Wildlife ecologists frequently make use of limited information on locations of a species of interest in combination with readily available GIS data to build models to predict space use. In addition to a wide range of statistical data models that are more commonly used, machine learning approaches provide another means to develop predictive spatial models. However, comparison of output from these two families of models for the same data set is not often carried out. It is important that wildlife managers understand the pitfalls and limitations when a single set of models is used with limited GIS data to try to predict and understand species distribution. To illustrate this, we carried out two sets of models (generalized linear mixed models (GLMMs) and boosted regression trees (BRTs)) to predict geographic occupancy of the eastern coyote (Canis latrans) on the island of Newfoundland, Canada. This exercise is illustrative of common spatial questions in wildlife research and management. Our results show that models vary depending on the approach (GLMM vs. BRT) and that, overall, BRT had higher predictive ability. Although machine learning has been criticized because it is not explicitly hypothesis-driven, it has been used in other areas of spatial modelling with success. Here, we demonstrate that it may be a useful approach for predicting wildlife space use and to generate hypotheses when data are limited. The results of this comparison can help to improve other models for species distributions and also guide future sampling and modelling initiatives. 相似文献
Abstract One of the primary missions of our university is to train future primary and secondary teachers. Geospatial sciences, including GIS, have long been excluded from teacher education curriculum. This article explains the curriculum revisions undertaken to increase the geospatial technology education of future teachers. A general education class introducing geospatial technology to the general student body has been developed, a cartography class has been modified to provide applied geospatial experience explicitly for future teachers, and a service learning partnership with local K–12 schools has been established where students are working with teachers to integrate geospatial sciences in their academic programs. 相似文献
The ongoing devolution of climate policy-making to sub-national levels has prompted growing interest in policy entrepreneurship by individuals who are politically and technically creative and institutionally resourceful. This paper investigates the case of the materials-management programme in the Oregon Department of Environmental Quality which has emerged as a national and international leader by focusing on the role of household consumption in greenhouse gas (GHG) emissions. Two noteworthy innovations involve the development of a consumption-based GHG emissions inventory and introduction of policies aimed at facilitating construction of small homes (so-called Accessory Dwelling Units, ADU). The case traces over several decades the higher order learning processes within the group and their entrepreneurship toward affecting broader changes in emission accounting and climate policies in Oregon. The paper identifies the enabling factors for these innovations, and considers: how to create the conditions for learning, experimentation, and policy entrepreneurship; how to reproduce these conditions in different locales; and how to recognize and foster innovations that arise outside the established mainstream ‘climate community’. It also stresses the benefits of breaking down the barriers between science-based analysis and policy. The two questions frequently raised in the climate policy debate – how to bring researchers and practitioners together to develop efficacious policies; and how to replicate successful programmes and policies across different communities, jurisdictions, and locations – should be re-examined. It may be more appropriate to ask instead: How to create conditions for learning, experimentation, and policy entrepreneurship; and how to reproduce these conditions in different locales.
Key policy insights
Using a consumption-based greenhouse gas emission inventory instead of a sector-based inventory radically changes climate policy priorities, shifting the emphasis from technological fixes to curbing household consumption.
Policy innovations thrive in teams that combine technical and scientific competencies with: a commitment to addressing societal problems; interest in inquiry, experimentation, and learning; entrepreneurship; and strategic and political savvy.
These qualities require breaking down artificial barriers between science and policy.
Transformative policy ideas can originate within institutional nodes that operate outside of an established community of expertise and authority; and these should be identified and fostered.
Primary productivity of ecosystem is important indicator about ecological assessment. Remote sensing technology has been used to monitor net primary productivity (NPP) of ecological system for several years. In this paper, the remotely sensed NPP simulation model of alpine vegetation in Qinghai Province of Tibet Plateau was set up based on the theory of light use efficiency. Firstly a new approach based on mixed pixels and Support Vector Machine (SVM) algorithm were used to correct simulated NPP values derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Finally, spatial distribution and monthly variation characteristics of NPP in Qinghai Province detail. The result showed in 2006 were analyzed in that NPP of vegetation in Qinghai Province in 2006 ranged from o to 422 gC/m2/a and the average NPP was 151 gC/m2/a. NPP gradually increased from northwest to southeast. NPP of different vegetation types were obviously different. The average NPP of broad-leaved forest was the largest (314 gC/m2/a), and sparse shrub was the smallest (101 gC/m2/a). NPP in Qinghai Province significantly changed with seasonal variation. The accumulation of NPP was primarily in the period (from April to September) with better moist and heat conditions. In July, the average NPP of vegetation reached the maximum value (43 gC/m2). In our model, the advantage of traditional LUE models was adopted, and our study fully considered typicalcharacteristics of alpine vegetation light use efficiency and environmental factors in the study area. Alpine vegetation is the most important ecological resource of Tibet Plateau, exactly monitoring its NPP value by remote sensing is an effective protection measure. 相似文献