Non-indigenous fishes and their role in freshwater fish imperilment
Bibliographic record
Abstract
INTRODUCTION The current state of freshwater biodiversity, and that of freshwater fishes in particular, can only be described as dismal (Dudgeon et al. , 2006; Burkhead, 2012; Chapters 1 and 2). Freshwater extinction rates have been estimated to be around 1000× above background extinction rates (Ricciardi & Rasmussen, 1999; Burkhead, 2012). Using North America as an example, a recent American Fisheries Society assessment indicates that 39% of North American freshwater fish taxa are imperilled: 230 are vulnerable, 190 are threatened, 280 are endangered and 61 are extinct or extirpated (Jelks et al. , 2008). Global assessments paint the same general picture (www.iucn.org). What are the factors ultimately responsible for this situation? The objective of this chapter is to examine the role of non-indigenous fishes (hereafter NIF) in the decline and imperilment of native freshwater fishes. We provide a short primer to clarify a few key terms relating to invasion biology Box 8.1). A biological invasion should not be viewed as an event, but rather a process comprised of several successive stages (Figure 8.1). The non-indigenous species introduction process begins with uptake of individuals, transport to a new area ( transport outside of native range ), and subsequently release into the wild ( introduction to the wild ). These released individuals may then establish a self-sustaining population in the new area ( establishment ), and an established population may increase in abundance and expand its geographic range ( spread ). Typically it is only when a species becomes abundant and/or widespread that it is perceived to cause ecological or economic harm ( impact ), such that the species is considered invasive (Lockwood et al. , 2013). Human values and perceptions greatly influence how and whether a non-indigenous species is viewed as having undesirable consequences. Alternatively, an introduced species population may remain small and localised in its distribution, with few or no tangible ecological or economic effects. To pass through the invasion stages shown in Figure 8.1, a species must overcome a suite of barriers to advancement. At each stage, there are ecological, environmental and stochastic forces that can either facilitate or hinder transition to subsequent stages. These may include the characteristics of the species (e.g. reproductive rate, physiological tolerance), environmental conditions (e.g. temperature or salinity match) and the nature of the introduction event (e.g. timing of introduction, number of individuals moved).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".