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opencc-zeroMay 2022View details →

Population structure and genetic variance among local populations of an non-native earthworm species in Minnesota, USA

A variety of human activities have been identified as driving factors for the release and spread of invasive earthworm species in North America. Population genetic markers can help to identify locally relevant anthropogenic vectors and provide insights into the processes of population dispersal and establishment. We sampled the invasive European earthworm species Lumbricus terrestris at nine sites and several bait shops within the metropolitan area of Minneapolis-St. Paul in Minnesota, USA. We used microsatellite markers to infer genetic diversity and population structure, and 16S rDNA to address multiple introduction events, including bait dumping, which is a common source of L. terrestris introductions into the wild. Our results indicate multiple introductions but not from current bait dumping. Overall, genetic structure was low and earthworms >5000 m apart were genetically differentiated, except for one sampling location, indicating jump-dispersal followed by population establishment. Further, earthworms at one location north of Minneapolis established from one or few founder individuals, suggesting that earthworm invasions are ongoing. We therefore encourage further monitoring of earthworm populations using molecular markers, in order to disentangle the different human-related vectors contributing to the spread of earthworms and their establishment, which is essential to develop adequate management strategies.

opencc-zeroAug 2022View details →

Seasonal variation in impact of non-native species on tropical seed dispersal networks

Invasive non-native species can alter animal-mediated seed dispersal interactions and ultimately affect the stability of recipient communities. The degree of such disturbances, however, is highly variable and depends on several factors, two of which have received little attention: the relative timing of native and non-native fruiting phenologies, and the associated variation in relative resource availability across the fruiting period. Both are likely to alter plant-seed disperser interactions threatened by biological invasions. Here we investigated the impact of plant invasions on the seasonal dynamics of frugivory and seed dispersal networks across a large-scale experimental setup and a plant invasion gradient on a tropical island. We recorded fruit and frugivore abundances, and plant-frugivore interactions across 8 inselbergs (i.e. rocky outcrops) with different levels of plant invasion during 10 months on the island of Mahé, Seychelles. By combining four sampling methods of plant-frugivore interactions we constructed quantitative seed dispersal networks at all sites across two 5-month seasons: the on-peak and off-peak fruiting season. Our findings showed that, by fruiting mostly synchronously with natives, non-native plants compete with natives for dispersal services, predominantly carried out by native frugivores. Variation in native seed dispersal was driven by plant invasion and seasonality. Specifically, native seed dispersal declined with the degree of invasion; dispersal frequency increased with fruit abundance more strongly during the off-peak fruiting season; and networks became increasingly specialised during off-peak. These results indicated that during the main fruiting peak seed dispersal services were saturated, which likely intensified the competition between native and non-native fruits. When resources were scarce during off-peak fruiting season, native and non-native frugivores were more selective in their fruit choice at sites dominated by non-native plants. We showed that native plant and frugivore populations and native seed dispersal interactions were more vulnerable in invaded plant communities, where non-native plants compete with natives for dispersal services potentially reducing native recruitment. As invasive non-native plants dominate many ecosystems worldwide, particularly on islands, our findings showed that controlling plant invasions in vulnerable native communities can be critical to maintain native ecosystem functions and biodiversity.

opencc-zeroOct 2022View details →

Beetle diversity in dead wood is lower in non-native than native tree species, especially those more distantly related to native species

1. Non-native tree species are widely used in forest plantations. This may have negative consequences for biodiversity. Hitherto, most studies have compared species diversity between native and non-native forest stands, which makes it difficult to separate the impact of tree species per se from stand characteristics. Our study, conducted in the south of Sweden, compares saproxylic beetle diversity across different nutritional groups, in dead wood of two native and four non-native tree species in a block design after one and three seasons. Such an approach allows analysis of the impact of non-native tree species per se. 2. Mean species richness (±SD) per log was lower in non-native than in native tree species (non-native trees: lodgepole pine: 10.7 (± 5.3); Sitka spruce: 8.5 (± 4.3), Douglas fir: 7.1 (± 4.3), Japanese larch 9.4 (± 4.6); native trees: Norway spruce: 12.0 (± 6.0), Scots pine: 12.3 (± 5.2)). Sample-based rarefaction revealed that when only native tree species were pooled, the species richness was higher than for all tree species combined. The difference in species composition among tree species was strongly driven by bark and wood consumers in the first season, while for predators and fungivores, the differences were smaller. Species composition differed more after one season. 3. Dissimilarity in beetle species composition was positively correlated with phylogenetic distances of the tree species. Species richness was lower in non-native tree species that are only remotely related to native trees species. Of the studied non-native tree species, lodgepole pine was more closely related to native tree species and consistently harboured higher species richness. 4. Synthesis and applications. Although non-native tree species also harbour saproxylic beetle communities, the use of non-native tree species, especially those only remotely related to native tree species, reduces local diversity of saproxylic beetles. Thus, for biodiversity conservation, an extensive use of non-native tree species is not recommended as this increases the risk of losing forest biodiversity, especially when they are only distantly related to native tree species.

opencc-zeroApr 2024View details →

Native and non-native insect herbivores associated with native and non-native European trees

We compiled a list of all native and non-native insect species known to feed on 77 tree species in Europe. For each tree species, a list of insects known to utilize that species as a host was compiled using a variety of sources. The resulting list consists of 7,598 tree species -insect species pairs. Each insect was researched to determine its taxonomic groupings, feeding guild (gall-maker, folivore, reproductive plant feeder, sap-feeder, or phloem/wood-borer), and whether it was native to Europe or non-native.

opencc-zeroApr 2024View details →

Dataset used for analyzing the critical area thresholds for undergoing rapid increases of established non-native terrestrial vertebrates in global islands

Biological invasions are among the threats to global biodiversity and social sustainability, especially on islands. Identifying the threshold of area at which non-native species begin to increase abruptly is crucial for the early prevention strategies. The small-island effect (SIE) was proposed to quantify the nonlinear relationship between native species richness and area but has not yet been applied to non-native species and thus to predict the key breakpoints at which established non-native species start to increase rapidly. Here, based on the extensive global dataset including 769 non-native bird, mammal, amphibian, and reptile species established on 4,277 islands across 54 archipelagos, we detected a high prevalence of SIEs across 66.7% of archipelagos, and approximately 50% of islands have reached the threshold area and thus may be undergoing a rapid increase of biological invasions. SIEs were more likely to occur in those archipelagos with more non-native species introduction events, more established historical non-native species, lower habitat diversity and larger archipelago area range. Our findings may have important implications not only for targeted surveillance of biological invasions on global islands but also for predicting the responses of both non-native and native species to ongoing habitat fragmentation under sustained land-use modification and climate change.

opencc-zeroMay 2024View details →

Data from: High foraging fidelity and plant-pollinator network dominance of non-native honeybees (Apis mellifera) in the Ecuadorian Andes

These data reflect the floral visitor survey and mark-recapture efforts used in the 2022 study, "High Foraging Fidelity and Plant‑Pollinator Network Dominance of Non‑native Honeybees (Apis mellifera) in the Ecuadorian Andes"

opencc-by-4.0May 2024View details →

Dimensional coding of native and non-native speech categories: data and code

Data: (i) fMRI nii files, (ii) categorization responses , and (iii) stimuli Code: (i) Dimensionality analyses, (ii) statistical analyses, and (iii) MVPC analyses, and (iv) data visualization

opencc-by-4.0May 2024View details →

Are rice fields less diverse and more invaded by non-native species than less impacted habitats? A test with wetland microcrustaceans

Raw data of environmental variables and microcrustacean abundance community matrix R scripts used in the study

opencc-by-4.0Jan 2024View details →

Figure 1 in Risk screening of non-native freshwater fishes in Yunnan Province, China

Figure 1. Map of the study (risk screening) area (Yunnan Province in southwest China)

Fig. 4 in New distributional records of non-native vascular plants in northern Italy

Fig. 4 - Perilla frutescens, Basaluzzo (AL), September 2014 (Photo: F. Verloove).

Fig. 3 in New distributional records of non-native vascular plants in northern Italy

Fig. 3 - Oenothera pedemontana, Bereguardo (PV), September 2014 (Photo: N. Ardenghi).

Fig. 2 in New distributional records of non-native vascular plants in northern Italy

Fig. 2 - Bidens vulgatus, Martignana di Po (CR), September 2014 (Photo: F. Verloove).

Fig. 5 in New distributional records of non-native vascular plants in northern Italy

Fig. 5 - Populus deltoides, San Rocco al Porto (LO), September 2014 (Photo: F. Verloove).

opencc-by-4.0Mar 2016View details →

Fig. 2 in Use of crape myrtle, Lagerstroemia (Myrtales: Lythraceae), cultivars as a pollen source by native and non-native bees (Hymenoptera: Apidae) in Quincy, Florida

Fig. 2. Bahiagrass quadrat positions.

opencc-by-4.0Mar 2016View details →

Fig. 3 in Use of crape myrtle, Lagerstroemia (Myrtales: Lythraceae), cultivars as a pollen source by native and non-native bees (Hymenoptera: Apidae) in Quincy, Florida

Fig. 3. Isolines depicting Bombus impatiens aggregations and gaps in bahiagrass on 23 Jul 2010.

opencc-by-4.0Mar 2016View details →

Fig. 4 in Use of crape myrtle, Lagerstroemia (Myrtales: Lythraceae), cultivars as a pollen source by native and non-native bees (Hymenoptera: Apidae) in Quincy, Florida

Fig. 4. Isolines depicting Bombus impatiens distributions in crape myrtle on 21 Jul 2010.

opencc-by-4.0Jul 2024View details →

The role of non-native plant species in modulating riverbank erosion: a systematic review - extracted coded articles dataset

Data extraction sheet of code typologies for the review titled: The role of non-native plant species in modulating riverbank erosion: a systematic review. The uploaded csv file contains all the extracted values for each typology.

opencc-by-4.0Jan 2018View details →

Studies that quantify fraction of native and non-native species

This database contains the results of a literature search of studies that report number (or fraction) of animal and plant native and non-native species in a study. Compiled by Martin A. Schlaepfer, University of Geneva, Switzerland. 31 January 2018.

opencc-by-4.0Feb 2016View details →

NNSeval: Evaluating Lexical Simplification for Non-Natives

We have conducted a user study to learn more about word complexity for non-native speakers. 400 non-native speakers participated in the experiment, all university students or staff. They were asked to judge whether or not they could understand the meaning of each content word (nouns, verbs, adjectives and adverbs, as tagged by Freeling (Padr and Stanilovsky (2012)) in a set of sentences, each of which was judged independently. Volunteers were instructed to annotate all words that they could not understand individually, even if they could comprehend the meaning of the sentence as a whole. All sentences used were taken from Wikipedia, LSeval and LexMTurk. A total of 35,958 distinct words from 9,200 sentences were annotated (232,481 total), of which 3,854 distinct words (6,388 total) were deemed as complex by at least one annotator. Using the data produced in the user study, we first assessed reliability of the LSeval and LexMTurk datasets in evaluating LS systems for non-native speakers. We found that the proportion of target words deemed complex by at least one annotator was only 30.8% for LexMTurk, and 15% for LSe- val. As for the candidate substitutions, 21.7% of the ones in LSeval and 13.4% in LexMTurk were deemed complex by at least one annotator. These results show that, although they may not be used in their entirety, both datasets contain instances that are suit- able for our purposes. To create our dataset, we first used the Text Adorning module of LEXenstein (Paetzold and Specia 2015; Burns 2013) to inflect all candidate verbs and nouns in both datasets to the same tense as the target word. We then used the Spelling Correction module of LEXenstein to correct any misspelled words among the candidates of both datasets. Next, we removed all candidate substitutes which were deemed complex by at least one annotator in our user study. Finally, we discarded all instances in which the target word was not deemed complex by any of our annotators. The resulting dataset, which we refer to as NNSeval, contains 239 instances.