Physiologically based demographic model/GIS analyses of thirteen invasive species in Africa: why the biology matters
Bibliographic record
Abstract
Abstract Globally, research and policy groups often lack the expertise to develop appropriate models to analyze agroecological and invasive species problems holistically to inform management and quarantine policy development under extant and climate change over wide geographic landscapes. Off-the-shelf species distribution models (SDM) correlate weather and other variables to records of species presence and have become the mainstay for predicting the geographic distribution and favorability of invasive species (Elith 2017). However, SDM analyses lack the capacity to examine the underpinning dynamics of agroecosystems required to inform policy and develop management strategies. We propose that age-structured physiologically based demographic models (PBDMs) can solve important aspects of this challenge as they can be used to examine prospectively species dynamics locally as well as their potential geographic distribution and relative abundance across vast areas independent of presence records. PBDMs fall under the ambit of time-varying life tables (TVLTs; cf. Gilbert et al . 1976) and capture the weather driven biology, dynamics, and interactions of species, and can be used to examine the system from the perspective of any of the interacting species. Here, we use the PBDM structure to examine the dynamics across Africa of thirteen invasive species from various taxa having diverse biology and trophic interactions (see Gutierrez 1996, Gutierrez and Ponti 2013a). PBDMs are perceived to be difficult to develop, hence the raison d’être is to show this is not the case and illustrate their utility invasive and endemic agricultural and medical/veterinary pest species at the local and the large geographic scale of Africa. We note that PBDMs provide a structure for continued model improvements. The development of open access software is proposed to facilitate PBDM development by non-experts emphasizing the crucial role of sound biological data on species responses to weather and to other species in a multi-trophic, interactions, and provide a guide for collecting the appropriate biological data. While the emphasis is on plant/arthropod interactions, models of diseases can be accommodated. The Supplemental Materials summarizes a large array of heritage PBDMs reported in the literature based on the methods outline herein, noting that the same model structure can be used to analyze and manage non pest species.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".