265 research outputs found
Phenology differences between native and novel exoticâdominated grasslands rival the effects of climate change
1. Novel ecosystems can differ from the native systems they replaced. We used phenology measures to compare ecosystem functioning between novel exotic-dominated and native-dominated grasslands in the central U.S.
2. Phenology, or timing of biological events, is affected by climate and land use changes. We assessed how phenology shifts are being altered by exotic species dominance by comparing remotely sensed Normalized Difference Vegetation Index within growing seasons at exotic- and native-dominated sites along a latitudinal gradient. Exotic species were dominated by the C3 species functional group in the north and the C4 species functional group in the south.
3. Date of senescence was an average of 36 days later in exotic than native-dominated grasslands, and this effect was consistent across latitudes.
4. Exotic-dominated grasslands greened-up an average of 10.7 days earlier than native- dominated grasslands, but this effect was highly dependent on latitude and the plant functional group that dominated at that latitude. Green-up differed between native and exotic sites the most in central and northern regions that had dominant C3 grasses.
5. We estimated the effects of an increase in global temperatures on green-up and senescence with a space-for-time substitution, and by comparing growing degree day differences between historical average temperatures and +2.5°C. Green-up was significantly earlier and senescence was significantly later with a 2.5°C increase in temperature. The nativeâexotic difference was significantly greater than the difference due to increased temperature for senescence, but not for green-up.
6. Synthesis and applications. Native to exotic plant conversions in central U.S. grasslands have led to highly altered phenology, especially in terms of senescence, and this effect should be considered along with global warming in models moving forward. This conversion will have to be considered in developing estimates of how global change will affect phenology in locations where exotics are present, especially in cases where their abundance is increasing concurrent with climate change. Global change models and policy should consider exotic species invasion as an additional widespread factor behind changes in phenology
Tools for Assessing Climate Impacts on Fish and Wildlife
Climate change is already affecting many fish and wildlife populations. Managing these populations requires an understanding of the nature, magnitude, and distribution of current and future climate impacts. Scientists and managers have at their disposal a wide array of models for projecting climate impacts that can be used to build such an understanding. Here, we provide a broad overview of the types of models available for forecasting the effects of climate change on key processes that affect fish and wildlife habitat (hydrology, fire, and vegetation), as well as on individual species distributions and populations. We present a framework for how climate-impacts modeling can be used to address management concerns, providing examples of model-based assessments of climate impacts on salmon populations in the Pacific Northwest, fire regimes in the boreal region of Canada, prairies and savannas in the Willamette Valley-Puget Sound Trough-Georgia Basin ecoregion, and marten Martes americana populations in the northeastern United States and southeastern Canada. We also highlight some key limitations of these models and discuss how such limitations should be managed. We conclude with a general discussion of how these models can be integrated into fish and wildlife management
Fish Species of Greatest Conservation Need in Wadeable Iowa Streams: Current Status and Effectiveness of Aquatic Gap Program Distribution Models
Effective conservation of fish species of greatest conservation need (SGCN) requires an understanding of speciesâ habitat relationships and distributional trends. Thus, modeling the distribution of fish species across large spatial scales may be a valuable tool for conservation planning. Our goals were to evaluate the status of 10 fish SGCN in wadeable Iowa streams and to test the effectiveness of IowaAquatic Gap Analysis Project (IAGAP) species distribution models. We sampled fish assemblages from 86 wadeable stream segments in the Mississippi River drainage of Iowa during 2009 and 2010 to provide contemporary, independent fish species presenceâabsence data. The frequencies of occurrence in stream segments where species were historically documented varied from 0.0% for redfin shiner Lythrurus umbratilis to 100.0% for American brook lamprey Lampetra appendix, with a mean of 53.0%, suggesting that the status of Iowa fish SGCN is highly variable. Cohenâs kappa values and other model performance measures were calculated by comparing field-collected presenceâabsence data with IAGAP modelâpredicted presences and absences for 12 fish SGCN. Kappa values varied from 0.00 to 0.50, with a mean of 0.15. The models only predicted the occurrences of banded darter Etheostoma zonale, southern redbelly dace Phoxinus erythrogaster, and longnose dace Rhinichthys cataractae more accurately than would be expected by chance. Overall, the accuracy of the twelve models was low, with a mean correct classification rate of 58.3%. Poor model performance probably reflects the difficulties associated with modeling the distribution of rare species and the inability of the large-scale habitat variables used in IAGAP models to explain the variation in fish species occurrences. Our results highlight the importance of quantifying the confidence in species distribution model predictions with an independent data set and the need for long-term monitoring to better understand the distributional trends and habitat associations of fish SGCN
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Simple measures of climate, soil properties and plant traits predict national scale grassland soil carbon stocks
1. Soil carbon (C) storage is a key ecosystem service. Soil C stocks play a vital role in soil fertility and climate regulation, but the factors that control these stocks at regional and national scales are unknown, particularly when their composition and stability are considered. As a result, their mapping relies on either unreliable proxy measures or laborious direct measurements.
2. Using data from an extensive national survey of English grasslands we show that surface soil (0-7cm) C stocks in size fractions of varying stability can be predicted at both regional and national scales from plant traits and simple measures of soil and climatic conditions.
3. Soil C stocks in the largest pool, of intermediate particle size (50-250 ”m), were best explained by mean annual temperature (MAT), soil pH and soil moisture content. The second largest C pool, highly stable physically and biochemically protected particles (0.45-50 ”m), was explained by soil pH and the community abundance weighted mean (CWM) leaf nitrogen (N) content, with the highest soil C stocks under N rich vegetation. The C stock in the small active fraction (250-4000 ”m) was explained by a wide range of variables: MAT, mean annual precipitation, mean growing season length, soil pH and CWM specific leaf area; stocks were higher under vegetation with thick and/or dense leaves.
4. Testing the models describing these fractions against data from an independent English region indicated moderately strong correlation between predicted and actual values and no systematic bias, with the exception of the active fraction, for which predictions were inaccurate.
5. Synthesis and Applications: Validation indicates that readily available climate, soils and plant survey data can be effective in making local- to landscape-scale (1-100,000 km2) soil C stock predictions. Such predictions are a crucial component of effective management strategies to protect C stocks and enhance soil C sequestration
Biotic homogenization destabilizes ecosystem functioning by decreasing spatial asynchrony
Our planet is facing significant changes of biodiversity across spatial scales. Although the negative effects of local biodiversity (α diversity) loss on ecosystem stability are well documented, the consequences of biodiversity changes at larger spatial scales, in particular biotic homogenization, that is, reduced species turnover across space (ÎČ diversity), remain poorly known. Using data from 39 grassland biodiversity experiments, we examine the effects of ÎČ diversity on the stability of simulated landscapes while controlling for potentially confounding biotic and abiotic factors. Our results show that higher ÎČ diversity generates more asynchronous dynamics among local communities and thereby contributes to the stability of ecosystem productivity at larger spatial scales. We further quantify the relative contributions of α and ÎČ diversity to ecosystem stability and find a relatively stronger effect of α diversity, possibly due to the limited spatial scale of our experiments. The stabilizing effects of both α and ÎČ diversity lead to a positive diversityâstability relationship at the landscape scale. Our findings demonstrate the destabilizing effect of biotic homogenization and suggest that biodiversity should be conserved at multiple spatial scales to maintain the stability of ecosystem functions and services
Habitat Associations of Fish Species of Greatest Conservation Need at Multiple Spatial Scales in Wadeable Iowa Streams
Fish and habitat data were collected from 84 wadeable stream reaches in the Mississippi River drainage of Iowa to predict the occurrences of seven fish species of greatest conservation need and to identify the relative importance of habitat variables measured at small (e.g., depth, velocity, and substrate) and large (e.g., stream order, elevation, and gradient) scales in terms of their influence on species occurrences. Multiple logistic regression analysis was used to predict fish species occurrences, starting with all possible combinations of variables (5 large-scale variables, 13 small-scale variables, and all 18 variables) but limiting the final models to a maximum of five variables. Akaikeâs information criterion was used to rank candidate models, weight model parameters, and calculate model-averaged predictions. On average, the correct classification rate (CCR = 80%) and Cohenâs kappa (Îș = 0.59) were greatest for multiple-scale models (i.e., those including both large-scale and small-scale variables), intermediate for small-scale models (CCR = 75%; Îș = 0.49), and lowest for large-scale models (CCR = 73%; Îș = 0.44). The occurrence of each species was associated with a unique combination of large-scale and small-scale variables. Our results support the necessity of understanding factors that constrain the distribution of fishes across spatial scales to ensure that management decisions and actions occur at the appropriate scale
Biodiversity increases the resistance of ecosystem productivity to climate extremes
It remains unclear whether biodiversity buffers ecosystems against climate extremes, which are becoming increasingly frequent worldwide1. Early results suggested that the ecosystem productivity of diverse grassland plant communities was more resistant, changing less during drought, and more resilient, recovering more quickly after drought, than that of depauperate communities2. However, subsequent experimental tests produced mixed results3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13. Here we use data from 46 experiments that manipulated grassland plant diversity to test whether biodiversity provides resistance during and resilience after climate events. We show that biodiversity increased ecosystem resistance for a broad range of climate events, including wet or dry, moderate or extreme, and brief or prolonged events. Across all studies and climate events, the productivity of low-diversity communities with one or two species changed by approximately 50% during climate events, whereas that of high-diversity communities with 16â32 species was more resistant, changing by only approximately 25%. By a year after each climate event, ecosystem productivity had often fully recovered, or overshot, normal levels of productivity in both high- and low-diversity communities, leading to no detectable dependence of ecosystem resilience on biodiversity. Our results suggest that biodiversity mainly stabilizes ecosystem productivity, and productivity-dependent ecosystem services, by increasing resistance to climate events. Anthropogenic environmental changes that drive biodiversity loss thus seem likely to decrease ecosystem stability14, and restoration of biodiversity to increase it, mainly by changing the resistance of ecosystem productivity to climate events
Habitat, Fish Species, and Fish Assemblage Associations of the Topeka Shiner in West-Central Iowa
Our goal was to identify habitat, fish species, and fish assemblages associated with the occurrence of Topeka Shiners Notropis topeka in stream and off-channel habitat (OCH) of west-central Iowa. Fish assemblages and habitat characteristics were estimated in 67 stream and 27OCHsites during 2010â2011. Topeka Shiners were sampled in 52% of OCH sites, but in only 9% of stream sites, which supports the hypothesis that OCH is an important component of their life history. Fish assemblages containing Topeka Shiners were different from those that did not contain Topeka Shiners in OCH sites, but this was not evident in stream sites. Results from logistic regression models suggested that Topeka Shiner presence was associated with increased submerged vegetation and abundance of Fathead Minnow Pimephales promelas. Contrary to the findings of other studies, the abundance of large piscivorous fishes was not associated with the occurrence of Topeka Shiners. Our results provide new information about the biology and life history of the Topeka Shiner that will guide habitat restoration and other recovery efforts
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