Planning to plant an Australian native like wattle? Read this first — you might be spreading a weed

Coastal wattle.
Dr David Chael, Author provided

Singarayer Florentine, Federation University AustraliaAustralian native plants are having a moment in the sun, with more of us seeking out and planting native species than in the past. Our gardens — and our social media feeds — are brimming with beautiful Australian native blooms.

But not all Australian native species belong in all Australian environments. In fact, many have become pests in places far from their original homes.

They can crowd out other native endemic species, affect the local balance of insects and other animals, wreck soils and even increase fire risk.

Here are three Australian native plants that have become invasive species after ending up in places they don’t belong.

Sydney golden wattle (Acacia longifolia subspecies longifolia)

Originally extending from East Gippsland in Victoria up about as far as Brisbane in Queensland, this species is undoubtedly photogenic. It’s also an invasive weed in parts of Victoria, South Australia and Western Australia.

It was spread across the nation by well-meaning gardeners who saw it as a charming ornamental plant. However, its seeds made their way into the wild and took off — it’s what’s known in my field as “a garden escapee”.

Like many weeds, this species can capitalise on a natural disaster; after fire it can send out shoots from its base. Acacias are often one of the first species to sprout following a bushfire. They’re now completely dominant and spreading in many areas.

Sydney golden wattle is an invasive weed in other parts of Victoria, South Australia and Western Australia.
Gill Armstrong, Author provided

Seeds of Sydney golden wattle can last in the soil for many decades, long after the parent plants have died. The heat from a fire cracks the hard seed coat, allowing water to enter and germination to take off.

In the Grampians, in Victoria, Sydney golden wattle is causing terrible soil problems. Many native plants endemic to this area don’t like high levels of soil nitrogen, but Acacia longifolia subsp. longifolia is a nitrogen-fixing plant.

Acacia longifolia subsp. longifolia has quite long, thin seed pods.
Acacia longifolia subsp. longifolia has quite long, thin seed pods.
Gill Armstrong, Author provided

In other words, it increases the nitrogen in the soil and changes the soil nutrient status and even physical aspects of the soil. It can grow tall and produce a lot of foliage, which reduces the amount of light coming to the ground. That makes it harder for native species lower to the ground to survive.

This is a major challenge, especially in biodiversity-rich places like the Grampians.

Coast wattle (Acacia longifolia subspecies sophorae)

The blooms on Acacia longifolia subspecies sophorae (Coast wattle) look more or less the same as many other wattles, but the leaves are a bit shorter and stubbier.

Originally, Coast wattle occurred along the east coast from western Victoria — up about as far as Brisbane and down south as far as Tasmania (where Sydney golden wattle did not occur naturally).

_Acacia longifolia subsp. sophorae_, also known as 'Coastal Wattle', has shorter, stubby leaves.
Acacia longifolia subsp. sophorae, also known as ‘Coastal Wattle’, has shorter, stubby leaves.
Tatters ✾/Flickr, CC BY

It was originally restricted to sandy sites at the top of beaches but has been deliberately planted as a “sand-binder” in other sites. It’s also naturally spread into heathlands inland of the beaches and is now causing huge problems around our coasts.

Like the earlier example, it dominates local ecosystems and displaces native species endemic to the area (particularly in our species-rich heathlands), which affects local insect habitats. It is also now modifying natural sand dune patterns.

It is increasing fire risk by changing heathland plant profiles from mostly short shrubs of limited bulk to tall, dense shrublands with much higher fuel levels.

Coast teatree (Leptospermum laevigatum)

As with Coast wattle, Coast teatree was formerly restricted to a narrow strip on sandy soils just above the beaches of south-eastern Australia. But it has now spread into nearby heathlands and woodlands. It’s even reached as far as Western Australia.

Coast teatree, Leptospermum laevigatum, is now an invasive species in some areas. It has small white flowers.
Coast teatree, Leptospermum laevigatum, is now an invasive species in some areas.
Flickr/Margaret Donald, CC BY

This teatree plant is now considered an invasive species in parts of Victoria and South Australia.

Although the mature plants are usually killed by fire, the seeds are abundant and very good at surviving; they pop out of their capsules after fires.

Coast teatree
Coast teatree produces a lot of seeds.
Dr David Chael, Author provided

They are high-density plants that burn quickly in a fire. They are very quick to take over and push out endemic species.

For example, parts of the Wilson’s Prom National Park in Victoria, which was originally a Banksia woodland, have now been converted almost to a teatree monoculture. It is very sad.

A call to action

Authorities are trying their best to keep these and other native invasive species under control, but in some cases things may never go back to the way they were. Sometimes, the best you can hope for is just to strike a balance between native and invasive species.

When you do landcare restoration work or home gardening, I urge you to look up the plant history and see if the species you’re thinking of planting is listed as one that might cause problems in future.

When you go to purchase from a nursery or plant centre, be cautious. Think twice before you bring something into your garden. Too many species have “jumped the garden fence” and now cost us a great deal in control efforts and in native species loss.

Lots of apps, such as PlantNet, can help you identify plants and see what is native to your area.

Australia has spent billions trying to control invasive species and environmental weeds. Anything you can do to help is a bonus.The Conversation

Singarayer Florentine, Professor (Restoration Ecologist), Federation University Australia

This article is republished from The Conversation under a Creative Commons license. Read the original article.


How machine learning is helping us fine-tune climate models to reach unprecedented detail


Navid Constantinou, Australian National UniversityFrom movie suggestions to self-driving vehicles, machine learning has revolutionised modern life. Experts are now using it to help solve one of humanity’s biggest problems: climate change.

With machine learning, we can use our abundance of historical climate data and observations to improve predictions of Earth’s future climate. And these predictions will have a major role in lessening our climate impact in the years ahead.

Read more:
Satellites reveal ocean currents are getting stronger, with potentially significant implications for climate change

What is machine learning?

Machine learning is a branch of artificial intelligence. While it has become something of a buzzword, it is essentially a process of extracting patterns from data.

Machine learning algorithms use available data sets to develop a model. This model can then make predictions based on new data that were not part of the original data set.

Going back to our climate problem, there are two main approaches by which machine learning can help us further our understanding of climate: observations and modelling.

In recent years, the amount of available data from observation and climate models has grown exponentially. It’s impossible for humans to go through it all. Fortunately, machines can do that for us.

AI and computers can greatly aid efforts to create accurate climate models for the future.
Josué Martínez-Moreno

Observations from space

Satellites are continuously monitoring the ocean’s surface, giving scientists useful insight into how ocean flows are changing.

NASA’s Surface Water and Ocean Topography (SWOT) satellite mission — scheduled to launch late next year — aims to observe the ocean surface in unprecedented detail compared with current satellites.

But a satellite can’t observe the entire ocean at once. It can only see the portion of ocean beneath it. And the SWOT satellite will need 21 days to go over every point around the globe.

This diagram shows the area covered by the SWOT satellite after three days in orbit. Although SWOT allows high-accuracy measurements, neighbouring areas in the ocean are not sampled as frequently.
C. Ubelmann/CLS

Is there a way to fill in the missing data, so we can have a complete global picture of the ocean’s surface at any given moment?

This is where machine learning comes in. Machine learning algorithms can use data retrieved by the SWOT satellite to predict the missing data between each SWOT revolution.

An artist’s impression of the SWOT satellite.

Obstacles in climate modelling

Observations inform us of the present. However, to predict future climate we must rely on comprehensive climate models.

The latest IPCC climate report was informed by climate projections from various research groups across the world. These researchers ran a multitude of climate models representing different emissions scenarios that yielded projections hundreds of years into the future.

Read more:
Climate change has already hit Australia. Unless we act now, a hotter, drier and more dangerous future awaits, IPCC warns

To model the climate, computers overlay a computational grid on the oceans, atmosphere and land. Then, by starting with the climate of today, they can solve the equations of fluid and heat motion within each box of this grid to model how the climate will evolve in the future.

The size of each box in the grid is what we call the “resolution” of the model. The smaller the box’s size is, the finer the flow details the model can capture.

But running climate models that project forward hundreds of years brings even the most powerful supercomputers to their knees. Thus, we’re currently forced to run these models at a coarse resolution. In fact, it’s sometimes so coarse that the flow looks nothing like real life.

For example, ocean models used for climate projections typically look like the one on the left below. But in reality, ocean flow looks much more like the image on the right.

Here you can see ocean surface currents modelled at two different resolutions. On the left is a model akin to those typically used for climate projections. The model on the right is much more accurate and realistic, but is unfortunately too computationally restrictive to be used for climate projections.
COSIMA, Author provided

Unfortunately, we currently don’t have the computational power needed to run high-resolution and realistic climate models for climate projections.

Climate scientists are trying to find ways to incorporate the effects of the fine, small-scale turbulent motions in the above-right image into the coarse-resolution climate model on the left.

If we can do this, we can generate climate projections that are more accurate, yet still computationally feasible. This is what we refer to as “parameterisation” — the holy grail of climate modelling.

Simply, this is when we can achieve a model that doesn’t necessarily include all the smaller-scale complex flow features (which require huge amounts of processing power) — but which can still integrate their effects into the overall model in a simpler and cheaper way.

A clearer picture

Some parameterisations already exist in coarse-resolution models, but often don’t do a good job integrating the smaller-scale flow features in an effective way.

Machine learning algorithms can use output from realistic, high-resolution climate models (like the one on the right above) to develop far more accurate parameterisations.

As our computational capacity grows — along with our climate data — we’ll be able to engage increasingly sophisticated machine learning algorithms to sift through this information and deliver improved climate models and projections.

An interactive model of NASA’s SWOT satellite. The Conversation

Navid Constantinou, ARC DECRA Research Fellow, Australian National University

This article is republished from The Conversation under a Creative Commons license. Read the original article.