle and cast dataset, our project’s goal shifted from establishing whether any correlations can be drawn from the fish larvae and fish catch to investigating any environmental factors that may add variance to the relationship as well. Additionally, it helped us focus on one species in particular: the Pacific Sardine.

The Shift Towards Sardine¶

# clean and process landing data (time series version)
fish_count_ts_df = landing_summary_ts.filter(['Year','SpeciesName'],axis=1)
fish_count_ts_df.drop(fish_count_ts_df.tail(5).index,inplace=True)

# create separate dataframes for each species
# groupby year, find counts per year, and rename columns
mjack_ts = fish_count_ts_df[fish_count_ts_df['SpeciesName']=='Mackerel, jack'].groupby('Year').count().reset_index().rename(columns={'SpeciesName':'Count'})
anchovy_ts = fish_count_ts_df[fish_count_ts_df['SpeciesName']=='Anchovy, northern             '].groupby('Year').count().reset_index().rename(columns={'SpeciesName':'Count'})
mpacific_ts = fish_count_ts_df[fish_count_ts_df['SpeciesName']=='Mackerel, Pacific'].groupby('Year').count().reset_index().rename(columns={'SpeciesName':'Count'})
yellowtail_ts = fish_count_ts_df[fish_count_ts_df['SpeciesName']=='Yellowtail'].groupby('Year').count().reset_index().rename(columns={'SpeciesName':'Count'})
opah_ts = fish_count_ts_df[fish_count_ts_df['SpeciesName']=='Opah'].groupby('Year').count().reset_index().rename(columns={'SpeciesName':'Count'})
sardine_ts2 = fish_count_ts_df[fish_count_ts_df['SpeciesName']=='Sardine, Pacific              '].groupby('Year').count().reset_index().rename(columns={'SpeciesName':'Count'})

# label each dataframe based on species type
mjack_ts['Species'] = 'Mackerel Jack'
anchovy_ts['Species'] = 'Anchovy'
mpacific_ts['Species'] = 'Mackerel Pacific'
yellowtail_ts['Species'] = 'Yellowtail'
opah_ts['Species'] = 'Opah'
sardine_ts2['Species'] = 'Sardine'

# combine dataframes
species_ts = pd.concat([mjack_ts,anchovy_ts,mpacific_ts,yellowtail_ts,opah_ts,sardine_ts2],ignore_index=True)

# pivot combined dataframe
species_total_ts = species_ts.pivot(
    index = 'Year',
    columns = 'Species',
    values = 'Count'
)

# impute missing values in Opah column with means
species_total_ts_final = species_total_ts.fillna(round(species_total_ts.Opah.mean(),1))

# construct line plot
# add custom colors to grey out all species except sardine
fig_species_total = px.line(
    species_total_ts_final,
    y = ['Sardine','Anchovy','Mackerel Jack','Mackerel Pacific', 'Opah', 'Yellowtail'],
    color_discrete_sequence= ['blue','gray','gray','gray','gray','gray'],
    labels = {'value':'Abundance (thousands)'},
    title = 'Abundance By Year (All Species)'
)

# add annotations to specified points on the lines
fig_species_total.update_layout(
    annotations=[
        {'x':8,'y':360,'text':'Mackerel Jack'},
        {'x':6,'y':268,'text':'Anchovy'},
        {'x':12,'y':438,'text':'Mackerel Pacific'},
        {'x':15,'y':264,'text':'Opah'},
        {'x':4,'y':144,'text':'Yellowtail'},
        {'x':38,'y':296,'text':'Sardine'}],
        showlegend=False)


fig_species_total.show() 