Does dispersal capacity matter for freshwater biodiversity under climate change?
Bibliographic record
Abstract
Summary Freshwater ecosystems appear to be sensitive to even minor climatic shifts, and the dendritic nature of rivers as well as patchy distribution of habitats within the terrestrial landscape could limit the ability of species to track suitable climate conditions. Although the importance of dispersal is recognised in theory, there is great uncertainty when quantifying the capacity of species to shift their distributions in response to climate change. The influence of dispersal capacity on species’ vulnerability to climate change was assessed, using the modelled projections of 527 freshwater species in New South Wales (NSW), Australia. Species’ future ranges were calculated by iteratively identifying colonisation of accessible habitats and loss of suitable habitats within network models. The accessibility of new habitats was based on a given dispersal mode (aquatic, semi‐terrestrial and aerial). The relative impact of dispersal parameters on projected range were evaluated alongside other known sources of uncertainty (climate and emissions scenarios, modelling algorithm and biological group), analysed collectively in a generalised additive mixed‐model, and spatially to locate regions of NSW where projections are associated with the most uncertainty. Our simulations (1.4 million scenario combinations) suggest at least a third of species will lose more than half their range under climate change. Nevertheless, we emphasise the broad uncertainty that any average encapsulates. Dispersal capacity only had a minor impact on projected range shifts relative to other modelling assumptions but the network‐pathways and maps of uncertainty have value for conservation planning at large scales. Projected range losses initially decreased rapidly as dispersal rates increased but the benefits are reduced above 2–3 km year −1 . Taxa restricted to dispersal within the stream network (aquatic) were more vulnerable to climate change than taxa with semi‐terrestrial or aerial dispersal and maps of variation due to dispersal mode and rate indicate where habitat connectivity would be most beneficial. This study demonstrates the breadth of uncertainties that challenge plans for improving ecosystem adaptation under climate change and highlights where in the landscape those differences were consistent. We emphasise the need for freshwater conservation studies to be ecologically representative, to focus on broad‐scale connectivity for taxa that can move between catchments, and an accessible network of refugia for taxa with more limited dispersal.
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How this classification was reachedexpand
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.179 | 0.013 |
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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".