The conditions for functional mechanisms of compensation and reward for environmental services

Mechanisms of compensation and reward for environmental services (CRES) are becoming increasingly contemplated as means for managing human–environment interactions. Most of the functional mechanisms in the tropics have been developed within the last 15 years; many developing countries still have had little experience with functional mechanisms. We consider the conditions that foster the origin and implementation of functional mechanisms. Deductive and inductive approaches are combined. Eight hypotheses are derived from theories of institution and policy change. Five case studies, from Latin America, Africa, and Asia, are then reviewed according to a common framework. The results suggest the following to be important conditions for functional CRES mechanisms: (1) localized scarcity for particular environmental services, (2) influence from international environmental agreements and international organizations, (3) government policies and public attitudes favoring a mixture of regulatory and marketbased instruments, and (4) security of individual and group property rights

Minimal mechanisms for vegetation patterns in semiarid regions

The minimal ecological requirements for formation of regular vegetation patterns in semiarid systems have been recently questioned. Against the general belief that a combination of facilitative and competitive interactions is necessary, recent theoretical studies suggest that, under broad conditions, nonlocal competition among plants alone may induce patterns. In this paper, we review results along this line, presenting a series of models that yield spatial patterns when finite-range competition is the only driving force. A preliminary derivation of this type of model from a more detailed one that considers water-biomass dynamics is also presented. Keywords: Vegetation patterns, nonlocal interactionsComment: 8 pages, 4 figure

Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation

Consuming news from social media is becoming increasingly popular. However, social media also enables the widespread of fake news. Because of its detrimental effects brought by social media, fake news detection has attracted increasing attention. However, the performance of detecting fake news only from news content is generally limited as fake news pieces are written to mimic true news. In the real world, news pieces spread through propagation networks on social media. The news propagation networks usually involve multi-levels. In this paper, we study the challenging problem of investigating and exploiting news hierarchical propagation network on social media for fake news detection. In an attempt to understand the correlations between news propagation networks and fake news, first, we build a hierarchical propagation network from macro-level and micro-level of fake news and true news; second, we perform a comparative analysis of the propagation network features of linguistic, structural and temporal perspectives between fake and real news, which demonstrates the potential of utilizing these features to detect fake news; third, we show the effectiveness of these propagation network features for fake news detection. We further validate the effectiveness of these features from feature important analysis. Altogether, this work presents a data-driven view of hierarchical propagation network and fake news and paves the way towards a healthier online news ecosystem.Comment: 10 page

Partnerships for Scaling Climate-Smart Agriculture (P4S) Phase II: 2019 annual report

First annual report of P4

A Bayesian space-time model for discrete spread processes on a lattice

Funding for this work was provided by GEOIDE through the Government of Canada’s Networks for Centres of Excellence program.In this article we present a Bayesian Markov model for investigating environmental spread processes. We formulate a model where the spread of a disease over a heterogeneous landscape through time is represented as a probabilistic function of two processes: local diffusion and random-jump dispersal. This formulation represents two mechanisms of spread which result in highly peaked and long-tailed distributions of dispersal distances (i.e., local and long-distance spread), commonly observed in the spread of infectious diseases and biological invasions. We demonstrate the properties of this model using a simulation experiment and an empirical case study - the spread of mountain pine beetle in western Canada. Posterior predictive checking was used to validate the number of newly inhabited regions in each time period. The model performed well in the simulation study in which a goodness-of-fit statistic measuring the number of newly inhabited regions in each time interval fell within the 95% posterior predictive credible interval in over 97% of simulations. The case study of a mountain pine beetle infestation in western Canada (1999-2009) extended the base model in two ways. First, spatial covariates thought to impact the local diffusion parameters, elevation and forest cover, were included in the model. Second, a refined definition for translocation or jump-dispersal based on mountain pine beetle ecology was incorporated improving the fit of the model. Posterior predictive checks on the mountain pine beetle model found that the observed goodness-of-fit test statistic fell within the 95% posterior predictive credible interval for 8 out of 10. years. The simulation study and case study provide evidence that the model presented here is both robust and flexible; and is therefore appropriate for a wide range of spread processes in epidemiology and ecology.PostprintPeer reviewe