Abstract:Pollination network studies are based on pollinator surveys conducted on focal plants. This plant-centred approach provides insufficient information on flower visitation habits of rare pollinator species, which are the majority in pollinator communities. As a result, pollination networks contain very high proportions of pollinator species linked to a single plant species (extreme specialists), a pattern that contrasts with the widely accepted view that plant-pollinator interactions are mostly generalized. In t… Show more
“…Number of missing links was reduced by (i) extending sampling time to a full season and (ii) using both a phytocentric and a zoo-centric approach to monitor links, i.e. flower-/fruit-visit data and data on pollen on the body surface of flower visitors and seeds in faeces of frugivores, respectively ( [22]; the 'smoking gun' method sensu [23]). As in any biodiversity monitoring, accounting for sampling effects when monitoring interactions has long been recognized as a fundamental aspect [6], and this two-sided approach was recommended by Blü thgen [24]; also see [22,25] to reduce effects of observation bias.…”
Ecological networks are complexes of interacting species, but not all potential links among species are realized. Unobserved links are either missing or forbidden. Missing links exist, but require more sampling or alternative ways of detection to be verified. Forbidden links remain unobservable, irrespective of sampling effort. They are caused by linkage constraints. We studied one Arctic pollination network and two Mediterranean seed-dispersal networks. In the first, for example, we recorded flower-visit links for one full season, arranged data in an interaction matrix and got a connectance C of 15 per cent. Interaction accumulation curves documented our sampling of interactions through observation of visits to be robust. Then, we included data on pollen from the body surface of flower visitors as an additional link 'currency'. This resulted in 98 new links, missing from the visitation data. Thus, the combined visit-pollen matrix got an increased C of 20 per cent. For the three networks, C ranged from 20 to 52 per cent, and thus the percentage of unobserved links (100 2 C) was 48 to 80 per cent; these were assumed forbidden because of linkage constraints and not missing because of under-sampling. Phenological uncoupling (i.e. non-overlapping phenophases between interacting mutualists) is one kind of constraint, and it explained 22 to 28 per cent of all possible, but unobserved links. Increasing phenophase overlap between species increased link probability, but extensive overlaps were required to achieve a high probability. Other kinds of constraint, such as size mismatch and accessibility limitations, are briefly addressed.
“…Number of missing links was reduced by (i) extending sampling time to a full season and (ii) using both a phytocentric and a zoo-centric approach to monitor links, i.e. flower-/fruit-visit data and data on pollen on the body surface of flower visitors and seeds in faeces of frugivores, respectively ( [22]; the 'smoking gun' method sensu [23]). As in any biodiversity monitoring, accounting for sampling effects when monitoring interactions has long been recognized as a fundamental aspect [6], and this two-sided approach was recommended by Blü thgen [24]; also see [22,25] to reduce effects of observation bias.…”
Ecological networks are complexes of interacting species, but not all potential links among species are realized. Unobserved links are either missing or forbidden. Missing links exist, but require more sampling or alternative ways of detection to be verified. Forbidden links remain unobservable, irrespective of sampling effort. They are caused by linkage constraints. We studied one Arctic pollination network and two Mediterranean seed-dispersal networks. In the first, for example, we recorded flower-visit links for one full season, arranged data in an interaction matrix and got a connectance C of 15 per cent. Interaction accumulation curves documented our sampling of interactions through observation of visits to be robust. Then, we included data on pollen from the body surface of flower visitors as an additional link 'currency'. This resulted in 98 new links, missing from the visitation data. Thus, the combined visit-pollen matrix got an increased C of 20 per cent. For the three networks, C ranged from 20 to 52 per cent, and thus the percentage of unobserved links (100 2 C) was 48 to 80 per cent; these were assumed forbidden because of linkage constraints and not missing because of under-sampling. Phenological uncoupling (i.e. non-overlapping phenophases between interacting mutualists) is one kind of constraint, and it explained 22 to 28 per cent of all possible, but unobserved links. Increasing phenophase overlap between species increased link probability, but extensive overlaps were required to achieve a high probability. Other kinds of constraint, such as size mismatch and accessibility limitations, are briefly addressed.
“…The food sources exploited by bees can be identified by direct observation of the visiting bee at the flower [23,24] or by pollen analysis [14,25,26]. Pollen analysis enables quantification of the diversity and frequency of pollen grains found on the bodies of the bees [14,27], the nest [28,29], and/or feces of adult and immature bees [13].…”
The floral sources used by bees can be identified by analyzing pollen grains obtained from their bodies, feces, brood cells, or storage pots in the nests. In addition to data on resource availability, this information enables the investigation on the selection of food resource by bees. We assessed the foraging patterns of Scaptotrigona aff. depilis in an urbanized area with seasonal availability of food resources. The species visited a percentage of 36.60% of the available flora, suggesting that these bees are selective at spatiotemporal scale. When many types of resources were available, the workers concentrated their collection activities on a limited group of sources. In contrast, more plant species were exploited during periods of lower number of flowering plants. A monthly analysis of the foraging patterns of the studied colonies revealed that Syzygium cumini (88.86%), Mimosa sp.1 (80.23%), Schinus terebinthifolius (63.36%), and Eucalyptus citriodora (61.75%) were the most frequently used species and are therefore important for maintaining S. aff. depilis at the study area. These plants are close to the colonies and exhibit mass flowering. This study is one of few works to quantify natural resource availability and to analyze the effects of flowering seasonality on the selection of food sources by bees.
“…10,11 The same can be applied from the pollinator's perspective -that is, the range of plants a pollinator prefers relates to it being generalised or specialised, as discussed below. 10,12 In some extreme cases, both parties could co-evolve morphologically and behaviourally to allow only one-on-one plant-pollinator interactions, whereby the plant protects access to rewards for its specific pollinator -a feature of, amongst others, many genera in the Orchidaceae. 13 It has been argued that the formation of specific floral structures in plants is largely driven by means of natural selection from their respective pollinators.…”
Section: Evolution Of Plant-pollinator Interactionsmentioning
confidence: 99%
“…27 Oligolectic bees still visit flowers from plant taxa other than those from which pollen are collected for other resources, such as nectar and oils. 12 Floral choices of bee pollinators play an important role in the sustainability of a plant community. According to food web theory, the more complex http://www.sajs.co.za…”
Section: Generalist Versus Specialist Interactionsmentioning
Plant-pollinator interactions are essential for maintaining both pollinator and plant communities in native and agricultural environments. Animal-instigated pollination can be complex. Plants are usually visited by a number of different animal species, which in turn may visit flowers of several plant species. Therefore, the identification of the pollen carried by flower visitors is an essential first step in pollination biology. The skill and time required to identify pollen based on structure and morphology has been a major stumbling block in this field. Advances in the genetic analysis of DNA, using DNA barcoding, extracted directly from pollen offers an innovative alternative to traditional methods of pollen identification. This technique, which is reviewed in detail, can be used on pollen loads sampled from bees in the field and from specimens in historic collections. Here the importance of pollination, the role-players involved, their management and the evolution of their interactions, behaviour and morphology are reviewed -with a special focus on South African bees.
Significance:• Pollen metabarcoding will enable the identification of pollen for a multitude of uses, including agriculture, conservation and forensics.• Plant-pollinator interaction documentation through pollen identification gives a more certain record of a visitor being a pollinator rather than a flower visitor that could be a nectar gatherer.
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