Most projects in Landscape Ecology, at some point, define a species-habitat association. These models are inherently spatial, dealing with landscapes and their configurations. Whether coding behavioral rules for dispersal of simulated organisms through simulated landscapes, or designing the sampling extent of field surveys and experiments in real landscapes, landscape ecologists must make assumptions about how organisms experience and utilize the landscape. These convenient working postulates allow modelers to project the model in time and space, yet rarely are they explicitly considered. The early years of landscape ecology necessarily focused on the evolution of effective data sources, metrics, and statistical approaches that could truly capture the spatial and temporal patterns and processes of interest. Now that these tools are well established, we reflect on the ecological theories that underpin the assumptions commonly made during species distribution modeling and mapping. This is crucial for applying models to questions of global sustainability. Due to the inherent use of GIS for much of this kind of research, and as several authors’ research involves the production of multicolored map figures, there would be an 8-page color insert. Additional color figures could be made available through a digital archive, or by cost contributions of the chapter authors. Where applicable, would be relevant chapters’ GIS data and model code available through a digital archive. The practice of data and code sharing is becoming standard in GIS studies, is an inherent method of this book, and will serve to add additional research value to the book for both academic and practitioner audiences.
Franklin summarises the methods used in species distribution modeling (also called niche modeling) and presents a framework for spatial prediction of species distributions based on the attributes (space, time, scale) of the data and ...
This book introduces the key stages of niche-based habitat suitability model building, evaluation and prediction required for understanding and predicting future patterns of species and biodiversity.
ÁÀ n j1⁄41n w ij dðÀ Þ zi ̄z zj À ̄z Á . IdðÞ1⁄4W dðÞ P n i1⁄41 P P i1⁄41n z i À À ̄z À Á2 ð5.4Þ Notice how similar Moran's I and Pearson correlation coefficients are: in essence Moran's I(d) is a Pearson's coefficient computed for one ...
Predictions about where different species are, where they are not, and how they move across a landscape or respond to human activities -- if timber is harvested, for instance, or stream flow altered -- are important aspects of the work of ...
Alternative futures for Monroe County, Pennsylvania: A case study in applying ecological principles. Pages 165–193 in V. H. Dale and R. Haeuber, editors. Applying ecological principles to land management. Springer-Verlag, New York, ...
National Academies of Sciences, Engineering, and Medicine. 2020. The impacts of racism and bias on black people pursuing careers in science, engineering, and medicine: proceedings of a workshop. Washington, DC: The National Academies ...
Currently, many ecologists do not utilize machine learning as a part of the scientific process. This volume highlights how machine learning techniques can complement the traditional methodologies currently applied in this field.
This book describes the myriad components of the Hindu Kush-Himalaya (HKH) region.
In this new text book this new field of landscape ecology is given the first fully integrated treatment suitable for the student.
Table 13.2 Coastal and marine ecosystem types of New England, with brief descriptions Ecosystem Description ... tidal zone Subtidal coastal Eelgrass Near-shore subtidal habitat dominated by Zostera marina Algal zone Near-shore subtidal ...