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Macroscale studies of aquatic ecosystems are needed to address many contemporary broad‐scale problems related to global change, particularly to inform national‐scale environmental policies. In this data paper, we fill two important gaps in data availability for lakes at the scale of the conterminous U.S., the lack of: (1) high‐resolution geographic representations of lakes and their watersheds and (2) an open data platform to connect disparate U.S. national‐scale lake datasets. We describe the LAGOS‐US LOCUS v1.0 data module that includes a detailed User Guide, which is part of the LAGOS‐US extensible research‐ready open data platform. This platform can be used to study the 479,950 lakes and reservoirs larger than or equal to 1 ha across the entire conterminous U.S. at multiple scales of space and time. The LOCUS module includes spatially‐explicit information on lakes and their watersheds, including location information, physical characteristics, and identifiers of other important national‐scale datasets.

Near-term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance introduced by different sources, including specification of the model structure, errors in driver data, and estimation of current states (initial conditions). Uncertainty partitioning could be particularly useful in improving forecasts of highly variable cyanobacterial densities, which are difficult to predict and present a persistent challenge for lake managers. As cyanobacteria can produce toxic and unsightly surface scums, advance warning when cyanobacterial densities are increasing could help managers mitigate water quality issues. Here, we fit 13 Bayesian state-space models to evaluate different hypotheses about cyanobacterial densities in a low nutrient lake that experiences sporadic surface scums of the toxin-producing cyanobacterium, Gloeotrichia echinulata. We used data from several summers of weekly cyanobacteria samples to identify dominant sources of uncertainty for near-term (1- to 4-week) forecasts of G. echinulata densities. Water temperature was an important predictor of cyanobacterial densities during model fitting and at the 4-week forecast horizon. However, no physical covariates improved model performance over a simple model including the previous week's densities in 1-week-ahead forecasts. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and did not capture rare peak occurrences, indicating that significant explanatory variables when fitting models to historical data are not always effective for forecasting. Uncertainty partitioning revealed that model process specification and initial conditions dominated forecast uncertainty. These findings indicate that long-term studies of different cyanobacterial life stages and movement in the water column as well as measurements of drivers relevant to different life stages could improve model process representation of cyanobacteria abundance. In addition, improved observation protocols could better define initial conditions and reduce spatial misalignment of environmental data and cyanobacteria observations. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems. Auburn Water District/Lewiston Water Division; Bates College; Dartmouth College; Division of Biological Infrastructure [0434684, 1933016]; Division of Computer and Network Systems [1737424]; Division of Emerging Frontiers [0842112, 0842125, 0842267]; Division of Environmental Biology [0749022, 1010862, 1137327, 1638577, 1702991, 1753639, MSB-1638575, MSB-1926050]; Lake Sunapee Protective Assocation; Midge Eliassen Fellowship; NSF, United States National Science Foundation [CNS-1737424, DBI-0434684, DEB-0749022, DEB-1010862, EF-0842112, EF-0842125, EF-0842267, EF-1137327, EF-1638577, EF-1702991, ICER-1517823, MSB EF-1638575]; Virginia Polytechnic Institute and State University; Virginia Water Resources Research Center Published version Auburn Water District/Lewiston Water Division; Bates College; Dartmouth College; Division of Biological Infrastructure, Grant/Award Numbers: 0434684, 1933016; Division of Computer and Network Systems, Grant/Award Number: 1737424; Division of Emerging Frontiers, Grant/Award Numbers: 0842112, 0842125, 0842267; Division of Environmental Biology, Grant/Award Numbers: 0749022, 1010862, 1137327, 1638577, 1702991, 1753639, MSB-1638575, MSB-1926050; Lake Sunapee Protective Assocation; Midge Eliassen Fellowship; NSF, United States National Science Foundation, Grant/Award Numbers: CNS-1737424, DBI-0434684, DEB-0749022, DEB-1010862, EF-0842112, EF-0842125, EF-0842267, EF-1137327, EF-1638577, EF-1702991, ICER-1517823, MSB EF-1638575; Virginia Polytechnic Institute and State University; Virginia Water Resources Research Center

Aquatic scientists require robust, accurate information about nutrient concentrations and indicators of algal biomass in unsampled lakes in order to understand and predict the effects of global climate and land‐use change. Historically, lake and landscape characteristics have been used as predictor variables in regression models to generate nutrient predictions, but often with significant uncertainty. An alternative approach to improve predictions is to leverage the observed relationship between water clarity and nutrients, which is possible because water clarity is more commonly measured than lake nutrients. We used a joint‐nutrient model that conditioned predictions of total phosphorus, nitrogen, and chlorophyll a on observed water clarity. Our results demonstrated substantial reductions (8–27%; median = 23%) in prediction error when conditioning on water clarity. These models will provide new opportunities for predicting nutrient concentrations of unsampled lakes across broad spatial scales with reduced uncertainty.

Biodiversity-ecosystem functioning (BEF) theory has largely focused on species richness, although studies have demonstrated that evenness may have stronger effects. While theory and numerous small-scale studies support positive BEF relationships, regional studies have documented negative effects of evenness on ecosystem functioning. We analysed a lake dataset spanning the continental US to evaluate whether strong evenness effects are common at broad spatial scales and if BEF relationships are similar across diverse regions and trophic levels. At the continental scale, phytoplankton evenness explained more variance in phytoplankton and zooplankton resource use efficiency (RUE; ratio of biomass to resources) than richness. For individual regions, slopes of phytoplankton evenness-RUE relationships were consistently negative and positive for phytoplankton and zooplankton RUE, respectively, and most slopes did not significantly differ among regions. Findings suggest that negative evenness effects may be more common than previously documented and are not exceptions restricted to highly disturbed systems.

Biodiversity conservation for terrestrial species often emphasizes land protection to help maintain connectivity among habitat patches. However, conservation of aquatic and semi-aquatic species is challenging because aquatic species (e.g., fish) move among lakes using aquatic connections (e.g., streams, wetlands), whereas semi-aquatic species (e.g., amphibians) use both aquatic connections and upland habitats. We applied the patch-matrix model to create an aquatic and semi-aquatic connectivity framework for lakes. We applied our framework using lakes in Michigan, USA to examine (1) the relationship between aquatic and semi-aquatic connectivity for lakes and (2) the extent to which protected areas encompass aquatic and semi-aquatic connectivity among lakes. We used principal component analysis to calculate aquatic and semi-aquatic connectivity scores for lakes. We then examined relationships among aquatic and semi-aquatic connectivity scores and existing protected areas (strict and multi-use). Fewer than 3% of lakes had high scores for either aquatic or semi-aquatic connectivity. Connectivity scores were generally higher in Michigan’s Upper Peninsula, which is heavily forested with greater land protection. Although lake protection was overall low (16 and 32% of lake watersheds in Michigan were ≥ 10% protected under strict and multi-use protection, respectively), highly connected lakes were generally more protected than less connected lakes. We propose using our aquatic and semi-aquatic connectivity framework to (1) identify and prioritize lakes for conservation that are likely to have high biodiversity and conservation value and (2) generate testable hypotheses for studying the integrated terrestrial-aquatic landscape under global change.

Climate change has emerged as a major threat to biodiversity, but there have been few assessments of variability in sensitivity to climate across species’ ranges. We examined the sensitivity of ponderosa pine (Pinus ponderosa), a common, commercially important tree species found throughout the western United States, to climate variability using tree rings mostly from the International Tree Ring Data Bank (161 chronologies) and multiple climate products for 1930–1979. We compared interannual tree ring variation to water-year precipitation (wy ppt), May-July vapor pressure deficit (MJJ VPD), April-July precipitation (AMJJ ppt) and average winter (Dec-Feb) minimum temperature (winter tmn). We mapped growth-climate correlations for each variable and applied hierarchical agglomerative cluster analysis to identify five main groups of climate sensitivities, one of which contained mostly climate-insensitive populations. Ponderosa pine growth was positively correlated with wy ppt and AMJJ ppt and negatively correlated with MJJ VPD throughout its range, but sensitivities were greater in the Southern Rockies and Northern Great Plains than in Pacific and Southwestern populations. Sensitivities to winter tmn were weaker than responses to other climate variables, but Pacific and interior populations mostly demonstrated positive and negative responses, respectively. In summary, our analyses revealed the general pattern that interior populations of ponderosa pine were more climate-sensitive than Pacific populations, but there is considerable heterogeneity in sensitivity throughout the species’ range. Sensitivities to climate variables were related to position along corresponding climate gradients for all variables except MJJ VPD, but the unevenness of these relationships suggests a role of local adaptation. Our results illustrate the variation in climate response for a uniquely wide-ranging species and suggest that “leading” and “trailing” range edge concepts can oversimplify patterns of species exposure to climate change.

Although ecosystems respond to global change at regional to continental scales (i.e., macroscales), model predictions of ecosystem responses often rely on data from targeted monitoring of a small proportion of sampled ecosystems within a particular geographic area. In this study, we examined how the sampling strategy used to collect data for such models influences predictive performance. We subsampled a large and spatially extensive data set to investigate how macroscale sampling strategy affects prediction of ecosystem characteristics in 6,784 lakes across a 1.8-million-km2 area. We estimated model predictive performance for different subsets of the data set to mimic three common sampling strategies for collecting observations of ecosystem characteristics: random sampling design, stratified random sampling design, and targeted sampling. We found that sampling strategy influenced model predictive performance such that (1) stratified random sampling designs did not improve predictive performance compared to simple random sampling designs and (2) although one of the scenarios that mimicked targeted (non-random) sampling had the poorest performing predictive models, the other targeted sampling scenarios resulted in models with similar predictive performance to that of the random sampling scenarios. Our results suggest that although potential biases in data sets from some forms of targeted sampling may limit predictive performance, compiling existing spatially extensive data sets can result in models with good predictive performance that may inform a wide range of science questions and policy goals related to global change.

International audience; In mountain landscapes, surface temperatures vary over short distances due to interacting influences of topography and overstory vegetation on local energy and water balances. At two study landscapes in the Sierra Nevada of California, characterized by foothill oak savanna at 276–481 m elevation and montane conifer forest at 1977–2135 m, we deployed 288 near-surface (5 cm above the surface) temperature sensors to sample site-scale (30 m) temperature variation related to hillslope orientation and vegetation structure and microsite-scale (2–10 m) variation related to microtopography and tree overstory. Daily near-surface maximum and minimum temperatures for the 2013 calendar year were related to topographic factors and vegetation overstory characterized using small footprint LiDAR imagery acquired by the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP). At both landscapes we recorded large site and microsite spatial variation in daily maximum temperatures, and less absolute variation in daily minimum temperatures. Generalized boosted regression trees were estimated to measure the influence of tree canopy density, understory solar radiation, cold-air drainage and pooling, ground cover and microtopography on daily maximum and minimum temperatures at site and microsite scales. Site-scale models based on indices of understory solar radiation and landscape position explained an average of 61–65% of daily variation in maximum temperature; site-scale models based on tree canopy density and landscape position explained 65–83% of variation in minimum temperatures. Models explained

Lakes are active processors of organic carbon (OC) and play important roles in landscape and global carbon cycling. Allochthonous OC loads from the landscape, along with autochthonous OC loads from primary production, are mineralized in lakes, buried in lake sediments, and exported via surface or groundwater outflows. Although these processes provide a basis for a conceptual understanding of lake OC budgets, few studies have integrated these fluxes under a dynamic modeling framework to examine their interactions and relative magnitudes. We developed a simple, dynamic mass balance model for OC, and applied the model to a set of five lakes. We examined the relative magnitudes of OC fluxes and found that long-term (> 10 year) lake OC dynamics were predominantly driven by allochthonous loads in four of the five lakes, underscoring the importance of terrestrially-derived OC in northern lake ecosystems. Our model highlighted seasonal patterns in lake OC budgets, with increasing water temperatures and lake productivity throughout the growing season corresponding to a transition from burial- to respiration-dominated OC fates. Ratios of respiration to burial, however, were also mediated by the source (autochthonous vs. allochthonous) of total OC loads. Autochthonous OC is more readily respired and may therefore proportionally reduce burial under a warming climate, but allochthonous OC may increase burial due to changes in precipitation. The ratios of autochthonous to allochthonous inputs and respiration to burial demonstrate the importance of dynamic models for examining both the seasonal and inter-annual roles of lakes in landscape and global carbon cycling, particularly in a global change context. Finally, we highlighted critical data needs, which include surface water DOC observations in paired tributary and lake systems, measurements of OC burial rates, groundwater input volume and DOC, and budgets of particulate OC. US National Science Foundation's MacroSystems Biology Program [EF1137353, EF1137327]; NSF Long-Term Ecological Research program (North Temperate Lakes) [DEB-1440297] This project was a product of the Global Lake Ecological Observatory Network (GLEON) Fellowship program supported by the US National Science Foundation's MacroSystems Biology Program (Awards #EF1137353 and EF1137327). Logistical support was provided by the University of Wisconsin-Madison Center for Limnology, the Cary Institute of Ecosystem Studies, the University of Wisconsin Trout Lake Station, the Lake Sunapee Protective Association, and Grace Hong. Limnological data providers included the NSF Long-Term Ecological Research program (North Temperate Lakes DEB-1440297 and Arctic LTER), the Swedish Meteorological and Hydrological Institute, the Swedish University of Agricultural Sciences and the Canadian Dorset Environmental Science Centre. Additional details on data sources were included online in the appendix (Supplementary material). IMM, HAD, KJF, AMM, ZO, DR, FS and PCH acquired data, developed the model, and performed data analyses. All authors participated in conceiving and developing the project and writing the paper. We thank two reviewers for constructive comments on a draft of this manuscript. Analysis scripts and public data (LTER lakes and Lake Vanern) can be freely downloaded here: https://github.com/GLEON/SOS.

Wildfires are becoming larger and more frequent across much of the United States due to anthropogenic climate change. No studies, however, have assessed fire prevalence in lake watersheds at broad spatial and temporal scales, and thus it is unknown whether wildfires threaten lakes and reservoirs (hereafter, lakes) of the United States. We show that fire activity has increased in lake watersheds across the continental United States from 1984 to 2015, particularly since 2005. Lakes have experienced the greatest fire activity in the western United States, Southern Great Plains, and Florida. Despite over 30 years of increasing fire exposure, fire effects on fresh waters have not been well studied; previous research has generally focused on streams, and most of the limited lake-fire research has been conducted in boreal landscapes. We therefore propose a conceptual model of how fire may influence the physical, chemical, and biological properties of lake ecosystems by synthesizing the best available science from terrestrial, aquatic, fire, and landscape ecology. This model also highlights emerging research priorities and provides a starting point to help land and lake managers anticipate potential effects of fire on ecosystem services provided by fresh waters and their watersheds.

The magnitude of lateral dissolved inorganic carbon (DIC) export from terrestrial ecosystems to inland waters strongly influences the estimate of the global terrestrial carbon dioxide (CO2) sink. At present, no reliable number of this export is available, and the few studies estimating the lateral DIC export assume that all lakes on Earth function similarly. However, lakes can function along a continuum from passive carbon transporters (passive open channels) to highly active carbon transformers with efficient in-lake CO2 production and loss. We developed and applied a conceptual model to demonstrate how the assumed function of lakes in carbon cycling can affect calculations of the global lateral DIC export from terrestrial ecosystems to inland waters. Using global data on in-lake CO2 production by mineralization as well as CO2 loss by emission, primary production, and carbonate precip- itation in lakes, we estimated that the global lateral DIC export can lie within the range of 0:70þ0:27 −0:31 to 1:52þ1:09 −0:90 Pg C yr−1 depending on the assumed function of lakes. Thus, the considered lake function has a large effect on the calculated lateral DIC export from terrestrial ecosystems to inland waters. We conclude that more robust estimates of CO2 sinks and sources will require the classification of lakes into their predominant function. This functional lake classification concept becomes particularly important for the estimation of future CO2 sinks and sources, since in-lake carbon transformation is predicted to be altered with climate change. Funding information. Financial support was received from the Swedish Research Council (Grant No. 2016-04153), the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 643052 (C-CASCADES project), and from the Knut and Alice Wallenberg Foundation (KAW project). This work profited from the Global Lake Ecological Observatory Network (GLEON). The Estonian partners were supported by institutional research funding IUT 21-02 of the Estonian Ministry of Education and Research, and American partners were supported, in part, through NSF EF- 1137327. Funding information. Financial support was received from the Swedish Research Council (Grant No. 2016-04153), the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 643052 (C-CASCADES project), and from the Knut and Alice Wallenberg Foundation (KAW project). This work profited from the Global Lake Ecological Observatory Network (GLEON). The Estonian partners were supported by institutional research funding IUT 21-02 of the Estonian Ministry of Education and Research, and American partners were supported, in part, through NSF EF- 1137327.

The highest densities of lakes on Earth are in north temperate ecosystems, where increasing urbanization and associated chloride runoff can salinize freshwaters and threaten lake water quality and the many ecosystem services lakes provide. However, the extent to which lake salinity may be changing at broad spatial scales remains unknown, leading us to first identify spatial patterns and then investigate the drivers of these patterns. Significant decadal trends in lake salinization were identified using a dataset of long-term chloride concentrations from 371 North American lakes. Landscape and climate metrics calculated for each site demonstrated that impervious land cover was a strong predictor of chloride trends in Northeast and Midwest North American lakes. As little as 1% impervious land cover surrounding a lake increased the likelihood of long-term salinization. Considering that 27% of large lakes in the United States have >1% impervious land cover around their perimeters, the potential for steady and long-term salinization of these aquatic systems is high. This study predicts that many lakes will exceed the aquatic life threshold criterion for chronic chloride exposure (230 mg L −1), stipulated by the US Environmental Protection Agency (EPA), in the next 50 y if current trends continue. Fil: Dugan, Hilary A.. University of Wisconsin–Madison; Estados Unidos. Cary Institute of Ecosystem Studies; Estados Unidos. University of California at Davis; Estados Unidos Fil: Bartlett, Sarah L.. University of Wisconsin–Milwaukee. School of Freshwater Sciences; Estados Unidos Fil: Burke, Samantha M.. University of Waterloo. Department of Biology; Estados Unidos Fil: Doubek, Jonathan P.. Virginia Tech. Department Of Biological Sciences; Estados Unidos Fil: Krivak Tetley, Flora E.. Dartmouth College. Department Of Biological Sciences; Estados Unidos Fil: Skaff, Nicholas K.. Michigan State University. Department Of Fisheries And Wildlife; Estados Unidos Fil: Summers, Jamie C.. Queens University. Department Of Biology; Canadá Fil: Farrell, Kaitlin J.. University Of Georgia. Odum School Of Ecology; Estados Unidos Fil: McCullough, Ian M.. University of California. Bren School Of Environmental Science And Management; Estados Unidos Fil: Morales Williams, Ana M.. Iowa State University. Department Of Ecology; Estados Unidos Fil: Roberts, Derek C.. University of California at Davis; Estados Unidos. UC Davis Tahoe Environmental Research Center; Estados Unidos Fil: Ouyang, Zutao. Michigan State University. Center for Global Change and Earth Observations; Estados Unidos Fil: Scordo, Facundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina Fil: Hanson, Paul C.. University of Wisconsin–Madison. Center for Limnology; Estados Unidos Fil: Weathers, Kathleen C.. Cary Institute of Ecosystem Studies; Estados Unidos

Climate change is a well-recognized threat to lake ecosystems and, although there likely exists geographic variation in the sensitivity of lakes to climate, broad-scale, long-term studies are needed to understand this variation. Further, the potential mediating role of local to regional ecological context on these responses is not well documented. In this study, we examined relationships between climate and water clarity in 365 lakes from 1981 to 2010 in two distinct regions in the northeastern and midwestern United States. We asked (1) How do climate-water-clarity relationships vary across watersheds and between two geographic regions? and (2) Do certain characteristics make some lakes more climate sensitive than others? We found strong differences in climate-water-clarity relationships both within and across the two regions. For example, in the northeastern region, water clarity was often negatively correlated with summer precipitation (median correlation = -0.32, n = 160 lakes), but was not correlated with summer average maximum temperature (median correlation = 0.09, n = 205 lakes). In the midwestern region, water clarity was not related to summer precipitation (median correlation = -0.04), but was often negatively correlated with summer average maximum temperature (median correlation = -0.18). There were few strong relationships between local and sub-regional ecological context and a lake's sensitivity to climate. For example, ecological context variables explained just 16-18% of variation in summer precipitation sensitivity, which was most related to total phosphorus, chlorophyll a, lake depth, and hydrology in both regions. Sensitivity to summer maximum temperature was even less predictable in both regions, with 4% or less of variation explained using all ecological context variables. Overall, we identified differences in the climate sensitivity of lakes across regions and found that local and sub-regional ecological context weakly influences the sensitivity of lakes to climate. Our findings suggest that local to regional drivers may combine to influence the sensitivity of lake ecosystems to climate change, and that sensitivities among lakes are highly variable within and across regions. This variability suggests that lakes are sensitive to different aspects of climate change (temperature vs. precipitation) and that responses of lakes to climate are heterogeneous and complex.

Water clarity is a strong indicator of regional water quality. Unlike other common water- quality metrics, such as chlorophyll a, total P, or trophic status, clarity can be accurately and efficiently estimated remotely on a regional scale. Satellite-based remote sensing is useful in regions with many lakes where traditional field-sampling techniques may be prohibitively expensive. Repeated sampling of easily accessed lakes can lead to spatially irregular, nonrandom samples of a region. Remote sensing remedies this problem. We applied a remote monitoring protocol we had previously developed for Maine lakes .8 ha based on Landsat satellite data recorded during 1995-2010 to identify spatial and temporal patterns in Maine lake clarity. We focused on the overlapping region of Landsat paths 11 and 12 to increase availability of cloud-free images in August and early September, a period of relative lake stability and seasonal poor-clarity conditions well suited for annual monitoring. We divided Maine into 3 regions (northeastern, south-central, western) based on morphometric and chemical lake features. We found a general decrease in average statewide lake clarity from 4.94 to 4.38 m during 1995-2010. Water clarity ranged from 4 to 6 m during 1995-2010, but it decreased consistently during 2005-2010. Clarity in both the northeastern and western lake regions has decreased from 5.22 m in 1995 to 4.36 and 4.21 m, respectively, in 2010, whereas lake clarity in the south-central lake region (4.50 m) has not changed since 1995. Climate change, timber harvesting, or watershed morphometry may be responsible for regional water-clarity decline. Remote sensing of regional water clarity provides a more complete spatial perspective of lake water quality than existing, interest-based sampling. However, field sampling done under existing monitoring programs can be used to calibrate accurate models designed to estimate water clarity remotely.

We evaluated use of MODIS 250 m imagery for remote lake monitoring in Maine. Despite limited spectral resolution (visible red and near infrared bands), the twice daily image capture has a potential advantage over conventionally used, often cloudy Landsat imagery (16 day interval) when short time windows are of interest. We analyzed 364 eligible (≥100 ha) Maine lakes during late summer (Aug–early Sep) 2000–2011. The red band was strongly correlated with natural log-transformed Secchi depth (SD), and the addition of ancillary lake and watershed variables explained some variability in ln(SD) (R2 = 0.68–0.85; 9 models). Weak spectral resolution and variable lake conditions limited accurate lake monitoring to relatively productive periods in late summer, as indicated by inconsistent, sometimes weak regressions during June and July when lakes were clearer and less stable (R2 = 0.19–0.74; 8 models). Additionally, SD estimates derived from 2 sets of concurrent MODIS and Landsat imagery generally did not ...

Seedling establishment is a critical step that may ultimately govern tree species’ distribution shifts under environmental change. Annual variation in the location of seed rain and microclimates results in transient “windows of opportunity” for tree seedling establishment across the landscape. These establishment windows vary at fine spatiotemporal scales that are not considered in most assessments of climate change impacts on tree species range dynamics and habitat displacement. We integrate field seedling establishment trials conducted in the southern Sierra Nevada and western Tehachapi Mountains of southern California with spatially downscaled grids of modeled water‐year climatic water deficit (CWDwy) and mean August maximum daily temperature (Tmax) to map historical and projected future microclimates suitable for establishment windows of opportunity for Quercus douglasii, a dominant tree species of warm, dry foothill woodlands, and Q. kelloggii, a dominant of cooler, more mesic montane woodlands and forests. Based on quasi‐binomial regression models, Q. douglasii seedling establishment is significantly associated with modeled CWDwy and to a lesser degree with modeled Tmax. Q. kelloggii seedling establishment is most strongly associated with Tmax and best predicted by a two‐factor model including CWDwy and Tmax. Establishment niche models are applied to explore recruitment window dynamics in the western Tehachapi Mountains, where these species are currently widespread canopy dominants. Establishment windows are projected to decrease by 50–95%, shrinking locally to higher elevations and north‐facing slopes by the end of this century depending on the species and climate scenario. These decreases in establishment windows suggest the potential for longer‐term regional population declines of the species. While many additional processes regulate seedling establishment and growth, this study highlights the need to account for topoclimatic controls and interannual climatic variation when assessing how seedling establishment and colonization processes could be affected by climate change.

© 2015, Springer Science+Business Media Dordrecht. Context: Predicting climate-driven species’ range shifts depends substantially on species’ exposure to climate change. Mountain landscapes contain a wide range of topoclimates and soil characteristics that are thought to mediate range shifts and buffer species’ exposure. Quantifying fine-scale patterns of exposure across mountainous terrain is a key step in understanding vulnerability of species to regional climate change. Objectives: We demonstrated a transferable, flexible approach for mapping climate change exposure in a moisture-limited, mountainous California landscape across 4 climate change projections under phase 5 of the Coupled Model Intercomparison Project (CMIP5) for mid-(2040–2069) and end-of-century (2070–2099). Methods: We produced a 149-year dataset (1951–2099) of modeled climatic water deficit (CWD), which is strongly associated with plant distributions, at 30-m resolution to map climate change exposure in the Tehachapi Mountains, California, USA. We defined climate change exposure in terms of departure from the 1951–1980 mean and historical range of variability in CWD in individual years and 3-year moving windows. Results: Climate change exposure was generally greatest at high elevations across all future projections, though we encountered moderate topographic buffering on poleward-facing slopes. Historically dry lowlands demonstrated the least exposure to climate change. Conclusions: In moisture-limited, Mediterranean-climate landscapes, high elevations may experience the greatest exposure to climate change in the 21st century. High elevation species may thus be especially vulnerable to continued climate change as habitats shrink and historically energy-limited locations become increasingly moisture-limited in the future.

Satellite-based remote monitoring programs of regional lake water quality largely have relied on Landsat Thematic Mapper (TM) owing to its long image archive, moderate spatial resolution (30 m), and wide sensitivity in the visible portion of the electromagnetic spectrum, despite some notable limitations such as temporal resolution (i.e., 16 days), data pre-processing requirements to improve data quality, and aging satellites. Moderate-Resolution Imaging Spectroradiometer (MODIS) sensors on Aqua/Terra platforms compensate for these shortcomings, although at the expense of spatial resolution. We developed and evaluated a remote monitoring protocol for water clarity of large lakes using MODIS 500 m data and compared MODIS utility to Landsat-based methods. MODIS images captured during May–September 2001, 2004 and 2010 were analyzed with linear regression to identify the relationship between lake water clarity and satellite-measured surface reflectance. Correlations were strong ( R ² = 0.72–0.94) throughout the study period; however, they were the most consistent in August, reflecting seasonally unstable lake conditions and inter-annual differences in algal productivity during the other months. The utility of MODIS data in remote water quality estimation lies in intra-annual monitoring of lake water clarity in inaccessible, large lakes, whereas Landsat is more appropriate for inter-annual, regional trend analyses of lakes ≥ 8 ha. Model accuracy is improved when ancillary variables are included to reflect seasonal lake dynamics and weather patterns that influence lake clarity. The identification of landscape-scale drivers of regional water quality is a useful way to supplement satellite-based remote monitoring programs relying on spectral data alone.

Water clarity is a reliable indicator of lake productivity and an ideal metric of regional water quality. Clarity is an indicator of other water quality variables including chlorophyll-a, total phosphorus and trophic status; however, unlike these metrics, clarity can be accurately and efficiently estimated remotely on a regional scale. Remote sensing is useful in regions containing a large number of lakes that are cost prohibitive to monitor regularly using traditional field methods. Field-assessed lakes generally are easily accessible and may represent a spatially irregular, non-random sample of a region. We developed a remote monitoring program for Maine lakes > 8 ha (1511 lakes) to supplement existing field monitoring programs. We combined Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) brightness values for TM bands 1 (blue) and 3 (red) to estimate water clarity (secchi disk depth) during 1990–2010. Although similar procedures have been applied to Minnesota and Wisconsin lakes, neither state incorporates physical lake variables or watershed characteristics that potentially affect clarity into their models. Average lake depth consistently improved model fitness, and the proportion of wetland area in lake watersheds also explained variability in clarity in some cases. Nine regression models predicted water clarity (R 2 = 0.69–0.90) during 1990–2010, with separate models for eastern (TM path 11; four models) and western Maine (TM path 12; five models that captured differences in topography and landscape disturbance. Average absolute difference between model-estimated and observed secchi depth ranged 0.65–1.03 m. Eutrophic and mesotrophic lakes consistently were estimated more accurately than oligotrophic lakes. Our results show that TM bands 1 and 3 can be used to estimate regional lake water clarity outside the Great Lakes Region and that the accuracy of estimates is improved with additional model variables that reflect physical lake characteristics and watershed conditions.

© 2015, Springer Science+Business Media New York. Adaptive management is broadly recognized as critical for managing natural resources, yet in practice it often fails to achieve intended results for two main reasons: insufficient monitoring and inadequate stakeholder buy-in. Citizen science is gaining momentum as an approach that can inform natural resource management and has some promise for solving the problems faced by adaptive management. Based on adaptive management literature, we developed a set of criteria for successfully addressing monitoring and stakeholder related failures in adaptive management and then used these criteria to evaluate 83 citizen science case studies from peer-reviewed literature. The results suggest that citizen science can be a cost-effective method to collect essential monitoring information and can also produce the high levels of citizen engagement that are vital to the adaptive management learning process. The analysis also provides a set of recommendations for citizen science program design that addresses spatial and temporal scale, data quality, costs, and effective incentives to facilitate participation and integration of findings into adaptive management.

Accepted Ar tic le Averaged 30 year climate change projections mask opportunities for species establishment Josep M. Serra-Diaz 1,2 , Janet Franklin 1 , Lynn C. Sweet 3 , Ian M. McCullough 3 , Alexandra D. Syphard 4 , Helen M. Regan 5 , Lorraine E. Flint 6 , Alan L. Flint 6 , John R. Dingman 7 , Max Moritz 8 , Kelly Redmond 9 , Lee Hannah 10 , Frank W. Davis 3 School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA Harvard Forest, Harvard University, Petersham, MA, USA Bren School of Environmental Science and Management, University of California Santa Barbara, CA, USA Conservation Biology Institute, La Mesa, CA, USA Department of Biology, University of California, Riverside, CA, USA U.S. Geological Survey, California Water Science Center, Sacramento, CA, USA Air Resources Board, California Environmental Protection Agency, Sacramento, CA, USA Department of Environmental Science, Policy and Management, University of California Berkeley, CA, USA Division of atmospheric sciences, Desert Research Institute, Reno, NV, USA Conservation International, Arlington, VA, USA Corresponding author: Josep M. Serra-Diaz, School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA. E-mail: pep.science@gmail.com, jserradi@asu.edu Decision date: 30-Sep-2015 This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: [10.1111/ecog.02074]. ‘This article is protected by copyright. All rights reserved.’

Conservation in the face of climate change is hindered by a preoccupation with place. ‘Refugial capacity’ [1] adds useful descriptive power to the terminology of place, while ‘stepping stones’ and ‘holdouts’ are terms that usefully describe the dynamics of place [2]. Ultimately, no matter what happens in the lexicon, conservation needs to be implemented with a shift in focus from place to process. It will not be easy.

Microclimates have played a critical role in past species range shifts, suggesting that they could be important in biological response to future change. Terms are needed to discuss these future effects. We propose that populations occupying microclimates be referred to as holdouts, stepping stones and microrefugia. A holdout is a population that persists in a microclimate for a limited period of time under deteriorating climatic conditions. Stepping stones successively occupy microclimates in a way that facilitates species' range shifts. Microrefugia refer to populations that persist in microclimates through a period of unfavorable climate. Because climate projections show that return to present climate is highly unlikely, conservation strategies need to be built around holdouts and stepping stones, rather than low-probability microrefugia.

Estimating surface temperature from above-ground field measurements is important for understanding the complex landscape patterns of plant seedling survival and establishment, processes which occur at heights of only several centimeters. Currently, future climate models predict temperature at 2 m above ground, leaving ground-surface microclimate not well characterized. Using a network of field temperature sensors and climate models, a ground-surface temperature method was used to estimate microclimate variability of minimum and maximum temperature. Temperature lapse rates were derived from field temperature sensors and distributed across the landscape capturing differences in solar radiation and cold air drainages modeled at a 30-m spatial resolution. The surface temperature estimation method used for this analysis successfully estimated minimum surface temperatures on north-facing, south-facing, valley, and ridgeline topographic settings, and when compared to measured temperatures yielded an R2 of 0.88, 0.80, 0.88, and 0.80, respectively. Maximum surface temperatures generally had slightly more spatial variability than minimum surface temperatures, resulting in R2 values of 0.86, 0.77, 0.72, and 0.79 for north-facing, south-facing, valley, and ridgeline topographic settings. Quasi-Poisson regressions predicting recruitment of Quercus kelloggii (black oak) seedlings from temperature variables were significantly improved using these estimates of surface temperature compared to air temperature modeled at 2 m. Predicting minimum and maximum ground-surface temperatures using a downscaled climate model coupled with temperature lapse rates estimated from field measurements provides a method for modeling temperature effects on plant recruitment. Such methods could be applied to improve projections of species’ range shifts under climate change. Areas of complex topography can provide intricate microclimates that may allow species to redistribute locally as climate changes.