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| Bouchet, Pj; Miller, Dl; Roberts, Jj; Mannocci, Laura; Harris, Cm; Thomas, L. |
| Forecasting the responses of biodiversity to global change has never been more important. However, many ecologists faced with limited sample sizes and shoestring budgets often resort to extrapolating predictive models beyond the range of their data to support management actions in data‐deficient contexts. This can lead to error‐prone inference that has the potential to misdirect conservation interventions and undermine decision‐making. Despite the perils associated with extrapolation, little guidance exists on the best way to identify it when it occurs, leaving users questioning how much credence they should place in model outputs. To address this, we present dsmextra, a new R package for measuring, summarising, and visualising extrapolation in... |
| Tipo: Text |
Palavras-chave: Cetaceans; Distance sampling; Ecological predictions; Extrapolation; Model transferability; R package; Spatial modelling; Wildlife surveys. |
| Ano: 2020 |
URL: https://archimer.ifremer.fr/doc/00643/75485/76332.pdf |
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| Mengel, Friederike; Sciubba, Emanuela. |
| We study extrapolation between games in a laboratory experiment. Participants in our experiment first play either the dominance solvable guessing game or a Coordination version of the guessing game for five rounds. Afterwards they play a 3x3 normal form game for ten rounds with random matching which is either a game solvable through iterated elimination of dominated strategies (IEDS), a pure Coordination game or a Coordination game with pareto ranked equilibria. We find strong evidence that participants do extrapolate between games. Playing a strategically different game hurts compared to the control treatment where no guessing game is played before and in fact impedes convergence to Nash equilibrium in both the 3x3 IEDS and the Coordination games. Playing... |
| Tipo: Working or Discussion Paper |
Palavras-chave: Game Theory; Learning; Extrapolation; Research Methods/ Statistical Methods; C72; C91. |
| Ano: 2010 |
URL: http://purl.umn.edu/98475 |
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| Peron, Clara; Authier, Matthieu; Gremillet, David. |
| Aim Species distribution models (SDMs) are statistical tools aiming at mapping and predicting species distributions across landscapes. Data acquisition being limited in space and time, SDM are commonly used to predict species distribution in unsampled areas or years, with the expectation that modelled habitat-species relationships will hold across spatial or temporal contexts (i.e., model transferability). This key aspect of habitat modelling has major implications for spatial management, yet it has received limited attention, especially in the dynamic marine realm. Our aims were to test geographical and temporal habitat model transferability and to make recommendations for future population-scale habitat modelling. Location Methods Two contrasted regions... |
| Tipo: Text |
Palavras-chave: Biologging; Central-place foragers; Extrapolation; Habitat modelling; Seabirds; Transferability. |
| Ano: 2018 |
URL: https://archimer.ifremer.fr/doc/00470/58174/75133.pdf |
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