Publications

NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.

2020

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Abstract

Wildlife managers conduct population inventories to monitor species, particularly those at-risk. Although costly and time consuming, grid-based DNA hair-snag sampling has been the standard protocol for grizzly bear inventories in North America, while opportunistic fecal DNA sampling is more commonly used in Europe. Our aim is to determine if low-cost, low-effort scat sampling along roads can replace the current standard. We compare two genetic non-invasive techniques using concurrent sampling within the same grid system and spatially explicit capture–recapture. We found that given our methodology and the present status of fecal genotyping for grizzly bears, scat sampling along roads cannot replace hair sampling to estimate population size in low-density areas. Hair sampling identified the majority of individual grizzly bears, with a higher success rate of individuals identified from grizzly bear samples (100%) compared to scat sampling (14%). Using scat DNA to supplement hair data did not change population estimates, but it did improve estimate precision. Scat samples had higher success identifying species (98%) compared with hair (80%). Scat sampling detected grizzly bears in grid cells where hair sampling showed non-detection, with almost twice the number of cells indicating grizzly bear presence. Based on our methods and projected expenses for future implementation, we estimated an approximate 30% cost reduction for sampling scat relative to hair. Our research explores the application of genetic non-invasive approaches to monitor bear populations. We recommend wildlife managers continue to use hair-snag sampling as the primary method for DNA inventories, while employing scat sampling as supplemental to increase estimate precision. Scat sampling may better indicate presence of bear species through greater numbers and spatial distribution of detections, if sampling is systematic across the entire area of interest. Our findings speak to the management of other species and regions, and contribute to ongoing advances of monitoring wildlife populations.

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No abstract has been registered

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Abstract

Although it is well known that insects are sensitive to temperature, how they will be affected by ongoing global warming remains uncertain because these responses are multifaceted and ecologically complex. We reviewed the effects of climate warming on 31 globally important phytophagous (plant‐eating) insect pests to determine whether general trends in their responses to warming were detectable. We included four response categories (range expansion, life history, population dynamics, and trophic interactions) in this assessment. For the majority of these species, we identified at least one response to warming that affects the severity of the threat they pose as pests. Among these insect species, 41% showed responses expected to lead to increased pest damage, whereas only 4% exhibited responses consistent with reduced effects; notably, most of these species (55%) demonstrated mixed responses. This means that the severity of a given insect pest may both increase and decrease with ongoing climate warming. Overall, our analysis indicated that anticipating the effects of climate warming on phytophagous insect pests is far from straightforward. Rather, efforts to mitigate the undesirable effects of warming on insect pests must include a better understanding of how individual species will respond, and the complex ecological mechanisms underlying their responses.

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Aim: Distribution modelling is a useful approach to obtain knowledge about the spatial distribution of biodiversity, required for, for example, red-list assessments. While distribution modelling methods have been applied mostly to single species, modelling of communities and ecosystems (EDM; ecosystem-level distribution modelling) produces results that are more directly relevant for management and decision-making. Although the choice of predictors is a pivotal part of the modelling process, few studies have compared the suitability of different sets of predictors for EDM. In this study, we compare the performance of 50 single environmental variables with that of 11 composite landscape gradients (CLGs) for prediction of ecosystem types. The CLGs represent gradients in landscape element composition derived from multivariate analyses, for example “inner-outer coast” and “land use intensity.” Location: Norway. Methods: We used data from field-based ecosystem-type mapping of nine ecosystem types, and environmental variables with a resolution of 100 × 100 m. We built nine models for each ecosystem type with variables from different predictor sets. Logistic regression with forward selection of variables was used for EDM. Models were evaluated with independently collected data. Results: Most ecosystem types could be predicted reliably, although model performance differed among ecosystem types. We identified significant differences in predictive power and model parsimony across models built from different predictor sets. Climatic variables alone performed poorly, indicating that the current climate alone is not sufficient to predict the current distribution of ecosystems. Used alone, the CLGs resulted in parsimonious models with relatively high predictive power. Used together with other variables, they consistently improved the models. Main conclusions: Our study highlights the importance of variable selection in EDM. We argue that the use of composite variables as proxies for complex environmental gradients has the potential to improve predictions from EDMs and thus to inform conservation planning as well as improve the precision and credibility of red lists and global change assessments.conservation planning, distribution modelling, ecosystem classification, ecosystem types, IUCN Red List of Ecosystems, landscape gradients, spatial prediction, species response curves

Abstract

In studies of consumption of local food specialties (LFSs), individual personalities are rarely mentioned. In this article, we want to expand on and provide a nuanced explanation of the characteristics of these consumers of these products, asking: Are there any personality traits that characterize these consumers? We use the Big Five personality model to unpack the relationship between individuals' personalities and choices of LFS in the Norwegian context. The model consists of the following five personal traits: extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience. These personality traits are latent, but through questions regarding behavior, the traits may be revealed. To construct latent variables to measure these traits, we apply the graded response model. Furthermore, socioeconomic variables are combined with personality traits in logistic regression models to find the relationships between personality and choice of Norwegian LFSs. Our results show that in all models the latent variable Openness to experience was one of the most important predictors of all the choices of LFS made by individuals. Openness to experience is characterized by fantasy, aesthetic sensitivity, attentiveness to inner feelings, preference for variety, and intellectual curiosity. The consequence of the connection between Openness to experience and LFS is that stakeholders may take this into account when seeking to increase sales.

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Abstract

There has been much recent research interest in the existence of a major axis of life‐history variation along a fast–slow continuum within almost all major taxonomic groups. Eco‐evolutionary models of density‐dependent selection provide a general explanation for such observations of interspecific variation in the "pace of life." One issue, however, is that some large‐bodied long‐lived “slow” species (e.g., trees and large fish) often show an explosive “fast” type of reproduction with many small offspring, and species with “fast” adult life stages can have comparatively “slow” offspring life stages (e.g., mayflies). We attempt to explain such life‐history evolution using the same eco‐evolutionary modeling approach but with two life stages, separating adult reproductive strategies from offspring survival strategies. When the population dynamics in the two life stages are closely linked and affect each other, density‐dependent selection occurs in parallel on both reproduction and survival, producing the usual one‐dimensional fast–slow continuum (e.g., houseflies to blue whales). However, strong density dependence at either the adult reproduction or offspring survival life stage creates quasi‐independent population dynamics, allowing fast‐type reproduction alongside slow‐type survival (e.g., trees and large fish), or the perhaps rarer slow‐type reproduction alongside fast‐type survival (e.g., mayflies—short‐lived adults producing few long‐lived offspring). Therefore, most types of species life histories in nature can potentially be explained via the eco‐evolutionary consequences of density‐dependent selection given the possible separation of demographic effects at different life stages.