Murray-Darling Basin Biodiversity

Carlos E. González-Orozco and Peter Unmack

Bernd Gruber, Arthur Georges and Duanne White

Monday, 1st June 2015

Institute for Applied Ecology

MDBfutures CRN Biodiversity Project

Contributed Staff Prof Arthur Georges Prof Max Finlayson (CSU) Dr Duanne White Dr Xiuwen Zhang Senior Postdoctoral Fellow Dr Bernd Gruber Postdoctoral Fellows Dr Peter Unmack Dr Carlos Gonzalez-Orozco Dr Mariagrazia Bellio (CSU) Dr Aaron Adamack (ARC) Postgraduate Students Angelica Lopez Kate Hodges Bruno Ferronato Research Assistant Matthew Young

Many people beyond the project also contributed by providing samples, locality information, advice, and other help including: Mark Adams (SA Museum), Michael Hammer (NT Museum), Tim Page, Jane Hughes & Mark Kennard (Griffith Uni), Jon Marshall (Qld DSITI), Bernie Cockayne Qld DNRM), Bruce Chessman (NSW EH), Gavin Butler & Dean Gilligan (NSW Fisheries), Tarmo Raadik (Vic DELWP), Luciano Beheregaray lab (Flinders Uni), James Shelley (Melb Uni) and many others. Kate Hodges, Tim Page, Phil Littlejohn, Alan Couch, Lara Upton, Elena Farnsworth, Margaret Sweeney, Ian Muir, Josh Malony all joined us to help with fieldwork

Acknowledgements

1. Biodiversity and

bioregionalization of the MDB

2. Species distribution modelling

3. Genetic population structure

Top-Down study patterns of

biodiversity

Bottom-Up study processes

that affect biodiversity

Management recommendations

Biodiversity and Conservation

Species Distribution Modelling (Bernd Gruber)

Bioregionalization of the MDB (Carlos E. González-Orozco)

Genetic population structure (Peter Unmack)

Ramsar Wetlands (Max Finlayson)

Sub-projects

One of the main pitfalls in Biodiversity

Biodiversity isn’t just species

• Biodiversity is the whole tree of life, not just the named species

• There are clades smaller and larger than the traditional species level

• Species are not comparable between lineages in any manner, just an arbitrary cut-off somewhere along a branch tree of life

Species richness for eucalyptus in the MDB

Number of species per grid cell

New methods applied to our project

1. Multiple-taxon combinations (Summary maps for raw diversity, clusters of fuzzy

logic, combine maps of significance)

2. Categorical Analyses of Neo- and Paleo- Endemism (CANAPE). Developed as part of software Biodiverse

3. CANAPE modelling under future climate change. Developed as part of software Biodiverse

Data Sources

1. Species distributions: Australia’s Virtual Herbarium, historical collections from museums

2. Phylogenies: Different universities, CSIRO

3. Climate change projections: Hadley global climate model (hadcm3) available from http://wallaceinitiative.org/

Multiple taxon combinations: Species richness for all taxon groups Acacias Eucalypts Plant genera Tree frogs Fishes

Categorical Analysis of Neo and Paleo Endemism (CANAPE)

© J.Plaza RBG Sydney

Paleo-endemism Neo-endemism

Wollemi Pine (200 Mya, Araucariaceae)

Darwin’s Finches (15-20 Mya, Galapagos)

What do we learn from the new methods? CANAPE

Raw values of species richness

CANAPE

Need for hypothesis tests: raw values do not tell us much, unless we understand how much to expect

Maps of CANAPE for five taxonomic groups in the MDB

CANAPE Acacia Eucalyptus Plant genera

Tree frogs Fishes No common phylogenetic

patterns across the studied taxon groups

González-Orozco, C.E et al. (2015) Manuscript2 in prep.

Phylo-bioregions for acacias and eucalyptus in the MDB

acacias

eucalyptus

“Phylo-Jaccard index express the

proportion of lineage diversity

that is shared between two set

of locations”

González-Orozco, C.E et al. (2015) Manuscript2 in prep.

Some conservation applications: acacias and eucalyptus in the MDBA as an example

González-Orozco, C.E et al. (2015) Manuscript3 in prep.

Example of the impact of climate change on phylogenetic diversity of eucalyptus

• Spatial data

• Phylogenetic tree

• Modelling species shifts under current and future scenarios

• Incorporation of phylogenetic approaches under current and future climate change scenarios

Eucalypt species are shifting under climate change by 2085

González-Orozco, C.E et al. (2015) Manuscript1 in prep.

Phylogenetic diversity of eucalypts on the move

González-Orozco, C.E et al. (2015) Manuscript1 in prep.

2014 2085

Disappearance of areas with paleo and neo-endemism of eucalypts by 2085 due to climate change

González-Orozco, C.E et al. (2015) Manuscript1 in prep.

2014 2085

Losses of unique branches from the eucalypts tree under climate change by 2085

González-Orozco, C.E et al. (2015) Manuscript1 in prep.

Eucalyptus (5 sp.) southwest Western Australia

Aquatic Biogeography MDB

M. adspersa

Goal

A. agassizii

Are there common phylogeographic patterns within MDB aquatic species?

Should we manage parts of MDB separately?

Goal

The MDB is surrounded by more basins (22) than any other in Australia

This provides a convoluted and dynamic biogeographic setting with different fishes having relationships with many surrounding basins

R. semoni

Outline

• Within basin biogeographic patterns

• Within basin genetic patterns

• Next generation sequence data

G. bispinosus

MDB contains

36 obligate freshwater fishes

3 diadromous fishes

at least 5 estuarine spp

N. australis

MDB has relatively few narrow range species (9/36)

Species # Basins Basin

Porochilus rendahli 1 Condamine Galaxias Tantangara 1 Murrumbidgee Galaxias fuscus 1 Goulburn Gadopsis bispinosus II 1 Goulburn Gadopsis marmoratus II 1 Wimmera Nannoperca australis B 1 Wimmera Nannoperca obscura 1 Lower Murray tribs Melanotaenia splendida 2 Warrego-Paroo Craterocephalus amniculus 3 Border, Namoi, Gwydir 15 species are essentially found across most of the MDB (>16/24 sub-basins)

MDB contains 14/36 (39%) endemic fishes

Maccullochella peelii (22) Craterocephalus fluviatilis (7)

Gadopsis marmoratus I (21) Galaxias Riffle (5)

Melanotaenia fluviatilis (20) Gadopsis bispinosus I (5)

Macquaria ambigua (20) Craterocephalus amniculus (3)

Galaxias rostratus (12) Galaxias fuscus (1)

Maccullochella macquariensis (12) Galaxias Tantangara (1)

Macquaria australasica (11) Gadopsis bispinosus II (1)

G. rostratus

MDB UPMGA faunal similarity based on Jaccard’s Index

0.0 0.2 0.4 0.6 0.8

UMU

KIE

OVE

BRO

CAM

LOD

MUR

GOU

MBI

LMU

LAC

MAC

LMT

BOR

GWY

NAM

CAS

MOO

DAR

CON

WAR

PAR

AVO

WIM

Murray

Darling

Warrego-Paroo

Wimmera-Avoca

Outline

• Within basin biogeographic patterns

• Within basin genetic patterns

• Next generation sequence data

H. klunzingeri

mtDNA Data

Most fishes in the basin have been examined

Most species show low haplotype numbers, diversity and structure (no multiple ESUs identified within MDB)

Some rivers are slightly distinct, especially Warrego-Paroo

Four species show higher within basin differences which should be considered as MUs

Craterocephalus amniculus C. fluviatilis

Nannoperca australis Gadopsis marmoratus

Microsatellite Data

Few species examined

Similar to mtDNA, but more resolution

M. peelii Lachlan, Macquarie, Gwydir (Rourke et al. 2011)

M. ambigua Macquarie, Paroo (but only slight) (Faulks et al. 2010)

M. australasica unclear given massive decline (Faulks et al. 2011)

N. obscura no diffs within MDB (Brauer et al. submitted)

N. australis most sub-basins distinct (Cole et al. in prep)

G. marmoratus most sub-basins distinct (Lean et al. in prep)

Next-gen SNP datasets are now being gathered for several species

Outline

• Within basin biogeographic patterns

• Within basin genetic patterns

• Next generation sequence data

H. klunzingeri P. grandiceps

Study Groups

Our genetic work is focused on four taxonomic groups:

Australian Smelt Retropinna semoni

River turtle Emydura macquarii

River shrimp Macrobrachium australiense

Yabby Cherax destructor

All are widespread, lack quirks such as hybridisation or cryptic species

Genetic Methods

Samples preserved in the field in liquid nitrogen

DNA extractions

Diversity Arrays Technology conducts the DNA sequencing using “double-digest restriction-site associated DNA sequencing” or ddRadSeq for short

Genetic Methods

ddRadSeq

Genomic DNA is “digested” using two enzymes which cut DNA at specific sites (e.g., AATTCC)

This creates lots of different length fragments

Fragments ~78 base pairs long get saved and sequenced

This produces approximately 2,500,000 reads per sample, or about 20 gigabases of sequence (seven human genomes)

Data is then cleaned up bioinformatically, but is time and computationally intensive

From DNA submission to data is around 3 months

Genetic Methods

DArT produces data file with around 30-40,000 variable loci (locus = unique place within the genome)

Because this data type is new, only a few methods are available

Complicated by the massive size of data files

Bernd is developing new pipeline using R package for analyses

Turtle Genetic Patterns

Turtle Genetic Patterns

RAxML, 400 reps

76 otus, 1246416 bp!

1230818 characters are constant

1896 variable characters are pars-uninformative

Pars-informative characters = 13702

SEQ Pine Sandgate SEQ Wivenhoe

SEQ Pine Bunya SEQ Albert

NEN Tweed Uki NEN Georges NEN Jackadery NEN Yates

NEN Casino NEN Hunter Clarencetown NEN Hunter Isis NEN StClaire

MDB Narromine MDB Mollee

MDB Eulo MDB Toonborough

MDB Boothulla MDB Dartmouth

MDB Ambathala MDB Sandford

MDB Binya MDB Bourke

MDB Gunnedah MDB Pindari MDB Coonabarabran MDB Gin Gin

MDB Bingara MDB Wetherell MDB Lock 10

MDB Barren MDB Bowman

MDB Burrinjuck MDB Lake Forbes

MDB Tarrawingee MDB Tintaldra MDB Laanecoorie MDB Tahbilk MDB Gurra Gurra MDB Mungabareena MDB Morgan Cadel MDB Murray Bridge

MDB Cudgegong MDB Bonshaw MDB Kurmalla MDB Goondiwindi MDB Leslie

MDB Archers MDB Kupunda

MDB Mitchell SEQ Fraser Mackenzie

SEQ Fraser Bowarrady SEQ Mary Petrie SEQ Mary Borumba

SEQ Barambah SEQ Mary Pioneers

SEQ Kolan CEQ Fitz Fairbairn CEQ Fitz Alligator

CEQ Fitz Widewater CEQ Fitz Korcha

CEQ Fitz Warrinilla CEQ Styx

CEQ Laglan CEQ Ross

NEQ Normanby Jacks NEQ Johnstone Warina

NEQ Barron Gardens NEQ Eubenangee NEQ Barron Tinaroo

BULL Como BULL Gumbo BULL Quilpie LEB Dunn LEB Cullyamurra LEB Eulbertie

CEQ Fitz Hoods

LEB

NEN

SEQ

CEQ

NEQ

MDB

SEQ

MDB Turtle Genetic Patterns d = 5

MDB-Ambathala

MDB-Archers

MDB-Barren

MDB-Bingara

MDB-Binya

MDB-Bonshaw MDB-Bourke

MDB-Bowman

MDB-Burrinjuck

MDB-Coonabarabran MDB-Cudgegong

MDB-Dartmouth

MDB-Gin-Gin MDB-Goondiwindi

MDB-Gunnedah

MDB-Gurra_Gurra

MDB-Kupunda MDB-Kurmalla

MDB-Laanecoorie

MDB-Lake_Forbes

MDB-Leslie

MDB-Lock_10

MDB-Mitchell

MDB-Mollee

MDB-Morgan-Cadel MDB-Mungabareena

MDB-Murray_Bridge

MDB-Narromine

MDB-Pindari

MDB-Sandford MDB-Tahbilk MDB-Tarrawingee MDB-Tintaldra

MDB-Wetherell

Subset MDB Turtle Genetic Patterns

Turtle Genetic Diversity NEN

SEQ

CEQ

NEQ

MDB

LEB / BULL

NEN

-Hu

nte

r C

lare

nce

tow

n

NEN

-St

Cla

ire

NEN

-Hu

nte

r Is

is

NEQ

-No

rman

by

Jack

s LE

B-E

ulb

erti

e

LEB

-Cu

llyam

urr

a

MD

B-N

arro

min

e

NEN

-Geo

rges

N

EN-J

acka

der

y

SEQ

-Alb

ert

NEQ

-Jo

hn

sto

ne

War

ina

NEN

-Yat

es

CEQ

-Ro

ss

CEQ

-Lag

lan

N

EQ-E

ub

enan

gee

SEQ

-Mar

y P

ion

eers

SEQ

-Fra

ser

Mac

ken

zie

SE

Q-F

rase

r-B

ow

arra

dy

SEQ

-Pin

e B

un

ya

MD

B-A

mb

ath

ala

NEQ

-Bar

ron

Tin

aro

o

LEB

-Du

nn

NEQ

-Bar

ron

Gar

den

s N

EN-C

asin

o

CEQ

-Sty

x

MD

B-M

olle

e

SEQ

-Wiv

enh

oe

MD

B-G

un

ned

ah

MD

B-M

itch

ell

MD

B-C

on

nab

arab

ran

MD

B-G

in-G

in

MD

B-P

ind

ari

MD

B-L

ake

Forb

es

CEQ

-Fit

z-K

orc

ha

MD

B-K

up

un

da

MD

B-D

artm

ou

th

MD

B-B

urr

inju

ck

CEQ

-Fit

z W

idew

ater

0.20

0.15

0.10

0.05

0.00

BU

LL-Q

uilp

ie

NEN

-Tw

eed

Uki

M

DB

-Les

lie

SEQ

-Mar

y P

etr

ie

MD

B-S

and

ford

B

ULL

-Gu

mb

o

CEQ

-Fit

z W

arri

nill

a

CEQ

-Fit

z A

lliga

tor

MD

B-B

ow

man

M

DB

-Bin

ya

MD

B-B

oo

thu

lla

CEQ

-Fit

z Fa

irb

urn

MD

B-T

arra

win

gee

MD

B-C

ud

gego

na

MD

B-B

arre

n

SEQ

-Ko

lan

MD

B-L

ock

1.0

MD

B-L

aan

eco

ori

e M

DB

-Gu

rra

Gu

rra

MD

B-M

urr

ay B

rid

ge

MD

B-T

ahb

ilk

MD

B-M

org

an-C

adel

M

DB

-Bin

gara

MD

B-G

oo

nd

iwin

di

MD

B-M

un

gab

aree

na

MD

B-K

urm

alla

MD

B-B

on

shaw

MD

B-T

inta

ldra

M

DB

-Bo

urk

e

MD

B-A

rch

ers

SEQ

-Mar

y B

oru

mb

a

MD

B-W

eth

erel

l

SEQ

-Bar

amb

ah

SEQ

-Pin

e Sa

nd

gate

CEQ

-Fit

z H

oo

ds

MD

B-T

oo

nb

oro

ugh

BU

LL-C

om

o

MD

B-E

ulo

Shrimp Genetic Patterns

d = 5

MDB-Ashf

MDB-Bour

MDB-Goon

MDB-Goul

MDB-Laan

MDB-Oven

MDB-Pall

MDB-Toon

MDB-Umur

MDB-Wagg

MDB-Wilc

Shrimp Genetic Diversity

Yabby Genetic Diversity d = 20

MDB-Ashf MDB-Bour MDB-Goon

MDB-Laan MDB-Naga MDB-Oven

MDB-Pall

MDB-Toon

MDB-Umur MDB-Wagg

MDB-Wilc

Yabby Genetic Diversity

Paroo Sth MDB Nth MDB

ARC Linkage Grant

Drivers of fine scale

genetic spatial

structuring in

aquatic organisms

Conclusions

MDB, at least for turtles has high levels of genetic diversity

Paroo River consistently sticks out, although possibly for different reasons

Some north-south separation in shrimp and yabby

Severn River tends to stand out as being a little different

C. fluviatilis

Thank You

M. splendida