Effects of rarity form on species' responses to land use

Anthropogenic land-use change causes substantial changes in local and global biodiversity. Rare and common species can differ in sensitivity to land-use change, with rare species expected to be more negatively affected. Rarity may be defined in terms of geographic range size, population density or breadth of habitat requirements. How these three forms of rarity interact in determining global responses to land use is yet to be assessed. Using global data representing 912 vertebrate species, we test for differences in the responses to land use of species characterised by different types of rarity. Species considered rare with respect to all three forms of rarity showed particularly strong declines in disturbed land uses (more than 40% of species and 30% of individuals in the most disturbed land uses). In contrast, species common both geographically and numerically, and with broad habitat requirements, showed strong increases (up to 90% increase in species and 40% in abundance in some land uses). Our results suggest that efforts to understand the vulnerability of species to environmental changes should account for different types of rarity where possible. Our results also have potentially important implications for ecosystem functioning, given that rare species may play unique roles within ecosystems. Article impact statement: Rare species show stronger negative responses to anthropogenic land use than common species

Tracking artificial intelligence in climate inventions with patent data

Artificial intelligence (AI) is spreading rapidly in many technology areas, and AI inventions may help climate change mitigation and adaptation. Previous studies of climate-related AI mainly rely on expert studies of literature, not large-scale data. Here I present an approach to track the relation between AI and climate inventions on an economy-wide scale. Analysis of over 6 million US patents, 1976 to 2019, shows that within climate patents, AI is referred to most often in transportation, energy and industrial production technologies. In highly cited patents, AI occurs more frequently in adaptation and transport than in other climate mitigation areas. AI in climate patents was associated with around 30–100% more subsequent inventions when counting all technologies. Yet AI-climate patents led to a greater share of citations from outside the climate field than non-AI-climate patents. This suggests the importance of tracking both increased invention activity and the areas where subsequent inventions emerge

Assessing the effects of land use on biodiversity in the world's drylands and Mediterranean environments

Biodiversity models make an important contribution to our understanding of global biodiversity changes. The effects of different land uses vary across ecosystem types, yet most broad-scale models have failed to account for this variation. The effects of land use may be different in systems characterized by low water availability because of the unusual conditions within these systems. Drylands are expanding, currently occupying over 40% of the terrestrial land, while Mediterranean systems are highly endangered biodiversity hotspots. However, the impact of land use on biodiversity in these biomes is yet to be assessed. Using a database of local biodiversity surveys, we assess the effects of land use on biodiversity in the world’s drylands and Mediterranean ecosystems. We compare the average species richness, total abundance, species diversity, ecological dominance, endemism rates, and compositional turnover across different land uses. In drylands, there was a strong turnover in species composition in disturbed land uses compared with undisturbed natural habitat (primary vegetation), but other measures of biodiversity did not respond significantly. However, it is important to note that the sample size for drylands was very low, a gap which should be filled promptly. Mediterranean environments showed a very high sensitivity of biodiversity to land uses. In this biome, even habitat recovering after past disturbance (secondary vegetation) had substantially reduced biodiversity and altered community composition compared with primary vegetation. In an effort to maintain original biodiversity and the ecosystem functions it supports within Mediterranean biomes, conservation measures should therefore prioritize the preservation of remaining primary vegetation

An expectation-maximization algorithm for the exponential-generalized inverse Gaussian regression model with varying dispersion and shape for modelling the aggregate claim amount

This article presents the Exponential–Generalized Inverse Gaussian regression model with varying dispersion and shape. The EGIG is a general distribution family which, under the adopted modelling framework, can provide the appropriate level of flexibility to fit moderate costs with high frequencies and heavy-tailed claim sizes, as they both represent significant proportions of the total loss in non-life insurance. The model’s implementation is illustrated by a real data application which involves fitting claim size data from a European motor insurer. The maximum likelihood estimation of the model parameters is achieved through a novel Expectation Maximization (EM)-type algorithm that is computationally tractable and is demonstrated to perform satisfactorily

Beyond mean modelling: Bias due to misspecification of dispersion in Poisson-inverse Gaussian regression.

In clinical trials one traditionally models the effect of treatment on the mean response. The underlying assumption is that treatment affects the response distribution through a mean location shift on a suitable scale, with other aspects of the distribution (shape/dispersion/variance) remaining the same. This work is motivated by a trial in Parkinson's disease patients in which one of the endpoints is the number of falls during a 10-week period. Inspection of the data reveals that the Poisson-inverse Gaussian (PiG) distribution is appropriate, and that the experimental treatment reduces not only the mean, but also the variability, substantially. The conventional analysis assumes a treatment effect on the mean, either adjusted or unadjusted for covariates, and a constant dispersion parameter. On our data, this analysis yields a non-significant treatment effect. However, if we model a treatment effect on both mean and dispersion parameters, both effects are highly significant. A simulation study shows that if a treatment effect exists on the dispersion and is ignored in the modelling, estimation of the treatment effect on the mean can be severely biased. We show further that if we use an orthogonal parametrization of the PiG distribution, estimates of the mean model are robust to misspecification of the dispersion model. We also discuss inferential aspects that are more difficult than anticipated in this setting. These findings have implications in the planning of statistical analyses for count data in clinical trials.The trial and post hoc analyses were funded by Lundbeck

Relation between respiratory diseases and air pollution and climatic variables, in Curitiba, Parana, Brazil

ResumoNeste estudo, foram observados níveis de concentrações de poluentes atmosféricos da cidade de Curitiba, no período de novembro de 2003 a junho de 2008, da estação automática de monitoramento da praça Ouvidor Pardinho. O objetivo foi desenvolver uma metodologia de modelagem estatística que explique o número de notificações de doenças respiratórias registradas em idosos (acima de 60 anos) e crianças (abaixo de 5 anos). Além das concentrações de poluentes, algumas variáveis climáticas foram analisadas. Após estudo de diversas metodologias estatísticas, considerou-se mais adequado o ajuste de Modelos Aditivos Generalizados para Locação, Escala e Forma. Os modelos ajustados apresentaram resultados satisfatórios e consideraram como variáveis significativas as partículas inaláveis e a temperatura. Além disso, verificou-se que existe redução proporcional do número de notificações de doenças respiratórias no período observado, em consequência da redução dos níveis de concentração de partículas inaláveis no município de Curitiba. Os resultados encontrados podem ter sido influenciados pelas campanhas de vacinação de idosos, pela utilização de motores menos poluentes no transporte coletivo, pelo aumento da produção de carros “flex” e também pela grande cobertura vegetal existente no município, responsável pelos mais altos índices de área verde por habitante entre os grandes centros urbanos do Brasil. AbstractRelation between respiratory diseases and air pollution and climatic variables, in Curitiba, Parana, Brazil. In this research, concentration levels of atmospheric pollutants were observed between November 2003 and June 2008 from an automated monitoring station located at Ouvidor Pardinho Square, City of Curitiba. It aimed to develop a methodology of statistical model in order to explain the number of notifications of respiratory diseases registered in the elderly (above the age of 60 years) and children (below the age of 5 years). Apart from the concentration of pollutants, climatic variables were also analyzed. After study of several statistical methodologies, it was considered that the most adequate was an adjustment of the Generalized Additive Models for Location, Scale and Shape. The adjusted models presented satisfactory results and considered as significant variables the particulate matter and temperature. It was also observed a proportional reduction in the number of notifications of respiratory diseases within the observed period as a consequence of  the concentration level reduction of the particulate matter in the city. These results may have been influenced by vaccination campaigns involving the elderly, by use of less polluting engines in the public transportation system, by an increase in production of “flex”- type cars and also by the large vegetation coverage in the city, which is responsible for one of the highest indexes of green area per inhabitant amongst the large urban centers in Brazil.Keywords: Generalized linear models; pollutants; particulate matter; GAMLSS.In this research, concentration levels of atmospheric pollutants were observed between November 2003 and June 2008 from an automated monitoring station located at Ouvidor Pardinho Square, City of Curitiba. It aimed to develop a methodology of statistical model in order to explain the number of notifications of respiratory diseases registered in the elderly (above the age of 60 years) and children (below the age of 5 years). Apart from the concentration of pollutants, climatic variables were also analyzed. After study of several statistical methodologies, it was considered that the most adequate was an adjustment of the Generalized Additive Models for Location, Scale and Shape. The adjusted models presented satisfactory results and considered as significant variables the particulate matter and temperature. It was also observed a proportional reduction in the number of notifications of respiratory diseases within the observed period as a consequence of  the concentration level reduction of the particulate matter in the city. These results may have been influenced by vaccination campaigns involving the elderly, by use of less polluting engines in the public transportation system, by an increase in production of “flex”- type cars and also by the large vegetation coverage in the city, which is responsible for one of the highest indexes of green area per inhabitant amongst the large urban centers in Brazil

Crossing the hurdle: the determinants of individual scientific performance

An original cross sectional dataset referring to a medium sized Italian university is implemented in order to analyze the determinants of scientific research production at individual level. The dataset includes 942 permanent researchers of various scientific sectors for a three year time span (2008 - 2010). Three different indicators - based on the number of publications or citations - are considered as response variables. The corresponding distributions are highly skewed and display an excess of zero - valued observations. In this setting, the goodness of fit of several Poisson mixture regression models are explored by assuming an extensive set of explanatory variables. As to the personal observable characteristics of the researchers, the results emphasize the age effect and the gender productivity gap, as previously documented by existing studies. Analogously, the analysis confirm that productivity is strongly affected by the publication and citation practices adopted in different scientific disciplines. The empirical evidence on the connection between teaching and research activities suggests that no univocal substitution or complementarity thesis can be claimed: a major teaching load does not affect the odds to be a non-active researcher and does not significantly reduce the number of publications for active researchers. In addition, new evidence emerges on the effect of researchers administrative tasks, which seem to be negatively related with researcher's productivity, and on the composition of departments. Researchers' productivity is apparently enhanced by operating in department filled with more administrative and technical staff, and it is not significantly affected by the composition of the department in terms of senior or junior researchers.Comment: Revised version accepted for publication by Scientometric

Agriculture and climate change reshape insect biodiversity worldwide

Several previous studies have investigated changes in insect biodiversity, with some highlighting declines and others showing turnover in species composition without net declines1,2,3,4,5. Although research has shown that biodiversity changes are driven primarily by land-use change and increasingly by climate change6,7, the potential for interaction between these drivers and insect biodiversity on the global scale remains unclear. Here we show that the interaction between indices of historical climate warming and intensive agricultural land use is associated with reductions of almost 50% in the abundance and 27% in the number of species within insect assemblages relative to those in less-disturbed habitats with lower rates of historical climate warming. These patterns are particularly evident in the tropical realm, whereas some positive responses of biodiversity to climate change occur in non-tropical regions in natural habitats. A high availability of nearby natural habitat often mitigates reductions in insect abundance and richness associated with agricultural land use and substantial climate warming but only in low-intensity agricultural systems. In such systems, in which high levels (75% cover) of natural habitat are available, abundance and richness were reduced by 7% and 5%, respectively, compared with reductions of 63% and 61% in places where less natural habitat is present (25% cover). Our results show that insect biodiversity will probably benefit from mitigating climate change, preserving natural habitat within landscapes and reducing the intensity of agriculture

A framework for modelling overdispersed count data, including the Poisson-shifted generalized inverse Gaussian distribution

A variety of methods of modelling overdispersed count data are compared. The methods are classified into three main categories. The first category are ad hoc methods (i.e. pseudo-likelihood, (extended) quasi-likelihood, double exponential family distributions). The second category are discretized continuous distributions and the third category are observational level random effects models (i.e. mixture models comprising explicit and non-explicit continuous mixture models and finite mixture models). The main focus of the paper is a family of mixed Poisson distributions defined so that its mean [mu] is an explicit parameter of the distribution. This allows easier interpretation when [mu] is modelled using explanatory variables and provides a more orthogonal parameterization to ease model fitting. Specific three parameter distributions considered are the Sichel and Delaporte distributions. A new four parameter distribution, the Poisson-shifted generalized inverse Gaussian distribution is introduced, which includes the Sichel and Delaporte distributions as a special and a limiting case respectively. A general formula for the derivative of the likelihood with respect to [mu], applicable to the whole family of mixed Poisson distributions considered, is given. Within the framework introduced here all parameters of the distributions are modelled as parametric and/or nonparametric (smooth) functions of explanatory variables. This provides a very flexible way of modelling count data. Maximum (penalized) likelihood estimation is used to fit the (non)parametric models.