Does land use and landscape contribute to self-harm? A sustainability cities framework

Self-harm has become one of the leading causes of mortality in developed countries. The overall rate for suicide in Canada is 11.3 per 100,000 according to Statistics Canada in 2015. Between 2000 and 2007 the lowest rates of suicide in Canada were in Ontario, one of the most urbanized regions in Canada. However, the interaction between land use, landscape and self-harm has not been significantly studied for urban cores. It is thus of relevance to understand the impacts of land-use and landscape on suicidal behavior. This paper takes a spatial analytical approach to assess the occurrence of self-harm along one of the densest urban cores in the country: Toronto. Individual self-harm data was gathered by the National Ambulatory Care System (NACRS) and geocoded into census tract divisions. Toronto’s urban landscape is quantified at spatial level through the calculation of its land use at di erent levels: (i) land use type, (ii) sprawl metrics relating to (a) dispersion and (b) sprawl/mix incidence; (iii) fragmentation metrics of (a) urban fragmentation and (b) density and (iv) demographics of (a) income and (b) age. A stepwise regression is built to understand the most influential factors leading to self-harm from this selection generating an explanatory model.This research was supported by the Canadian Institutes of Health Research Strategic Team Grant in Applied Injury Research # TIR-103946 and the Ontario Neurotrauma Foundation grantinfo:eu-repo/semantics/publishedVersio

Does residence time affect responses of alien species richness to environmental and spatial processes?

One of the most robust emerging generalisations in invasion biology is that the probability of invasion increases with the time since introduction (residence time). We analysed the spatial distribution of alien vascular plant species in a region of north-eastern Italy to understand the influence of residence time on patterns of alien species richness. Neophytes were grouped according to three periods of arrival in the study region (1500–1800, 1800–1900, and > 1900). We applied multiple regression (spatial and nonspatial) with hierarchical partitioning to determine the influence of climate and human pressure on species richness within the groups. We also applied variation partitioning to evaluate the relative importance of environmental and spatial processes. Temperature mainly influenced groups with species having a longer residence time, while human pressure influenced the more recently introduced species, although its influence remained significant in all groups. Partial regression analyses showed that most of the variation explained by the models is attributable to spatially structured environmental variation, while environment and space had small independent effects. However, effects independent of environment decreased, and spatially independent effects increased, from older to the more recent neophytes. Our data illustrate that the distribution of alien species richness for species that arrived recently is related to propagule pressure, availability of novel niches created by human activity, and neutral-based (dispersal limitation) processes, while climate filtering plays a key role in the distribution of species that arrived earlier. This study highlights the importance of residence time, spatial structure, and environmental conditions in the patterns of alien species richness and for a better understanding of its geographical variation

THE DYNAMICS OF LAND-COVER CHANGE IN WESTERN HONDURAS: SPATIAL AUTOCORRELATION AND TEMPORAL VARIATION

This paper presents an econometric analysis of land-cover change in western Honduras. Ground-truthed satellite image analysis indicates that between 1987 and 1996, net reforestation occurred in the 1,015.12 km2 study region. While some reforestation can be attributed to a 1987 ban on logging, the area of reforestation greatly exceeds that of previously clear-cut areas. Further, new area was also deforested between 1987-1996. Thus, the observed land-cover changes most likely represent a complex mosaic of changing land-use patterns across time and space. We estimate a random-effects probit model to capture drivers of land-cover change that are spatial, temporal or both. We employ two techniques to correct for spatial error dependence in econometric analysis suitable to qualitative dependent variables. Lastly, we simulate the impact of anticipated changes in transportation costs on land cover. We find that market accessibility, increase in national coffee prices, and agricultural suitability are the most important determinants of recent land-cover change.Land Economics/Use,

Locating the poor: Spatially disaggregated poverty maps for Sri Lanka

Poverty / Mapping

Deforestation, Growth and Agglomeration Effects: Evidence From Agriculture in the Brazilian Amazon

The role of population growth and migration has been emphasized as a key variable to explain deforestation and land conversion in developing countries. In early studies a ‘Malthusian’ process is put forward to associate the growing demand for resources caused by larger populations in frontier areas. Recent empirical research has also focused on the role of population primarily as a measure for local demand and pressure over natural resources. The spatial distribution of human population and economic activities is remarkably uneven. At any geographical scale we find that different forms of agglomerations are pervasive. On the one hand, in central countries or regions, agglomeration is reflected in ‘large varieties of cities as shown by the stability of urban hierarchy within most countries’. On the other, less developed regions faces a dynamic process where new agglomerations form and develop as a result of frontier expansion. The recent literature on spatial economics has emphasized the role of agglomeration and clustering of economic activities as fundamental causes of an enhanced level of local economic performance, creating externalities that cause firms to grow faster and larger than they otherwise would do. However, very little has been done to examine the presence of agglomeration economies on economic performance of agricultural activities. The Brazilian Amazon is perhaps one of the most interesting regions for analysing eventual relationships between agglomeration economies, economic growth and deforestation. In this paper we empirically examine whether an initial level of agglomeration impacts the subsequent economic growth and deforestation rates in the Brazilian Amazon. We also test whether congestion effects at the higher levels of agglomeration limit these impacts by a non-linear relationship. The regression estimates indicate that there is a significant non-linear association between the initial intensity of agglomeration with both growth and land conversion in subsequent periods. We also find evidence of other factors associated with growth and land conversion.

Birds and people in Europe

At a regional scale, species richness and human population size are frequently positively correlated across space. Such patterns may arise because both species richness and human density increase with energy availability. If the species-energy relationship is generated through the 'more individuals' hypothesis, then the prediction is that areas with high human densities will also support greater numbers of individuals from other taxa. We use the unique data available for the breeding birds in Europe to test this prediction. Overall regional densities of bird species are higher in areas with more people; species of conservation concern exhibit the same pattern. Avian density also increases faster with human density than does avian biomass, indicating that areas with a higher human density have a higher proportion of small-bodied individuals. The analyses also underline the low numbers of breeding birds in Europe relative to humans, with a median of just three individual birds per person, and 4 g of bird for every kilogram of human

Untenable nonstationarity: An assessment of the fitness for purpose of trend tests in hydrology

The detection and attribution of long-term patterns in hydrological time series have been important research topics for decades. A significant portion of the literature regards such patterns as ‘deterministic components’ or ‘trends’ even though the complexity of hydrological systems does not allow easy deterministic explanations and attributions. Consequently, trend estimation techniques have been developed to make and justify statements about tendencies in the historical data, which are often used to predict future events. Testing trend hypothesis on observed time series is widespread in the hydro-meteorological literature mainly due to the interest in detecting consequences of human activities on the hydrological cycle. This analysis usually relies on the application of some null hypothesis significance tests (NHSTs) for slowly-varying and/or abrupt changes, such as Mann-Kendall, Pettitt, or similar, to summary statistics of hydrological time series (e.g., annual averages, maxima, minima, etc.). However, the reliability of this application has seldom been explored in detail. This paper discusses misuse, misinterpretation, and logical flaws of NHST for trends in the analysis of hydrological data from three different points of view: historic-logical, semantic-epistemological, and practical. Based on a review of NHST rationale, and basic statistical definitions of stationarity, nonstationarity, and ergodicity, we show that even if the empirical estimation of trends in hydrological time series is always feasible from a numerical point of view, it is uninformative and does not allow the inference of nonstationarity without assuming a priori additional information on the underlying stochastic process, according to deductive reasoning. This prevents the use of trend NHST outcomes to support nonstationary frequency analysis and modeling. We also show that the correlation structures characterizing hydrological time series might easily be underestimated, further compromising the attempt to draw conclusions about trends spanning the period of records. Moreover, even though adjusting procedures accounting for correlation have been developed, some of them are insufficient or are applied only to some tests, while some others are theoretically flawed but still widely applied. In particular, using 250 unimpacted stream flow time series across the conterminous United States (CONUS), we show that the test results can dramatically change if the sequences of annual values are reproduced starting from daily stream flow records, whose larger sizes enable a more reliable assessment of the correlation structures