Development of a spatial-temporal co-

occurrence index to evaluate pesticide

risks to threatened and endangered

speciesCALFED Part 4

Gerco Hoogeweg, Debra Denton (EPA R9), Richard S. Breuer (CA DWR), Gregg Hancock, and Patti TenBrook (EPA R9)

Project Context

Spatial and Temporal Quantification of Pesticide

Loadings to the Sacramento River, San Joaquin

River, and Bay‐Delta to Guide Risk Assessment for

Sensitive Species

Part 1. Project overview (Sunday)

Part 2. Agricultural modeling (Monday)

Part 3. Urban modeling (Tuesday)

Part 4. Development and application of a multi-dimensional index

2

Project Context

• The decline in pelagic species in the San Francisco Bay-

Delta Estuary has led to investigations into the role of

contaminants as the cause of decline.

• The POD contaminants work group identified the need to

provide spatial and temporal information on the presence

of contaminants to further focus biomarker and toxicity

identification evaluation (TIE) studies.

3

Project Context

• Key Objectives• Provide further knowledge of the fate and transport of agricultural

chemicals in the Sacramento River, San Joaquin River, Bay-

Delta Estuary, and headwater tributaries

• Overlay pesticide loading results with the identification and

location of sensitive fish species critical habitats

• Identify and rank areas of highest risk and pesticide

loadings

Determine co-occurrence of species and at the

same time provide a relative ranking of areas

4

Study Area

Bay-Delta estuary land use

5

Species List

1. Chinook Salmon (Oncorhynchus tshawytscha)• Sacramento River winter-run

• Central Valley spring-run

• Central Valley fall run

• Central Valley late fall run

2. Central Valley Steelhead (O. mykiss)

3. Southern North American Green Sturgeon (Acipenser medirostris)

4. Delta Smelt (Hypomesus transpacificus)

5. Striped Bass (Morone saxatilis)

6. San Francisco Longfin Smelt (Spirinchus

thaleichthys)

7. Threadfin Shad (Dorosoma petenense)

8. California Red-legged Frog (Rana draytonii)

9. California Freshwater Shrimp (Syncaris pacifica)Longfin smelt photo by René Reyes, US Bureau

of Reclamation. http://calfish.ucdavis.edu/index.cfm 6

Pesticides

• Abamectin Insecticide

• Bifenthrin Insecticide

• Bromacil Herbicide

• Captan Fungicide

• Carbaryl Insecticide

• Chlorothalonil Fungicide

• Chlorpyrifos Insecticide

• Cyfluthrin Insecticide

• Cypermethrin Insecticide

• Deltamethrin Insecticide

• Diazinon Insecticide

• Dimethoate Insecticide

• Diuron Herbicide

• Esfenvalerate Insecticide

• Fipronil Insecticide

• Hexazinone Herbicide

• Imidacloprid Insecticide

• Indoxacarb Insecticide

• Lambda cyhalothrin Insecticide

• Malathion Insecticide

• Mancozeb Fungicide

• Maneb Fungicide

• Methomyl Insecticide

• Naled Insecticide

• Norflurazon Herbicide

• Oxyflurofen Herbicide

• Paraquat dichloride Herbicide

• Pendimethalin Herbicide

• Permethrin Insecticide

• Propanil Herbicide

• Propargite Insecticide

• Pyraclostrobin Fungicide

• (s)-Metolachlor Herbicide

• Simazine Herbicide

• Tralomethrin Insecticide

• Trifluralin Herbicide

• Ziram Fungicide

7

Blue = High Overall Relative-Risk Level Green = Moderate Overall Relative-Risk Level

Co-occurrence Approaches

• GIS Overlay

• Probabilistic assessment (e.g. Cramer Fish Sciences)

• Joint Probability

• Presence-absence matrices

• Checkerboard approach (number of species forming perfect

checkerboard)

• Checkerboard C-Score

• Variance ratio

• Combo (number of unique combinations)

• Bayesian co-occurrence

8

Co-occurrence

• What is co-occurring?

• Pesticides in surface water

• Species of concern

• Requirement for co-occurrence

• Same location (PLSS section)

• Same time (month)

• Goal is to develop an scalable index that

takes into account available species and

pesticide information

9

Co-occurrence Index

• Pesticides in surface water

• Risk assessment the Risk Quotient (RQ) is used.

• Events with RQ ≥ 1 are of interest

• Potential effects on SOC

• In order to calculate the RQ, you need to have a

benchmark.

10

Co-occurrence Index

• Pesticides Toxicity

• For each pesticide (40) an acute threshold was

chosen:

• Determined most conservative EPA-OPP benchmark for

each pesticide

• Only one toxicity threshold for each pesticide

• Added 10x safety factor to account for T&ES

11

Questions to consider when developing a

Co-occurrence Index

RQ ≥ 1

What if two

pesticides have RQ ≥

1 on the same day?

Do we need to

consider

additive/cumulative

effects?

What time period do

we consider?

Can we calculate a

fraction of events

exceeding the

benchmarks?

Can we kill the same

fish or shrimp twice?

Do we know which

pesticide affects

which species?

Do we consider all

events where RQ ≥

1?

Do we need to

consider sublethal

effects?

Are we doing a

absolute or relative

ranking?

Consider Indirect effects?

What is more important;

the species or the

pesticide? 12

Co-occurrence Index

• Indicator Day Scalar

• Indicator Day is a day that one or

more pesticides exceed the

toxicity threshold

1. On monthly basis compute the

fraction of indicator days

In = (I / (10 * Nd))

Develop a “population” dependent

scalar that takes into account the

full distribution of exceedance

days determined in the study

framework

13

Indicator Days

Distribution of Indicator Days for randomly selected PLSS Sections

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1 2 3 4 5 6 7 8 9 10 11 12

Fra

cti

on

of

Ind

icato

r D

ays

Month

14

Co-occurrence Index

• Indicator Day Scalar• Indicator Day is a day that one or more

pesticides exceed the toxicity threshold

1. On monthly basis compute the fraction of indicator days

In = (I / (10 * Nd))

2. Determine the percentile points• (10th, 20th, … 90th, 100th)

• For each percentile level we know the respective fraction of indicator days

Develop a “population” dependent

scalar that takes into account the

full distribution of exceedance

days determined in the study

framework

15

Indicator Days

Distribution of Indicator Days for all months and all PLSS sections

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

0

1000

2000

3000

4000

5000

6000

7000

0.0

03

0.0

4

0.0

8

0.1

2

0.1

6

0.2

0.2

4

0.2

8

0.3

2

0.3

6

0.4

0.4

4

0.4

8

0.5

2

0.5

6

0.6

0.6

4

0.6

8

0.7

2

0.7

6

0.8

0.8

4

0.8

8

0.9

2

0.9

6 1

Fre

qu

en

cy

Fraction Indicator Days

FrequencyCumulative %

16

Indicator Day Scalar

• Percentile Fractions

Percentile Indicator DaysFraction

10 0.017

20 0.055

30 0.100

40 0.153

50 0.206

60 0.303

70 0.447

80 0.500

90 0.589

100 0.994

Provided a relative ranking of

indicator days as defined within the

framework of the study

Used for defining a standardized

map legend and thus we can easily

compare the months

17

Indicator Days

18

Co-occurrence Index

• Critical habitat data was not

used

• Cramer Fish Sciences

developed range data

• Species Information

• Presence/Absence

• Abundance

• Data is available at a monthly

basis

• Approach: Determine species

richness for each PLSS

section by month

19

Species Richness Scalar

• Species

• 12 species are considered in a study (N)

• M species present at any given time

(month) at a given location (PLSS

section)

Sn = (M/N)

• Sn = Fraction of species richness

• Next determine the percentile of the

species richness

• (10th, 20th, … 90th, 100th)

Develop a “population” dependent

scalar that takes into account the

full distribution of number of

species present in the study

framework

20

Co-occurrence Index

• Species richness scalar

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Fre

qu

en

cy

Species Richness

Frequency

Percentile Fraction

10 0.250

20 0.250

30 0.250

40 0.333

50 0.333

60 0.333

70 0.333

80 0.333

90 0.500

100 0.917

Basic statistics

21

Species Richness

Fraction Percentiles

22

Co-occurrence Index

• What we know:

• Scalar: representing the fraction of indicator days by

month

• Scalar: representing the species richness by month

• Next we combine these two sets into a multi-

dimensional index

• Holmes Index of Co-occurrence

23

0

0

Multi-dimensional Index

No potential co-

occurrence

Increased

Potential co-

occurrence

Indicator Days

S

pecie

s R

ichness

No co-occurrence

No co-occurrence24

Multi-dimensional Index

S

pecie

s R

ichness

Indicator Days

0 1 2 3 4 5 6 7 8 9 10

0 00 01 02 03 04 05 06 07 08 09 010

1 10 11 12 13 14 15 16 17 18 19 110

2 20 21 22 23 24 25 26 27 28 29 210

3 30 31 32 33 34 35 36 37 38 39 310

4 40 41 42 43 44 45 46 47 48 49 410

5 50 51 52 53 54 55 56 57 58 59 510

6 60 61 62 63 64 65 66 67 68 69 610

7 70 71 72 73 74 75 76 77 78 79 710

8 80 81 82 83 84 85 86 87 88 89 810

9 90 91 92 93 94 95 96 97 98 99 910

10 100 101 102 103 104 105 106 107 108 109 1010

25

0 1 2 3 4 5 6 7 8 9 10

0 00 01 02 03 04 05 06 07 08 09 010

1 10 11 12 13 14 15 16 17 18 19 110

2 20 21 22 23 24 25 26 27 28 29 210

3 30 31 32 33 34 35 36 37 38 39 310

4 40 41 42 43 44 45 46 47 48 49 410

5 50 51 52 53 54 55 56 57 58 59 510

6 60 61 62 63 64 65 66 67 68 69 610

7 70 71 72 73 74 75 76 77 78 79 710

8 80 81 82 83 84 85 86 87 88 89 810

9 90 91 92 93 94 95 96 97 98 99 910

10 100 101 102 103 104 105 106 107 108 109 1010

Multi-dimensional Index

S

pecie

s R

ichness

Indicator Days

What

does 73

mean?

73 means that at

least 70% of the

species are present

and the top 70

percentile of the

indicator events.

Emphasis would be

on the species

What

does

37

mean

?

37 means that top

70 percentile of the

indicator events are

considered and at

least 30% of the

species are present.

Emphasis would be

on the pesticides.

26

Implementation

• Q: Show all areas with

at least half the

species present and

50th percentile

indicator events (0.2)

for the month of July?

Biggest driver for the

gaps is lack of

species data

27

Implementation

• Q: Show all areas

exceeding the 90th

percentile for the

indicator days?

• Period Jan - March

28

Conclusions

• 2-dimensional scalable Index matrix was

developed to determine co-occurrence of TE&S

and pesticide concentrations in surface water

exceeding toxicity benchmark

• Index allows you to scale the rankings based on

available data

• Index provides a relative ranking of area with

“high” co-occurrence

• Good species distribution data is important

29