Landscape Partnership Resources Library
Assumptions
From the Imperiled Aquatic Species Conservation Strategy for the Upper Tennessee River Basin.
Species and Threats
From the Imperiled Aquatic Species Conservation Strategy for the Upper Tennessee River Basin.
Introduction and Geographic Scope
From the Imperiled Aquatic Species Conservation Strategy for the Upper Tennessee River Basin.
Purpose
From the Imperiled Aquatic Species Conservation Strategy for the Upper Tennessee River Basin.
Cave/Karst Resources Across the Appalachian LCC: A Visual Guide to Results PDF
This visual guide documents 18 months of work gathering and analyzing data on caves and karst resources in the Appalachian LCC. The maps and files provide a comprehensive overview of data available for examining relationships between environmental factors and biological diversity and distribution within karst areas in the region. This visual survey is intended to be a guide to what the researchers have accomplished, and a guide to what new questions and results would be interesting to end-users.
Guidelines for Using the NatureServe Climate Change Vulnerability Index
Motivated by the need to rapidly assess the vulnerability of species to climate change, NatureServe developed a Climate Change Vulnerability Index. The Index uses a scoring system that integrates a species’ predicted exposure to climate change within an area and three sets of factors associated with climate change sensitivity, each supported by published studies: 1) indirect exposure to climate change, 2) species-specific sensitivity and adaptive capacity factors and 3) documented response to climate change. Our primary goal for the Index is to provide valuable input for key planning documents, such as revisions of state wildlife action plans, to allow consideration of climate change impacts together with other stressors. We also hope this tool will help land managers develop and prioritize strategies for climate change adaptation that lead to actions that increase the resilience of species to climate change.
Guidelines for Using the NatureServe Climate Change Vulnerability Index
Motivated by the need to rapidly assess the vulnerability of species to climate change, NatureServe developed a Climate Change Vulnerability Index. The Index uses a scoring system that integrates a species’ predicted exposure to climate change within an area and three sets of factors associated with climate change sensitivity, each supported by published studies: 1) indirect exposure to climate change, 2) species-specific sensitivity and adaptive capacity factors and 3) documented response to climate change. Our primary goal for the Index is to provide valuable input for key planning documents, such as revisions of state wildlife action plans, to allow consideration of climate change impacts together with other stressors. We also hope this tool will help land managers develop and prioritize strategies for climate change adaptation that lead to actions that increase the resilience of species to climate change.
Diffusion into new markets: evolving customer segments in the solar photovoltaics market
The US residential solar market is growing quickly, and as solar adoption diffuses into new populations, later adopters may differ significantly from earlier ones. Using a unique household-level survey dataset including 1234 adopters and 790 non-adopters from San Diego County, California, we explore differences in attitudinal and socio-economic factors for three groups: (i) adopters and non- adopters; (ii) early and more recent adopters; (iii) consumers adopting via buying or leasing. Our results suggest that adopters overall have higher incomes, are more educated, live in larger homes, and expect to stay in their homes for longer than their non-adopting peers. They also differ in their expectations of electricity retail rate changes and the impact solar could have on their home resale value. When examining differences between early and more recent adopters, we find that recent adopters are more representative of general homeowners and more politically moderate. They are also increasingly installing solar to protect against future electricity price increases and to lower electricity costs as opposed to adopting strictly for environmental reasons. Furthermore, more recent adopters differ significantly from earlier adopters in the situations that prompted them to adopt. The findings demonstrate how solar markets are evolving, reflecting changes in the underlying drivers of consumer adoption as well as innovative solar marketing strategies.
Seasonal weather patterns drive population vital rates and persistence in a stream fish
Here, we investigated effects of seasonal air temperature and precipitation (fall, winter, and spring) on survival and recruitment of brook trout (Salvelinus fontinalis) at a broad spatial scale using a novel stage-structured population model. The data were a 15-year record of brook trout abundance from 72 sites distributed across a 170-km-long mountain range in Shenandoah National Park, Virginia, USA. Population vital rates responded differently to weather and site-specific conditions. Specifically, young-of-year survival was most strongly affected by spring temperature, adult survival by elevation and per-capita recruitment by winter precipitation. Low fall precipitation and high winter precipitation, the latter of which is predicted to increase under climate change for the study region, had the strongest negative effects on trout populations. Simulations show that trout abundance could be greatly reduced under constant high winter precipitation, consistent with the expected effects of gravel-scouring flows on eggs and newly hatched individuals. However, high-elevation sites would be less vulnerable to local extinction because they supported higher adult survival. Furthermore, the majority of brook trout populations are projected to persist if high winter precipitation occurs only intermittently (≤3 of 5 years) due to density-dependent recruitment. Variable drivers of vital rates should be commonly found in animal populations characterized by ontogenetic changes in habitat, and such stage-structured effects may increase population persistence to changing climate by not affecting all life stages simultaneously. Yet, our results also demonstrate that weather patterns during seemingly less consequential seasons (e.g., winter precipitation) can have major impacts on animal population dynamics.
Projected increase in lightning strikes in the United States due to global warming
Lightning plays an important role in atmospheric chemistry and in the initiation of wildfires, but the impact of global warming on lightning rates is poorly constrained. Here we propose that the lightning flash rate is proportional to the convective available potential energy (CAPE) times the precipitation rate. Using observations, the product of CAPE and precipitation explains 77% of the variance in the time series of total cloud-to- ground lightning flashes over the contiguous United States (CONUS). Storms convert CAPE times precipitated water mass to discharged lightning energy with an efficiency of 1%. When this proxy is applied to 11 climate models, CONUS lightning strikes are predictedto increase 12 +_ 5% per degree Celsius of global warming and about 50% over this century
The future of farming: return to roots?
Large scale farming would be more sustainable if major crop plants lived for years and built deep root systems.
Sustainable Development under Population Pressure: Lessons from Developed Land Consumption in the Conterminous U.S.
Population growth will result in a significant anthropogenic environmental change worldwide through increases in developed land (DL) consumption. DL consumption is an important environmental and socioeconomic process affecting humans and ecosystems. Attention has been given to DL modeling inside highly populated cities. However, modeling DL consump- tion should expand to non-metropolitan areas where arguably the environmental consequences are more significant. Here, we study all counties within the conterminous U.S. and based on satellite-derived product (National Land Cover Dataset 2001) we calculate the associated DL for each county. By using county population data from the 2000 census we present a comparative study on DL consumption and we propose a model linking population with expected DL consumption. Results indicate distinct geographic patterns of comparatively low and high consuming counties moving from east to west. We also demonstrate that the relationship of DL consumption with population is mostly linear, altering the notion that expected population growth will have lower DL consumption if added in counties with larger population. Added DL consumption is independent of a county’s starting population and only dependent on whether the county belongs to a Metropolitan Statistical Area (MSA). In the overlapping MSA and non-MSA population range there is also a constant DL efficiency gain of approximately 20km2 for a given population for MSA counties which suggests that transitioning from rural to urban counties has significantly higher benefits in lower populations. In addition, we analyze the socioeconomic composition of counties with extremely high or low DL consumption. High DL consumption counties have statistically lower Black/ African American population, higher poverty rate and lower income per capita than average in both NMSA and MSA counties. Our analysis offers a baseline to investigate further land consumption strategies in anticipation of growing population pressures.
The cold-water climate shield: delineating refugia for preserving salmonid fishes through the 21st century
The distribution and future fate of ectothermic organisms in a warming world will be dictated by thermal-scapes across landscapes. That is particularly true for stream fishes and cold-water species like trout, salmon, and char that are already constrained to high elevations and latitudes. The extreme climates in those environments also preclude invasions by most non-native species, so identifying especially cold habitats capable of absorbing future climate change while still supporting native populations would highlight important refugia. By coupling crowd-sourced biological datasets with high-resolution stream temperature scenarios, we delineate network refugia across >250 000 stream km in the Northern Rocky Mountains for two native salmonids—bull trout (BT) and cutthroat trout (CT). Under both moderate and extreme climate change scenarios, refugia with high probabilities of trout population occupancy (>0.9) were predicted to exist (33–68 BT refugia; 917–1425 CT refugia). Most refugia are on public lands (>90%) where few currently have protected status in National Parks or Wilderness Areas (<15%). Forecasts of refuge locations could enable protection of key watersheds and provide a foundation for climate smart planning of conservation networks. Using cold water as a ‘climate shield’ is generalizable to other species and geographic areas because it has a strong physiological basis, relies on nationally available geospatial data, and mines existing biological datasets. Importantly, the approach creates a framework to integrate data contributed by many individuals and resource agencies, and a process that strengthens the collaborative and social networks needed to preserve many cold-water fish populations through the 21st century.
Regional variability in extinction thresholds for forest birds in the north-eastern United States: an examination of potential drivers using long-term breeding bird atlas datasets
Main conclusions: Extinction threshold estimates varied tremendously across species and landscapes. Thus, habitat thresholds are difficult to generalize as they depend on many factors beyond landscape fragmentation and habitat availability (e.g. landscape characteristics such as matrix quality). Our findings highlight the need to avoid oversimplification and generalization of habitat thresholds, especially as they might prove counterproductive to conservation efforts. Instead, we propose that we evaluate thresholds for individual species – preferably using species-centred habitat definitions in threshold modelling – to derive generalities for ecological and conservation applications.
Herb layer extinction debt in highly fragmented temperate forests – Completely paid after 160 years?
The time-delayed extinction of plant species following habitat fragmentation is a well-known phenomenon in ecology. The length of the relaxation time until this ‘extinction debt’ is paid (i.e., until extinctions cease) depends on the ecosystem, species group and extent of fragmentation. Studies of grassland ecosystems have revealed that plant extirpations after fragmentation can occur rapidly when the degree of frag- mentation is high. Studies of extinction debt in highly fragmented forests, however, are lacking. In this study, we evaluated the existence of an extinction debt in the Prignitz, Brandenburg, Germany, where 94% of the semi-natural forests have vanished since 1780. We surveyed the herb-layer species of 104 forest patches and fitted species richness as a function of the historical and present-day patch configurations. Models including the present-day habitat area and connectivity explained the present day species richness better than models including historical patch-configuration variables. There was no significant effect of the historical habitat area on the present day species richness. However, the effect of historical patch connectivity on the richness of forest specialists with short-distance dispersal potential was significant when excluding present-day habitat area from the models and habitat quality and heterogeneity were used as covariables. The extinction debt has largely been paid after approximately 160 years of relaxation time which contrasts with previous studies of temperate forests that have found extinction debts persisting 120–225 years after fragmentation. We demonstrate that extinction debts in temperate forests may be paid off more rapidly if the degree of fragmentation is high.
Divergent phenological response to hydroclimate variability in forested mountain watersheds
Mountain watersheds are primary sources of freshwater, carbon sequestration, and other ecosystem services. There is significant interest in the effects of climate change and variability on these processes over short to long time scales. Much of the impact of hydroclimate variability in forest ecosystems is manifested in vegetation dynamics in space and time. In steep terrain, leaf phenology responds to topoclimate in complex ways, and can produce specific and measurable shifts in landscape forest patterns. The onset of spring is usually delayed at a specific rate with increasing elevation (often called Hopkins’ Law; Hopkins, 1918), reflecting the dominant controls of temperature on greenup timing. Contrary with greenup, leaf senescence shows inconsistent trends along elevation gradients. Here, we present mechanisms and an explanation for this variability and its significance for ecosystem patterns and services in response to climate. We use moderate-resolution imaging spectro-radiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data to derive landscape-induced phenological patterns over topoclimate gradients in a humid temperate broadleaf forest in southern Appalachians. These phenological patterns are validated with different sets of field observations. Our data demonstrate that divergent behavior of leaf senescence with elevation is closely related to late growing season hydroclimate variability in temperature and water balance patterns. Specifically, a drier late growing season is associated with earlier leaf senescence at low elevation than at middle elevation. The effect of drought stress on vegetation senescence timing also leads to tighter coupling between growing season length and ecosystem water use estimated from observed precipitation and runoff generation. This study indicates increased late growing season drought may be leading to divergent ecosystem response between high and low elevation forests. Landscape-induced phenological patterns are easily observed over wide areas and may be used as a unique diagnostic for sources of ecosystem vulnerability and sensitivity to hydroclimate change.
DATA MINING TO ESTIMATE BROILER MORTALITY WHEN EXPOSED TO HEAT WAVE
Heat waves usually result in losses of animal production since they are exposed to thermal stress inducing an increase in mortality and consequent economical losses. Animal science and meteorological databases from the last years contain enough data in the poultry production business to allow the modeling of mortality losses due to heat wave incidence. This research analyzes a database of broiler production associated to climatic data, using data mining techniques such as attribute selection and data classification (decision tree) to model the impact of heat wave incidence on broiler mortality. The temperature and humidity index (THI) was used for screening environmental data. The data mining techniques allowed the development of three comprehensible models for estimating specifically high mortality during broiler production. Two models yielded a classification accuracy of 89.3% by using Principal Component Analysis (PCA) and Wrapper feature selection approaches. Both models obtained a class precision of 0.83 for classifying high mortality. When the feature selection was made by the domain experts, the model accuracy reached 85.7%, while the class precision of high mortality was 0.76. Meteorological data and the calculated THI from meteorological stations were helpful to select the range of harmful environmental conditions for broilers 29 and 42 days old. The data mining techniques were useful for building animal production models.
Heat stress related dairy cow mortality during heat waves and control periods in rural Southern Ontario from 2010–2012
Background: Heat stress is a physiological response to extreme environmental heat such as heat waves. Heat stress can result in mortality in dairy cows when extreme heat is both rapidly changing and has a long duration. As a result of climate change, heat waves, which are defined as 3 days of temperatures of 32 °C or above, are an increasingly frequent extreme weather phenomenon in Southern Ontario. Heat waves are increasing the risk for on-farm dairy cow mortality in Southern Ontario. Heat stress indices (HSIs) are generally based on temperature and humidity and provide a relative measure of discomfort which can be used to predict increased risk of on-farm dairy cow mortality. In what follows, the heat stress distribution was described over space and presented with maps. Similarly, on-farm mortality was described and mapped. The goal of this study was to demonstrate that heat waves and related HSI increases during 2010–2012 were associated with increased on-farm dairy cow mortality in Southern Ontario. Mortality records and farm locations for all farms registered in the CanWest Dairy Herd Improvement Program in Southern Ontario were retrieved for 3 heat waves and 6 three-day control periods from 2010 to 2012. A random sample of controls (2:1) was taken from the data set to create a risk-based hybrid design. On-farm heat stress was estimated using data from 37 weather stations and subsequently interpolated across Southern Ontario by geostatistical kriging. A Poisson regression model was applied to assess the on-farm mortality in relation to varying levels of the HSI. Results: For every one unit increase in HSI the on-farm mortality rate across Southern Ontario increases by 1.03 times (CI95% (IRR) = (1.025,1.035); p = ≤ 0.001). With a typical 8.6 unit increase in HSI from a control period to a heat wave, mortality rates are predicted to increase by 1.27 times. Conclusions: Southern Ontario was affected by heat waves, as demonstrated by high levels of heat stress and increased on-farm mortality. Farmers should be aware of these risks, and informed of appropriate methods to mitigate such risks.
Coupled catastrophes: sudden shifts cascade and hop among interdependent systems
From the Introduction: Sudden changes propagating among coupled systems pose a significant scientific challenge in many disciplines, yet we lack an adequate mathematical understanding of how local sudden changes spread [1]. The Earth’s biosphere, for example, appears to be approaching several planetary-scale sudden changes triggered by human activity, including species extinction, desertification and lake eutrophication, which spread from one spatial patch to another [1]. That spatial spread not only poses dangers but also opportunities for detecting early warning signs [2–4]. Socioeconomic systems have examples, too: booms and busts in business cycles in different economies appear to be synchronizing because of trade, financial and other linkages [5–8]. Poverty traps at multiple scales seem to be coupled [9]. Abrupt declines in an asset price can trigger sharp declines in confidence and fire sales of other assets, as occurred in the 2007–2008 global financial crisis [10]. Protests and social uprisings appear to spread contagiously among countries, with one protest seeming to inspire others via news and social media [11,12].
Asynchronous Online Foresight Panels: The Case of Wildfire Management
Framing the wildfire situation as a social trap emerged early in the Round 1 discussion and this topic was deemed important enough to merit its own discussion thread.
