Distribution and spread of the invasive slug Arion vulgaris Moquin-Tandon in Norway

Hatteland, Bjørn Arild; Roth, Steffen; Andersen, A.; Kaasa, Kristin; Støa, Bente; Solhøy, Torstein

doi:10.5324/fn.v32i0.1473

Cited by 21 publications

(16 citation statements)

References 19 publications

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“…This improves the realism of model predictions compared to approaches that provide binary predictions. (c) Deviations from a unimodal curve shape by inability of the species to fill its potential range, typically because of interactions between dispersal constraints and available time (Primack & Miao 1992, Hatteland et al 2013, may result in influences a species' response to one complex-gradient by other complex-gradients with different spatial patterns in the study area (Halvorsen 2012). (d) Local adaptations (Westley et al 2013).…”

Section: Establishing Theoretical Fop Curves: General Properties Of Smentioning

confidence: 99%

Sampling bias in presence-only data used for species distribution modelling: theory and methods for detecting sample bias and its effects on models

et al. 2018

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This paper provides a theoretical understanding of sampling bias in presence-only data in the context of species distribution modelling. This understanding forms the basis for two integrated frameworks, one for detecting sampling bias of different kinds in presence-only data (the bias assessment framework) and one for assessing potential effects of sampling bias on species distribution models (the bias effects framework). We exemplify the use of these frameworks to museum data for nine insect species in Norway, for which the distribution along the two main bioclimatic gradients (related to oceanicity and temperatures) are modelled using the MaxEnt method. Models of different complexity (achieved by use of two different model selection procedures that represent spatial prediction or ecological response modelling purposes, respectively) were generated with different types of background data (uninformed and background-target-group [BTG]). The bias assessment framework made use of comparisons between observed and theoretical frequency-of-presence (FoP) curves, obtained separately for each combination of species and bioclimatic predictor, to identify potential sampling bias. The bias effects framework made use of comparisons between modelled response curves (predicted relative FoP curves) and the corresponding observed FoP curves for each combination of species and predictor. The extent to which the observed FoP curves deviated from the expected, smooth and unimodal theoretical FoP curve, varied considerably among the nine insect species. Among-curve differences were, in most cases, interpreted as indications of sampling bias. Using BTG-type background data in many cases introduced strong sampling bias. The predicted relative FoP curves from MaxEnt were, in general, similar to the corresponding observed FoP curves. This indicates that the main structure of the data-sets were adequately summarised by the MaxEnt models (with the options and settings used), in turn suggesting that shortcomings of input data such as sampling bias or omission of important predictors may overshadow the effect of modelling method on the predictive performance of distribution models. The examples indicate that the two proposed frameworks are useful for identification of sampling bias in presence-only data and for choosing settings for distribution modelling options such as the method for extraction of background data points and determining the appropriate level of model complexity.

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Section: Establishing Theoretical Fop Curves: General Properties Of Smentioning

confidence: 99%

Sampling bias in presence-only data used for species distribution modelling: theory and methods for detecting sample bias and its effects on models

et al. 2018

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“…Populations of more than 50 slugs per m 2 have been reported from wildflower strips and meadows (Briner and Frank, 1998b). In Norway, high densities of A. vulgaris are mostly reported from the southern and western costal parts, but further spread is expected (Hatteland et al, 2013). Climatic changes with elevated winter temperatures and more rainfall can increase the survival rates (Slotsbo, 2012).…”

Section: Introductionmentioning

confidence: 98%

Effect of invasive slug populations (Arion vulgaris) on grass silage. II: Microbiological quality and feed safety

Gismervik

Randby

Rørvik

et al. 2015

Animal Feed Science and Technology

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a b s t r a c tThis study aimed to explore how invasive slug populations of Arion vulgaris influenced the microbiological quality and animal feed safety of grass silage, and the efficiency of silage additives and wilting to control the microbiology of slug contaminated crops. The effect of four slug contamination levels, including control, of a grass crop harvested for silage production, was evaluated in laboratory scale. The crop was wilted to two dry matter (DM) levels: low (253 g DM/kg) and high (372 g DM/kg). Adult slugs were applied to the low DM crop corresponding to 5 (low level), 10 (medium) and 20 (high level) seven-gram sized Arion vulgaris per m 2 in an assumed harvested regrowth yield of 2.5 ton DM/ha. For the high DM crop, slug weights corresponding to 6 (low level), 12 (medium) and 24 (high level) slugs per m 2 were applied. At low DM level, the effect of four additive treatments; control (C), inoculation with Lactobacillus plantarum (LP), a formic, propionic and benzoic acid mixture (ACID) and a chemical additive containing benzoic acid, NaNO 2 , hexamethylenetetramine and propionic acid (CHEM) were tested. Slugs, slug feces, grass, soil and silages were analyzed for lactic acid bacteria (LAB), Enterobacteriaceae, Listeria monocytogenes, Clostridium tyrobutyricum, molds and yeasts by cultivation methods and Clostridium botulinum type C by real-time PCR analysis.Increasing slug contamination reduced the microbial quality of silages by increasing C. tyrobutyricum levels at both silage DM levels. Only silages without slugs and silages treated with the nitrite containing additive CHEM had non-detectable mean levels of C. tyrobutyricum. Increasing slug contamination increased LAB enumerations in silages. No microbes of risk to human or animal health were detected in anaerobic silages even at the highest slug contamination.

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“…Arion vulgaris has the ability to hybridise with the related species of Arion thus posing a threat to the native biota (Hatteland et al, 2015). In Norway, the slug was first recorded in 1988 (Hatteland et al, 2013), and is known to inflict serious damage in gardens, horticulture and agriculture.…”

Section: Discussionmentioning

confidence: 99%

Angiostoma norvegicum n. sp. (Nematoda: Angiostomatidae) a parasite of arionid slugs in Norway

Ross

Haukeland²,

Hatteland

et al. 2017

Syst Parasitol

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Angiostoma norvegicum n. sp. (Angiostomatidae) is described from the oesophagus, crop and the buccal mass of five species of slugs of the family Arionidae, Arion vulgaris (Moquin-Tandon), Arion ater (L.), Arion fasciatus (Nilsson), Arion fuscus (Müller) and Arion rufus/Arion ater hybrid), collected throughout Norway. Angiostoma norvegicum n. sp. was found parasitising arionids at seven of the 30 sample sites examined (23.3%), and 9.9% of all Arion spp. were infected with this nematode. The new species is characterised by its large size (4.0–8.6 mm long) and in having: lateral alae; 6 + 6 papillae at the cephalic end; a large circular mouth aperture; a spacious stoma; a pharyngeal basal bulb without valvular apparatus; an excretory pore near the base of bulb; a distal part of posterior ovary always outstretched; an anterior ovary distally nearly always outstretched; a vulva situated anterior to mid-body; long, nearly straight spicules and a small gubernaculum; three circumcloacal papillae and caudal genital papillae (GP) arranged in a pattern 1+2/3+3 with GP 5 and GP 8 opened on dorsal side of narrow bursa not reaching tail tip; short conical tails in both sexes with tips supplied by 4 short, unequal denticles. Morphologically, A. norvegicum n. sp. is similar to Angiostoma limacis Dujardin, 1845, which diagnostic characteristics are given based on examination of specimens from Norway and the UK. Conversely, the phylogenetic analyses based on D2D3 large subunit (LSU) rRNA gene sequences performed in the present study did not support the morphological affinity of these two species. Phylogenetic analyses demonstrated that although Angiostoma spp. cluster together, A. norvegicum n. sp. forms a tight monophyletic clade with the milacid nematode parasites Angiostoma margaretae Ross, Malan & Ivanova, 2011 and Angiostoma milacis Ivanova & Wilson, 2009.

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Distribution and spread of the invasive slug Arion vulgaris Moquin-Tandon in Norway

Cited by 21 publications

References 19 publications

Sampling bias in presence-only data used for species distribution modelling: theory and methods for detecting sample bias and its effects on models

Sampling bias in presence-only data used for species distribution modelling: theory and methods for detecting sample bias and its effects on models

Effect of invasive slug populations (Arion vulgaris) on grass silage. II: Microbiological quality and feed safety

Angiostoma norvegicum n. sp. (Nematoda: Angiostomatidae) a parasite of arionid slugs in Norway

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