The role of web sharing, species recognition and host-plant defence in interspecific competition between two herbivorous mite species

When competing with indigenous species, invasive species face a problem, because they typically start with a few colonizers. Evidently, some species succeeded, begging an answer to the question how they invade. Here, we investigate how the invasive spider mite Tetranychus evansi interacts with the indigenous species T. urticae when sharing the solanaceous host plant tomato: do they choose to live together or to avoid each other’s colonies? Both species spin protective, silken webs on the leaf surfaces, under which they live in groups of con- and possibly heterospecifics. In Spain, T. evansi invaded the non-crop field where native Tetranychus species including T. urticae dominated. Moreover, T. evansi outcompetes T. urticae when released together on a tomato plant. However, molecular plant studies suggest that T. urticae benefits from the local down-regulation of tomato plant defences by T. evansi, whereas T. evansi suffers from the induction of these defences by T. urticae. Therefore, we hypothesize that T. evansi avoids leaves infested with T. urticae whereas T. urticae prefers leaves infested by T. evansi. Using wild-type tomato and a mutant lacking jasmonate-mediated anti-herbivore defences, we tested the hypothesis and found that T. evansi avoided sharing webs with T. urticae in favour of a web with conspecifics, whereas T. urticae more frequently chose to share webs with T. evansi than with conspecifics. Also, T. evansi shows higher aggregation on a tomato plant than T. urticae, irrespective of whether the mites occur on the plant together or not

On distinguishing between canonical tRNA genes and tRNA gene fragments in prokaryotes

Automated genome annotation is essential for extracting biological information from sequence data. The identification and annotation of tRNA genes is frequently performed by the software package tRNAscan-SE, the output of which is listed for selected genomes in the Genomic tRNA database (GtRNAdb). Here, we highlight a pervasive error in prokaryotic tRNA gene sets on GtRNAdb: the miscategorization of partial, non-canonical tRNA genes as standard, canonical tRNA genes. Firstly, we demonstrate the issue using the tRNA gene sets of 20 organisms from the archaeal taxon Thermococcaceae. According to GtRNAdb, these organisms collectively deviate from the expected set of tRNA genes in 15 instances, including the listing of eleven putative canonical tRNA genes. However, after detailed manual annotation, only one of these eleven remains; the others are either partial, noncanonical tRNA genes resulting from the integration of genetic elements or CRISPR-Cas activity (seven instances), or attributable to ambiguities in input sequences (three instances). Secondly, we show that similar examples of the mis-categorization of predicted tRNA sequences occur throughout the prokaryotic sections of GtRNAdb. While both canonical and non-canonical prokaryotic tRNA gene sequences identified by tRNAscan-SE are biologically interesting, the challenge of reliably distinguishing between them remains. We recommend employing a combination of (i) screening input sequences for the genetic elements typically associated with non-canonical tRNA genes, and ambiguities, (ii) activating the tRNAscan-SE automated pseudogene detection function, and (iii) scrutinizing predicted tRNA genes with low isotype scores. These measures greatly reduce manual annotation efforts, and lead to improved prokaryotic tRNA gene set predictions

On distinguishing between canonical tRNA genes and tRNA gene fragments in prokaryotes

Automated genome annotation is essential for extracting biological information from sequence data. The identification and annotation of tRNA genes is frequently performed by the software package tRNAscan-SE, the output of which is listed for selected genomes in the Genomic tRNA database (GtRNAdb). Here, we highlight a pervasive error in prokaryotic tRNA gene sets on GtRNAdb: the miscategorization of partial, non-canonical tRNA genes as standard, canonical tRNA genes. Firstly, we demonstrate the issue using the tRNA gene sets of 20 organisms from the archaeal taxon Thermococcaceae. According to GtRNAdb, these organisms collectively deviate from the expected set of tRNA genes in 15 instances, including the listing of eleven putative canonical tRNA genes. However, after detailed manual annotation, only one of these eleven remains; the others are either partial, noncanonical tRNA genes resulting from the integration of genetic elements or CRISPR-Cas activity (seven instances), or attributable to ambiguities in input sequences (three instances). Secondly, we show that similar examples of the mis-categorization of predicted tRNA sequences occur throughout the prokaryotic sections of GtRNAdb. While both canonical and non-canonical prokaryotic tRNA gene sequences identified by tRNAscan-SE are biologically interesting, the challenge of reliably distinguishing between them remains. We recommend employing a combination of (i) screening input sequences for the genetic elements typically associated with non-canonical tRNA genes, and ambiguities, (ii) activating the tRNAscan-SE automated pseudogene detection function, and (iii) scrutinizing predicted tRNA genes with low isotype scores. These measures greatly reduce manual annotation efforts, and lead to improved prokaryotic tRNA gene set predictions

On distinguishing between canonical tRNA genes and tRNA gene fragments in prokaryotes

Automated genome annotation is essential for extracting biological information from sequence data. The identification and annotation of tRNA genes is frequently performed by the software package tRNAscan-SE, the output of which is listed for selected genomes in the Genomic tRNA database (GtRNAdb). Here, we highlight a pervasive error in prokaryotic tRNA gene sets on GtRNAdb: the mis-categorization of partial, non-canonical tRNA genes as standard, canonical tRNA genes. Firstly, we demonstrate the issue using the tRNA gene sets of 20 organisms from the archaeal taxon Thermococcaceae. According to GtRNAdb, these organisms collectively deviate from the expected set of tRNA genes in 15 instances, including the listing of eleven putative canonical tRNA genes. However, after detailed manual annotation, only one of these eleven remains; the others are either partial, non-canonical tRNA genes resulting from the integration of genetic elements or CRISPR-Cas activity (seven instances), or attributable to ambiguities in input sequences (three instances). Secondly, we show that similar examples of the mis-categorization of predicted tRNA sequences occur throughout the prokaryotic sections of GtRNAdb. While both canonical and non-canonical prokaryotic tRNA gene sequences identified by tRNAscan-SE are biologically interesting, the challenge of reliably distinguishing between them remains. We recommend employing a combination of (i) screening input sequences for the genetic elements typically associated with non-canonical tRNA genes, and ambiguities, (ii) activating the tRNAscan-SE automated pseudogene detection function, and (iii) scrutinizing predicted tRNA genes with low isotype scores. These measures greatly reduce manual annotation efforts, and lead to improved prokaryotic tRNA gene set predictions

Response of Predatory Mites to a Herbivore-Induced Plant Volatile: Genetic Variation for Context-Dependent Behaviour

Plants infested with herbivores release specific volatile compounds that are known to recruit natural enemies. The response of natural enemies to these volatiles may be either learned or genetically determined. We asked whether there is genetic variation in the response of the predatory mite Phytoseiulus persimilis to methyl salicylate (MeSa). MeSa is a volatile compound consistently produced by plants being attacked by the two-spotted spider mite, the prey of P. persimilis. We predicted that predators express genetically determined responses during long-distance migration where previously learned associations may have less value. Additionally, we asked whether these responses depend on odors from uninfested plants as a background to MeSa. To infer a genetic basis, we analyzed the variation in response to MeSa among iso-female lines of P. persimilis by using choice-tests that involved either (1) MeSa presented as a single compound or (2) MeSa with background-odor from uninfested lima bean plants. These tests were conducted for starved and satiated predators, i.e., two physiological states, one that approximates migration and another that mimics local patch exploration. We found variation among iso-female lines in the responses to MeSa, thus showing genetic variation for this behavior. The variation was more pronounced in the starved predators, thus indicating that P. persimilis relies on innate preferences when migrating. Background volatiles of uninfested plants changed the predators’ responses to MeSa in a manner that depended on physiological state and iso-female line. Thus, it is possible to select for context-dependent behavioral responses of natural enemies to plant volatiles

Alternative phenotypes of male mating behaviour in the two-spotted spider mite

Severe intraspecific competition for mates selects for aggressive individuals but may also lead to the evolution of alternative phenotypes that do not act aggressively, yet manage to acquire matings. The two-spotted spider mite, Tetranychus urticae, shows male mate-guarding behaviour and male–male combat for available females. This may provide opportunity for weaker males to avoid fighting by adopting alternative mating behaviour such as sneaker or satellite tactics as observed in other animals. We investigated male precopulatory behaviour in the two-spotted spider mite by means of video-techniques and found three types of male mating behaviour: territorial, sneaker and opportunistic. Territorial and sneaker males associate with female teleiochrysales and spend much time guarding them. Territorial males are easily disturbed by rival males and engage themselves in fights with them. However, sneaker males are not at all disturbed by rival males, never engage in fights and, strikingly, never face attack by territorial males. Opportunistic males wander around in search of females that are in the teleiochrysalis stage but very close to or at emergence. To quickly classify any given mate-guarding male as territorial or sneaker we developed a method based on the instantaneous response of males to disturbance by a live male mounted on top of a brush. We tested this method against the response of the same males to natural disturbance by two or three other males. Because this method proved to be successful, we used it to collect territorial and sneaker males, and subjected them to morphological analysis to assess whether the various behavioural phenotypes are associated with different morphological characters. However, we found no statistical differences between territorial and sneaker males, concerning the length of the first legs, the stylets, the pedipalps or the body. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10493-013-9673-y) contains supplementary material, which is available to authorized users

UvA-DARE (Digital Academic Repository) The economics of altruistic punishment and the maintenance of cooperation The economics of altruistic punishment and the maintenance of cooperation

Explaining the evolution and maintenance of cooperation among unrelated individuals is one of the fundamental problems in biology and the social sciences. Recent findings suggest that altruistic punishment is an important mechanism maintaining cooperation among humans. We experimentally explore the boundaries of altruistic punishment to maintain cooperation by varying both the cost and the impact of punishment, using an exceptionally extensive subject pool. Our results show that cooperation is only maintained if conditions for altruistic punishment are relatively favourable: low cost for the punisher and high impact on the punished. Our results indicate that punishment is strongly governed by its costto-impact ratio and that its effect on cooperation can be pinned down to one single variable: the threshold level of free-riding that goes unpunished. Additionally, actual pay-offs are the lowest when altruistic punishment maintains cooperation, because the pay-off destroyed through punishment exceeds the gains from increased cooperation. Our results are consistent with the interpretation that punishment decisions come from an amalgam of emotional response and cognitive cost-impact analysis and suggest that altruistic punishment alone can hardly maintain cooperation under multi-level natural selection. Uncovering the workings of altruistic punishment as has been done here is important because it helps predicting under which conditions altruistic punishment is expected to maintain cooperation