Species across North America are being impacted by changing climate conditions. Plants and animals can respond to these changes in a variety of ways, including by shifting their geographic distributions. Determining whether or not observed biological changes, such as range shifts, are indeed the result of climate change is a key challenge facing natural resource managers and requires clarifying which areas have experienced detectable and significant changes in climate variables (such as monthly mean temperature or extreme precipitation). The objective of this study is to identify areas across North America that have (or have not) experienced detectable changes in ecologically-relevant climate variables. The overall goal of this work [...]
Summary
Species across North America are being impacted by changing climate conditions. Plants and animals can respond to these changes in a variety of ways, including by shifting their geographic distributions. Determining whether or not observed biological changes, such as range shifts, are indeed the result of climate change is a key challenge facing natural resource managers and requires clarifying which areas have experienced detectable and significant changes in climate variables (such as monthly mean temperature or extreme precipitation).
The objective of this study is to identify areas across North America that have (or have not) experienced detectable changes in ecologically-relevant climate variables. The overall goal of this work is to improve the quality and efficiency of scientific investigations into the effects of climate change on biodiversity by providing a means for scientists to focus their analyses on areas that are truly experiencing significant changes. Project researchers will compare documented cases of species range shifts with global climate models to determine whether range shifts were the result of natural climate variability, or were in response to a level of climate variability that exceeds what is considered normal.
This project will provide natural resource managers with information on why, when, how, and where plants and animals are moving in response to changing climate conditions. This study provides an opportunity to improve species status and trends assessments by reducing uncertainty about whether observed biological trends are responding to ecological changes outside of the range of natural variability. This science will help to inform management and conservation decisions concerning plant and animal populations and their ability to adapt to changing conditions.
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BlueRidgePkwy_NC_AlanCressler.jpg “Blue Ridge Parkway, NC - Credit: Alan Cressler”
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A key challenge for natural resource/conservation planning under climate change is understanding whether, or how strongly observed biological changes over some period of time are driven by climatic changes. Furthermore, if the observed biological changes are correlated or attributable to short-term climatic changes, but the climate trends are themselves indistinguishable from the background ‘internal climate variability’ (ICV), then it is difficult to infer whether the observed biological changes are actually different than what has occurred in the past; complicating adaptation efforts that require scientific knowledge of the ways in which the biota of interest are likely to respond to future climate change. Thus, detection of a biological signal that is attributable to climatic changes first requires an assessment of the detectability of the relevant climate change signals. The objective of this study is to identify areas across North America that have (or have not) experienced detectable changes in ecologically/biologically-relevant climate variables. The goal is to improve the quality and efficiency of investigations into climate change effects on biodiversity by providing a means to narrow such analyses to areas that truly experienced detectable and biologically-relevant environmental changes. To do this we will use existing publicly-available observational and global climate model (GCM) datasets to estimate the range of ICV, and then test whether observed trends exceeded the estimated level of background variability or ‘noise’. We will leverage recent advancements in signal detection and ICV estimation in order to: 1) better understand climate trend uncertainty due to ICV, and 2) estimate the Time of Emergence (ToE) of any ecologically/biologically-relevant climate trends in the recent past and near-term future. Our analysis will include a systematic evaluation of 50-year trends from 1966-2015 using historical GCM experiments within the Coupled Model Intercomparison Project phase 5 (CMIP5) and a 40-member large ensemble from a single GCM known as the CESM large ensemble (CESM-LE). Additionally, high-resolution Regional Climate Model (RCM) experiments will be used to estimate the sensitivity of the upper and lower bounds of ICV and improve our understanding of gaps in the ability of the current generation of GCMs to resolve trend detection at scales relevant to biodiversity. Because there are a wide range of species’ responses and sensitivities to many different climate variables and climate thresholds, we will apply our method to a large suite of climate variables and indices in collaboration with USGS scientists. This study provides an opportunity to improve species status and trends assessments by reducing uncertainty about whether observed biological trends are potentially outside of the range of natural ecological variability (for the portion of variability that is influenced by climatic changes). This will allow scientists to assess evidence for and against climate change-biodiversity hypotheses. The expected results from this study include original datasets that can be used to quickly assess whether ecologically-relevant climate trends are present. Expected maps and datasets will include spatially-explicit signal-to-noise ratio maps, and time of emergence of significant ecologically-relevant climate trends. Maps and data will be made publicly available through ScienceBase. We will partner with USGS scientists Dr. Shawn Carter, Madeleine Rubenstein, and Sarah Weiskopf to align climate variables/metrics with an accompanying meta-analysis of documented species’ range shifts.