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| Natura: | Preprint |
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2023
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| Accesso online: | https://arxiv.org/abs/2304.00654 |
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| _version_ | 1866909616214900736 |
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| author | Boschi, Martina Juozaitienė, Rūta Wit, Ernst-Jan Camiel |
| author_facet | Boschi, Martina Juozaitienė, Rūta Wit, Ernst-Jan Camiel |
| contents | Alien species refer to non-native species introduced by humans into an ecosystem, which can cause harm to the environment, economy, or human health. Although there is considerable literature on the subject, the presence of confounding factors has so far prevented a comprehensive picture of the relative importance of various drivers of such invasions. In this manuscript, we aim to develop and apply a general mixed additive relational event model to describe the pattern of global invasions of alien species. The diffusion of alien species can be regarded as a relational event, where the species -- the sender -- reaches a region -- the receiver -- at a specific time in history. We use the First Record Database, which contains all co-invasions by insects and plants between 1880 and 2005. A relational event model (REM) is employed to describe the underlying hazard of each species-region pair. Besides potentially time-varying, exogenous, and endogenous covariates, the mixed additive REM incorporates time-varying and random effects, allowing for taxa-specific baseline rates while accounting for the potential synergistic effect between plants and insects in the invasion process. Our efficient inference procedure relies on case-control sampling, yielding the same likelihood as that of a degenerate logistic regression. We propose fitting the mixed additive REM via a generalised additive model with random effects as 0-dimensional splines. The resulting computational efficiency means that complex models for large dynamic networks can be estimated in seconds on a standard computer. Furthermore, we present a framework for testing the goodness-of-fit of our mixed additive REM for the invasions by vascular plants and insects by means of cumulative martingale-residuals. Implementation is performed through the R package mgcv. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_00654 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Mixed additive modelling of global alien species co-invasions of plants and insects Boschi, Martina Juozaitienė, Rūta Wit, Ernst-Jan Camiel Applications Alien species refer to non-native species introduced by humans into an ecosystem, which can cause harm to the environment, economy, or human health. Although there is considerable literature on the subject, the presence of confounding factors has so far prevented a comprehensive picture of the relative importance of various drivers of such invasions. In this manuscript, we aim to develop and apply a general mixed additive relational event model to describe the pattern of global invasions of alien species. The diffusion of alien species can be regarded as a relational event, where the species -- the sender -- reaches a region -- the receiver -- at a specific time in history. We use the First Record Database, which contains all co-invasions by insects and plants between 1880 and 2005. A relational event model (REM) is employed to describe the underlying hazard of each species-region pair. Besides potentially time-varying, exogenous, and endogenous covariates, the mixed additive REM incorporates time-varying and random effects, allowing for taxa-specific baseline rates while accounting for the potential synergistic effect between plants and insects in the invasion process. Our efficient inference procedure relies on case-control sampling, yielding the same likelihood as that of a degenerate logistic regression. We propose fitting the mixed additive REM via a generalised additive model with random effects as 0-dimensional splines. The resulting computational efficiency means that complex models for large dynamic networks can be estimated in seconds on a standard computer. Furthermore, we present a framework for testing the goodness-of-fit of our mixed additive REM for the invasions by vascular plants and insects by means of cumulative martingale-residuals. Implementation is performed through the R package mgcv. |
| title | Mixed additive modelling of global alien species co-invasions of plants and insects |
| topic | Applications |
| url | https://arxiv.org/abs/2304.00654 |