Preliminary Evaluation of Long- Term EAHCP Biomonitoring Data · 2019. 11. 6. · Preliminary...

Preliminary Evaluation of Long-Term EAHCP Biomonitoring Data

Dr. Joshuah S. PerkinDepartment of Wildlife and Fisheries SciencesTexas A&M University

Dr. Ely KosnickiDr. Jacob Jackson

BIO-WEST, Inc.

Background

• Long-term monitoring established by EAA • Water Quality and Quantity• Habitat (SAV) • EAHCP Covered Species Populations• Many Components, some put in place

relatively recently or retired at some point

Objectives

• Summarize data from various long-term monitoring components

• Exploratory analysis• Recommend for questions for potential

future research related to EAHCP LTBG’s

Outline• Water Quantity• Water Quality• Submerged Aquatic Vegetation• Covered Species

– Fountain Darter– San Marcos and Comal Springs Salamanders– Comal Springs Riffle Beetle– Peck’s Cave Amphipod, Comal Springs Dryopid

Beetle and Invertebrate Drift– Macroinvertebrate Community

• Future research and Biological Goals

Water Quantity

• USGS gages– Long-term

• ADCP– Partition flow

among spring sources

Image: USGS

Water Quantity

Water Quantity

Water Quantity

• Summary– Flow regimes transitioned during 2000-

2015– Spatiotemporal variability in Comal Springs

system flows and dominance (per HCP design to protect Fountain Darter habitat) by Old Channel at low flows

Water Quality

• Temperature• Physiochemical/Contaminant

– Grab samples– Data sondes– Storm water and Sediment– Passive Diffuser Samplers

Water Quality• Summary

– Temperature regimes buffered at springs– Grab sample parameters vary longitudinally– Data sondes short-term, high resolution– Storm water elevated E. coli– Contaminants rarely detected; low

concentrations– Meets expectations of stable, clean spring

systems

Submerged Aquatic VegetationComal: 2000-2015 San Marcos: 2000-2015

Submerged Aquatic VegetationComal San Marcos

Submerged Aquatic Vegetation

• Summary– Long-term mapping study reaches– Native species dominate 4 of 7 sites– Non-native declines in coverage at 6 sites– Type-specific trends for 23 species/groups– Full system mapping of both systems

conducted in 2013 – to be repeated in 2018

Fountain Darter• Drop nets

– 7 locations (2000-2015)• Random Dip nets

– 7 locations (2006-2015)• Fixed dip nets

– 7 locations (2014-2015)• Timed dip nets

– 9 locations (2000-2015)• Visual observations

– 1 location (2001-2015)

Image: EAA

Images: BIO-WEST

Fountain Darter• Random forest models

– Tree-based machine learning– Appropriate for data mining

• Few assumptions / little knowledge of system• Nominal and continuous predictor variables• Classification (nominal response) or regression

(continuous response)– Partial dependence plots– Model performance

• % variance explained (regression)• Area Under Curve (classification)

Fountain Darter: Drop Nets

Fountain Darter: Drop Nets

Fountain Darter: Drop Nets

Partial Dependence Plot(modeled # of Fountain Darter based on individual predictors)

Comal Springs and San Marcos Salamanders

Comal (2002-2015) San Marcos (2002-2015)

San Marcos Salamander

Comal Springs Salamander

Comal Springs Riffle BeetleComal Springs System (2004-2015)

Comal Springs Riffle Beetle

Comal Springs Riffle Beetle

Invertebrate Drift

• Drift samples taken biannually from 2003-2015

• Focused on troglobitic species drifting• Estimated discharge (Q)• Investigated relationships of species of

concern with (Q)

Invertebrate Drift

• Some positive relationships with Q• Implies abundance of some species in drift

samples related to hydrology

Macroinvertebrate Community Sampling

• Originally designed to examine food for darters

• Investigate community patterns related to biological integrity and habitat quality

• NMDS macroinvertebrate community– Vegetation types– Sample reaches

Macroinvertebrate Community DataStress=0.26

Stress=0.26

Macroinvertebrate Community Data

Stress=0.26

Macroinvertebrate Community Summary

Conclusions• Current dataset is limited for exploring community-

level characteristics• No strong differences among vegetation types• River systems and separate reaches contain

different community structures– New sampling strategies implemented in 2017 will

capture more of the diversity– Value in developing specific monitoring program for

spring-driven river systems

Covered Species• Summary

– Little explanatory power using local-scale variables

– Abundances largely stable through time– Opportunity to link data sets to test

hypotheses regarding ecology of species• Water quantity, water quality, aquatic vegetation

Future Research• Monitoring data can be leveraged to measure

progress towards EAHCP Long-Term Biological Goals (LTBGs)

• Hypotheses:– Local-scale environmental covariates affect

detection, broad-scale processes affect abundances

– Flow, water quality, aquatic vegetation at system or site scale may correlate with abundances more strongly than local variables

Future Research: Fountain Darter• Modeled densities near or

exceed LTBGs

• No measure of uncertainty or links to processes

• Approach:– Open population N-

mixture models– Sample-scale detection

covariates– System- and site-scale

abundance covariates

Future Research: Salamander• Approach:

– Open population N-mixture models

– Sample-scale detection covariates

• Depth, rocks moved– System- and site-scale

abundance covariates• Spring flow, water

temperature, submerged aquatic vegetation

Goal

Goal

Goal

Goal

Goal

Future Research: Riffle Beetle

• Approach:– Spatial point models– Sample-scale detection

covariates• Repeated lure traps

distributed through space– System- and site-scale

abundance covariates• Spring flow

Goal

Goal

Goal

• RF model to estimate numbers in flow• Any species in drift• Ambient physicochemical measures• Temporal conditions

– Cumulative precipitation– Days since peak discharge– Ground water estimates

Future Research: Drift Monitoring Tool

Future Research: Drift Monitoring Tool

• Proof of concept with Stygobromus• Model explained ~ 37% of the variation• Predictions better for Spring Run 3 and West Shore

– Underestimates for Spring Run 1

• Predictions fairly good for between 50 – 200– Large deviations from predicted numbers imply changes in habitat

connectivity or population size– Estimate population size (with assistance of laboratory experiments)

Future Research: Drift Monitoring Tool

Thank You

Contact:jperkin@tamu.eduPh: (979) 458 1814

ekosnicki@bio-west.comjjackson@bio-west.com

Ph: (512) 990 3954