Understanding environmental change – before it is too late!
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We climbed out of the bus and stood on the bridge: a group of scientists from Canada, England, Spain, Chile, Japan, China, Australia and England, and our host from the Dutch Ministry of Environment, on a field trip in The Netherlands during the World Congress of the International Association for Landscape Ecology. There in front of us, flowing under the bridge, was a new stream – a stream created by people. Yes, a new stream, not the restoration of an existing or degraded stream! A Grey Heron stood in a pool, a Eurasian Coot was out on the water and somewhere nearby a frog jumped. It looked like how a stream should look: a curvilinear shape, gradual sloping edges, some pools away from the main channel, and a few stands of reeds. But mounds of fresh earth provided distinctive evidence that a bulldozer had recently been at work, shaping and sculpting the pathway of the new watercourse. Why was this stream being created? It is one example of a water management project undertaken as part of a large European programme termed Joint Approach for Managing Flooding (JAF). In the Twente region of eastern Netherlands, the river Regge had been cut off from a large part of its catchment by past management in order to speed up drainage via other channels. As a consequence, lower-lying areas have suffered flooding while in summer, large areas of the catchment are too dry. The purpose of this new stream, 13 km in length, is to reconnect the Regge with the part of its catchment from which it was cut off. It is called de Doorbraak (the breakthrough), and it is intended to be a ‘break through’ for water management, agriculture and nature. It will provide clean water for intensive agriculture in the region, but is also designed to have high nature conservation values and provide ecological connectivity through the landscape. Such a planned multifunctional stream does not come cheaply: the cost was 240 million, we were told, with much of that being to purchase the highly productive farmland through which it passes. The Dutch are leaders in restoration of land and water resources, and I was impressed by what I saw here. But after thinking further about this, I asked myself a different question – why is this expensive and complex restoration necessary? Why did it get to this stage? Restoration, by definition, is about restoring structure and function when the original function of a system has been degraded or lost. Here, stream restoration is being undertaken because the natural drainage of the catchment has been modified by past management. Restoration of connectivity in terrestrial environments, another area in which the Dutch are leaders, involves the installation of overpasses, underpasses and tunnels (‘ecoducts’) for various wildlife species, because the landscape has been carved up by roads and freeways. Although we applaud the Dutch for their skills and leadership in landscape restoration, is this the kind of leadership we wish to emulate here in the Australasian region? Certainly, restoration is an important and challenging task here too, but even more important is to recognize the changes that lead to degradation of ecosystem function before it is too late, before ecological function is entirely lost and expensive restoration is needed. In many parts of Australasia, we still have this opportunity and the opportunity for a different kind of leadership role – to be world leaders in managing dynamic natural ecosystems, avoiding degradation that requires expensive restoration. A first step is to recognize and value the natural environments and flora and fauna that we have. This highlights the role of survey, inventory and mapping – what do we have, where does it occur, how much is there, who is managing it, and what condition is it in? Natural resource management agencies in Australia have made much progress in this area in the last 20 years, particularly with the advent of geographical information system (GIS) technology. Many data sets have been compiled that document the distribution and spatial pattern of environmental attributes such as soils, vegetation types, streams and wetlands; and management features such as land tenure, management zones and reserve boundaries. Some progress is also being made in assessing and mapping the condition of environmental attributes. A more difficult but critical task is to understand how ecosystems change through time. More than ever, natural environments are exposed to a barrage of impacts: land clearing, introduced predators, invasive weeds, altered flooding regimes, different fire regimes, grazing by hard-hooved stock, expanding urban environments, different forms of agriculture, and so on. Unless we understand the nature and rate of change that arises from such processes, we will not know how close an ecosystem is to breakdown and loss of function – until it is too late. Two types of question are important. First, what are the consequences of these impacts, in terms of changes in species composition, changes in distribution and abundance of species, changes in interactions between species, or change in other ecological processes? Second, what is the rate and trajectory of such changes? Is there a time lag before change is evident or occurs fully? Is there a critical stage, such as a threshold, at which the rate of change accelerates and the system shifts into another state from which recovery is difficult or impossible – and if so, when is it? Of course, translating this knowledge into effective management of natural environments is a pivotal stage. Knowing when to intervene, where to intervene and how to intervene to ensure that ecosystems continue to function in a healthy manner is the challenge for land managers. Getting it right is not easy, and there is much to learn. A key element is the feedback loop between implementing management actions, evaluating and learning from the outcomes, and planning the next stage of management. Again, this highlights the critical role of understanding how ecosystems change through time, in this case to gauge the effectiveness of ecological management rather than disruption from detrimental impacts. What kind of legacy will our generation leave for future generations? Will our counterparts in 50 years time be occupied mainly in ecological management, or in ecological restoration? Will their priority be fine-tuning the ongoing management of functioning ecosystems, based on a sound understanding of ecosystem dynamics developed over 50 years of research, monitoring and adaptive management? Or will the legacy of our time be the need for a large cohort of restoration ecologists engaged in designing and recreating synthetic ecosystems in expensive restoration programmes?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it