RDMapper is a web-based application that allows rangeland forage production to be tracked bi-weekly using remotely sensed MODIS vegetation indices. Combining these data with monthly precipitation data and annual on-the-ground monitoring records, RDMapper provides statistical and graphical information and context that supports predictions of end-of-season residual dry matter (RDM). RDM is a landscape-scale metric that has been shown to be a good predictor of rangeland productivity and overall rangeland condition, and is used by grazing managers to monitor grazing impacts, and by land trusts and agencies for conservation easement compliance monitoring. RDMapper can help reduce grazing-related compliance issues and potential conflict between landowners and easement holders, strengthening the overall relationship with cooperating landowners and leading to greater protection of biodiversity values. We developed RDMapper using software written in the R programming language, including the Shiny package. In 2015, we predicted compliance with RDM objectives across approximately 44,000 hectares of conservation easement lands held by The Nature Conservancy in California. We based predictions on past RDM compliance and our interpretation of statistics and graphics that demonstrate differences in vegetation indices and precipitation characteristics for years that were in versus out of compliance with RDM objectives. We tested a framework for adding efficiency to field-based RDM monitoring in future years by evaluating pastures that we had a high confidence would be in RDM compliance in 2015. For these pastures, our prediction of ‘in compliance with high confidence’ was correct on 109 of 110 pastures. We propose that in future years, pastures we are confident will be in compliance can be monitored with a simple visual estimate of RDM, instead of more expensive field methods. We are currently testing RDMapper at additional properties and have transitioned it to a more powerful data processing framework based on MODIS and PRISM web services and Google’s Earth Engine.
The Nature Conservancy
Systematic conservation planning is the science of understanding which conservation interventions to enact, and when and where to do them given limited conservation budgets and the diverging needs of different stakeholders. This approach is fundamental to modern evidence-based conservation. In this workshop we’ll learn about the fundamental principles of systematic conservation planning, and discuss some examples of where it has been applied. This will be concreted with some simple tutorials using Marxan.
In high stress environments, such as deserts, positive interactions among plants maintain biodiversity and productivity. However, the role and mechanism of these positive interactions changes depending on the context of spatial scale.
I will discuss how positive plant-interactions in deserts change at the micro, local, and regional scale. I also discuss the challenges associated with examining plant interactions at different spatial scales as a researcher, such as sampling techniques, community structure, and interacting factors.
Typically studies examining positive plant-interactions focus on local gradients, thereby neglecting micro or regional scales. All spatial scales share similarities in that each have gradients that modifies the mechanism, magnitude, and direction of plant interactions. Drawing parallels among different spatial scales and considering all three simultaneously as a response surface can provide a better understanding of positive interactions. This can assist conservation biologist and restoration ecologists make better informed decisions when managing desert ecosystems in support of global biodiversity.
Achieving a sustainable industrial economic system is the defining challenge of our age, one that requires understanding both of human-made systems that generate stresses on the environment and of the natural systems that absorb them. Industrial ecology (IE) is a synthesis field that seeks to understand the sustainability implications of decisions made in the context of human systems (businesses, households, public agencies). The main organizing principle in IE research is the boundary that separates the natural environment from the domain of human activity (aka the “technosphere”). Though natural systems are necessarily spatial, human systems are often more readily thought of as graphs, where different activities happen at distinct points in order to satisfy demand for products and services in the economy.
I will introduce the core methodologies of IE, material flow analysis and life cycle assessment, describe data collection and analysis in comparison to the natural sciences, and discuss how the operational concerns of businesses influence how IE investigations are designed and how knowledge is shared.
Brandon Kuczenski, Ph.D.
University of California at Santa Barbara
Institute for Social, Behavioral, and Environmental Research
Santa Barbara, CA 93106-5131