Impacts of deforestation on plant-pollinator networks assessed using an agent based model

Newton, Adrian C.; Bôscolo, Danilo; Ferreira, Patrícia Alves; Lopes, Luciano Elsinor; Evans, Paul M.

doi:10.1371/journal.pone.0209406

Cited by 15 publications

(9 citation statements)

References 56 publications

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“…We saw that networks become more nested, but simpler, in landscapes with less remaining forest, with generalist species being relatively more frequent and playing a more critical role in the network, since specialists interact mostly with these generalists (Ferreira et al 2015;Newton et al 2018). In landscapes with less forest, the most sensitive species have already disappeared, and the resilient or resistant species (usually the most generalist and frequent) tend to remain and interact more frequently among themselves.…”

Section: Discussionmentioning

confidence: 95%

Forest and connectivity loss simplify tropical pollination networks

et al. 2020

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Mutualistic interactions between plants and pollinators play an essential role in the organization and persistence of biodiversity. The structure of interaction networks mediates the resilience of local communities and ecosystem functioning to environmental changes. Hence, network structure conservation may be more critical for maintaining biodiversity and ecological services than the preservation of isolated species in changing landscapes. Here, we intensively surveyed seven 36 km 2 landscapes to empirically investigate the effects of forest loss and landscape configuration on the structure of plantpollinator networks in understory vegetation of Brazilian Atlantic Forest. Our results indicate that forest loss and isolation affect the structure of the plant-pollinator networks, which were smaller in deforested landscapes, and less specialized as patch isolation increased. Lower nestedness and degree of specialization (Hʹ 2 ) indicated that the remaining plant and bee species tend to be generalists, and many of the expected specialized interactions in the network were already lost. Because generalist species generate a cohesive interaction core in these networks, these simplified networks might be resistant to loss of peripheral species, but may be susceptible to the extinction of the most generalist species. We suggest that such a network pattern is an outcome of landscapes with a few remaining isolated patches of natural habitat. Our results add a new perspective to studies of plant-pollinator networks in fragmented landscapes, showing that those interaction networks might also be used to indicate how changes in natural habitat affect biodiversity and biotic interactions.

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Section: Discussionmentioning

confidence: 95%

Forest and connectivity loss simplify tropical pollination networks

et al. 2020

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“…modules (Dupont et al ., 2009). Network statistics have been used as patterns in modelling plant–pollinator networks (Newton et al ., 2018; Chudzinska et al ., 2020).…”

Section: What Kinds Of Patterns Are Useful?mentioning

confidence: 99%

From theory to practice in pattern‐oriented modelling: identifying and using empirical patterns in predictive models

et al. 2021

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To robustly predict the effects of disturbance and ecosystem changes on species, it is necessary to produce structurally realistic models with high predictive power and flexibility. To ensure that these models reflect the natural conditions necessary for reliable prediction, models must be informed and tested using relevant empirical observations. Patternoriented modelling (POM) offers a systematic framework for employing empirical patterns throughout the modelling process and has been coupled with complex systems modelling, such as in agent-based models (ABMs). However, while the production of ABMs has been rising rapidly, the explicit use of POM has not increased. Challenges with identifying patterns and an absence of specific guidelines on how to implement empirical observations may limit the accessibility of POM and lead to the production of models which lack a systematic consideration of reality. This review serves to provide guidance on how to identify and apply patterns following a POM approach in ABMs (POM-ABMs), specifically addressing: where in the ecological hierarchy can we find patterns; what kinds of patterns are useful; how should simulations and observations be compared; and when in the modelling cycle are patterns used? The guidance and examples provided herein are intended to encourage the application of POM and inspire efficient identification and implementation of patterns for both new and experienced modellers alike. Additionally, by generalising patterns found especially useful for POM-ABM development, these guidelines provide practical help for the identification of data gaps and guide the collection of observations useful for the development and verification of predictive models. Improving the accessibility and explicitness of POM could facilitate the production of robust and structurally realistic models in the ecological community, contributing to the advancement of predictive ecology at large.

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“…Individual‐based models can circumvent some of the limitations faced by field studies because they allow the tracking of individual pollinators and their floral visitation through space and time (Newton et al, 2018). Individual‐based models permit recording the time and location of all events taking place during a simulation, together with the identity of the individuals (e.g.…”

Section: Introductionmentioning

confidence: 99%

Landscape composition and pollinator traits interact to influence pollination success in an individual‐based model

et al. 2023

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1. The arrangement of plant species within a landscape influences pollination via changes in pollinator movement trajectories and plant-pollinator encounter rates.Yet the combined effects of landscape composition and pollinator traits (especially specialisation) on pollination success remain hard to quantify empirically.2. We used an individual-based model to explore how landscape and pollinator specialisation (degree) interact to influence pollination. We modelled variation in the landscape by generating gradients of plant species intermixing-from no mixing to complete intermixing. Furthermore, we varied the level of pollinator specialisation by simulating plant-pollinator (six to eight species) networks of different connectance. We then compared the impacts of these drivers on three proxies for pollination: visitation rate, number of consecutive visits to the focal plant species and expected number of plants pollinated.3. We found that the spatial arrangements of plants and pollinator degree interact to determine pollination success, and that the influence of these drivers on pollination depends on how pollination is estimated. For most pollinators, visitation rate increases in more plant mixed landscapes. Compared to the two more functional measures of pollination, visitation rate overestimates pollination service. This is particularly severe in landscapes with high plant intermixing and for generalist pollinators. Interestingly, visitation rate is less influenced by pollinator traits (pollinator degree and body size) than are the two functional metrics, likely because 'visitation rate' ignores the order in which pollinators visit plants. However, the visitation sequence order is crucial for the expected number of plants pollinated, since only prior visits to conspecific individuals can contribute to pollination. We | 2057Functional Ecology KORTSCH et al.

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Impacts of deforestation on plant-pollinator networks assessed using an agent based model

Cited by 15 publications

References 56 publications

Forest and connectivity loss simplify tropical pollination networks

Forest and connectivity loss simplify tropical pollination networks

From theory to practice in pattern‐oriented modelling: identifying and using empirical patterns in predictive models

Landscape composition and pollinator traits interact to influence pollination success in an individual‐based model

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