Ecological processes 

About: Ecological processes is an academic journal published by SpringerOpen. The journal publishes majorly in the area(s): Species richness & Land use. It has an ISSN identifier of 2192-1709. It is also open access. Over the lifetime, 479 publications have been published receiving 8933 citations. The journal is also known as: Shengtai guocheng (Yingwen).

Applications of structural equation modeling (SEM) in ecological studies: an updated review

Abstract: This review was developed to introduce the essential components and variants of structural equation modeling (SEM), synthesize the common issues in SEM applications, and share our views on SEM’s future in ecological research. We searched the Web of Science on SEM applications in ecological studies from 1999 through 2016 and summarized the potential of SEMs, with a special focus on unexplored uses in ecology. We also analyzed and discussed the common issues with SEM applications in previous publications and presented our view for its future applications. We searched and found 146 relevant publications on SEM applications in ecological studies. We found that five SEM variants had not commenly been applied in ecology, including the latent growth curve model, Bayesian SEM, partial least square SEM, hierarchical SEM, and variable/model selection. We identified ten common issues in SEM applications including strength of causal assumption, specification of feedback loops, selection of models and variables, identification of models, methods of estimation, explanation of latent variables, selection of fit indices, report of results, estimation of sample size, and the fit of model. In previous ecological studies, measurements of latent variables, explanations of model parameters, and reports of key statistics were commonly overlooked, while several advanced uses of SEM had been ignored overall. With the increasing availability of data, the use of SEM holds immense potential for ecologists in the future.

Fine-scale hydrologic modeling for regional landscape applications: the California Basin Characterization Model development and performance

Abstract: Resource managers need spatially explicit models of hydrologic response to changes in key climatic drivers across variable landscape conditions We demonstrate the utility of a Basin Characterization Model for California (CA-BCM) to integrate high-resolution data on physical watershed characteristics with historical or projected climate data to predict watershed-specific hydrologic responses The CA-BCM applies a monthly regional water-balance model to simulate hydrologic responses to climate at the spatial resolution of a 270-m grid The model has been calibrated using a total of 159 relatively unimpaired watersheds for the California region As a result of calibration, predicted basin discharge closely matches measured data for validation watersheds The CA-BCM recharge and runoff estimates, combined with estimates of snowpack and timing of snowmelt, provide a basis for assessing variations in water availability Another important output variable, climatic water deficit, integrates the combined effects of temperature and rainfall on site-specific soil moisture, a factor that plants may respond to more directly than air temperature and precipitation alone Model outputs are calculated for each grid cell, allowing results to be summarized for a variety of planning units including hillslopes, watersheds, ecoregions, or political boundaries The ability to confidently calculate hydrologic outputs at fine spatial scales provides a new suite of hydrologic predictor variables that can be used for a variety of purposes, such as projections of changes in water availability, environmental demand, or distribution of plants and habitats Here we present the framework of the CA-BCM model for the California hydrologic region, a test of model performance on 159 watersheds, summary results for the region for the 1981–2010 time period, and changes since the 1951–1980 time period

Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis

Abstract: Evaluating the environmental impacts of climate change on water resources and biological components of the landscape is an integral part of hydrologic and ecological investigations, and the resultant land and resource management in the twenty-first century. Impacts of both climate and simulated hydrologic parameters on ecological processes are relevant at scales that reflect the heterogeneity and complexity of landscapes. At present, simulations of climate change available from global climate models [GCMs] require downscaling for hydrologic or ecological applications. Using statistically downscaled future climate projections developed using constructed analogues, a methodology was developed to further downscale the projections spatially using a gradient-inverse-distance-squared approach for application to hydrologic modeling at 270-m spatial resolution. This paper illustrates a methodology to downscale and bias-correct national GCMs to subkilometer scales that are applicable to fine-scale environmental processes. Four scenarios were chosen to bracket the range of future emissions put forth by the Intergovernmental Panel on Climate Change. Fine-scale applications of downscaled datasets of ecological and hydrologic correlations to variation in climate are illustrated. The methodology, which includes a sequence of rigorous analyses and calculations, is intended to reduce the addition of uncertainty to the climate data as a result of the downscaling while providing the fine-scale climate information necessary for ecological analyses. It results in new but consistent data sets for the US at 4 km, the southwest US at 270 m, and California at 90 m and illustrates the utility of fine-scale downscaling to analyses of ecological processes influenced by topographic complexity.

C:N:P stoichiometry and nutrient limitation of the soil microbial biomass in a grazed grassland site under experimental P limitation or excess

Abstract: The availability of essential nutrients, such as nitrogen (N) and phosphorus (P), can feedback on soil carbon (C) and the soil microbial biomass. Natural cycles can be supplemented by agricultural fertiliser addition, and we determined whether the stoichiometry and nutrient limitation of the microbial biomass could be affected by an unbalanced nutrient supply. Samples were taken from a long-term trial (in effect since 1968) with annual applications of 0, 15 and 30 kg P ha−1 with constant N and potassium. Soil and microbial biomass CNP contents were measured and nutrient limitation assessed by substrate-induced respiration. Linear regression and discriminant analyses were used to identify the variables explaining nutrient limitation. Soil and biomass CNP increased with increasing P fertiliser, and there was a significant, positive, correlation between microbial biomass P and biomass C, apart from at the highest level of P fertilisation when the microbial biomass was over-saturated with P. The molar ratios of C:N:P in the microbial biomass remained constant (homeostatic) despite large changes in the soil nutrient ratios. Microbial growth was generally limited by C and N, except in soil with no added P when C and P were the main limiting nutrients. C, N and P, however, did not explain all the growth limitation on the soils with no added P. Increased soil C and N were probably due to increased net primary production. Our results confirm that C:N:P ratios within the microbial biomass were constrained (i.e. homeostatic) under near optimum soil conditions. Soils with no added P were characterised by strong microbial P limitation and soils under high P by over-saturation of microorganisms with P. Relative changes in biomass C:P can be indicative of nutrient limitation within a site.

Farmers’ perception of climate change and adaptation strategies in the Dabus watershed, North-West Ethiopia

Abstract: This study is aimed at analyzing farmers’ perception and adaptation to climate change in the Dabus watershed. It is based on analysis of data collected from 734 randomly selected farm household heads substantiated with Focus Group Discussions and field observations. The study employed descriptive methods to assess farmers’ perception of climate change, local indicators of climate change and types of adaptation measures exercised to cope up with the risk of the change in climate. The study also employed the Heckman sample selection model to analyze the two-step process of adaptation to climate change which initially requires farmers’ perception that climate is changing prior to responding to the changes through adaptation measures. Based on the model result educational attainment, the age of the head of the household, the number of crop failures in the past, changes in temperature and precipitation significantly influenced farmers’ perception of climate change in wet lowland parts of the study area. In dry lowland condition, farming experience, climate information, duration of food shortage, and the number of crop failures experienced determined farmers’ perception of climate change. Farmers’ adaptation decision in both the wet and dry lowland conditions is influenced by household size, the gender of household head, cultivated land size, education, farm experience, non-farm income, income from livestock, climate information, extension advice, farm-home distance and number of parcels. However, the direction of influence and significance level of most of the explanatory variables vary between the two parts of the study area. In line with the results, any intervention that promotes the use of adaptation measures to climate change may account for location-specific factors that determine farmers' perception of climate change and adaptive responses thereof.