How to evaluate models: Observed vs. predicted or predicted vs. observed?

A common and simple approach to evaluate models is to regress predicted vs. observed values (or vice versa) and compare slope and intercept parameters against the 1:1 line. However, based on a review of the literature it seems to be no consensus on which variable (predicted or observed) should be placed in each axis. Although some researchers think that it is identical, probably because r² is the same for both regressions, the intercept and the slope of each regression differ and, in turn, may change the result of the model evaluation. We present mathematical evidence showing that the regression of predicted (in the y-axis) vs. observed data (in the x-axis) (PO) to evaluate models is incorrect and should lead to an erroneous estimate of the slope and intercept. In other words, a spurious effect is added to the regression parameters when regressing PO values and comparing them against the 1:1 line. Observed (in the y-axis) vs. predicted (in the x-axis) (OP) regressions should be used instead. We also show in an example from the literature that both approaches produce significantly different results that may change the conclusions of the model evaluation.     

Effects of Climate Change,Invasive Species,and Disease on the Distribution of Native European Crayfishes

Climate change will require species to adapt to new conditions or follow preferred climates to higher latitudes or elevations, but many dispersal‐limited freshwater species may be unable to move due to barriers imposed by watershed boundaries. In addition, invasive nonnative species may expand into new regions under future climate conditions and contribute to the decline of native species. We evaluated future distributions for the threatened European crayfish fauna in response to climate change, watershed boundaries, and the spread of invasive crayfishes, which transmit the crayfish plague, a lethal disease for native European crayfishes. We used climate projections from general circulation models and statistical models based on Mahalanobis distance to predict climate‐suitable regions for native and invasive crayfishes in the middle and at the end of the 21st century. We identified these suitable regions as accessible or inaccessible on the basis of major watershed boundaries and present occurrences and evaluated potential future overlap with 3 invasive North American crayfishes. Climate‐suitable areas decreased for native crayfishes by 19% to 72%, and the majority of future suitable areas for most of these species were inaccessible relative to native and current distributions. Overlap with invasive crayfish plague‐transmitting species was predicted to increase. Some native crayfish species (e.g., noble crayfish [Astacus astacus]) had no future refugia that were unsuitable for the modeled nonnative species. Our results emphasize the importance of preventing additional introductions and spread of invasive crayfishes in Europe to minimize interactions between the multiple stressors of climate change and invasive species, while suggesting candidate regions for the debatable management option of assisted colonization. Efectos del Cambio Climático, Especies Invasoras y Enfermedades sobre la Distribución de Cangrejos de Río Europeos Nativos     

Towards a control model for the highly cybernetic farming ecosystems

Abstract

A dynamic model based on the linear systems theory is developed in designing a highly cybernetic farming strategy to efficiently manage residuals generated in farm ecosystems. A linear cybernetic model would be used to describe the dynamic behavior of resource flow in the farm ecosystem in which the state variables are resource quantities, and the control variables are residual quantities. The controlled process is defined as the controlled management strategy change. Cybernetic mechanism shows the application of residuals as control measures have determinate effects on the controlled process as along as the farming system is observable and controllable in the control sense. To illustrate the model algorithm the idea is applied to simulate the dynamic response of residual phosphorus concentrations in an integrated pig/corn farming system located in the south Taiwan region. General results show that the residual phosphorous concentration is influenced by farming activities which are controlled by a system of gross input and net output parameters. This paper demonstrates using input‐output analysis technique that residuals generated in the farming system is the most important cybernetic variable, so that the proper management of residuals alone has the potential to maintain future productivity and sustainability.     

The influence of metam sodium on soil respiration

Abstract

A laboratory experiment was performed in order to evaluate the extent to which metam sodium (MS) applied at two different recommended rates and its degradation product, methyl isothiocyanate ( MITC ), affect soil respiration. Results suggest that MS degradation to MITC was complete within 4 hours and that MITC decomposed quickly in a few days, except in the soil containing high organic matter where it was still present after 15 days. Following the addition of MS, a lag phase appeared in CO₂‐C evolution in the soil. It was longer for the higher dose of MS added and for the two soils with low organic C content. The dynamics of the process was described by the Bonde and Rosswall model and by the Gompertz RS E model for the untreated and the MS‐treated soils, respectively.     

LNG池火热辐射模型及安全距离影响因素研究

重点对LNG池火热辐射模型,模型应用方式,以及热辐射安全距离的影响因素做了详细研究,给出池火热辐射模型采用及安全距离计算的方法。对常用的热辐射计算模型(点源模型、LNGFire3和PoFM ISE模型)加以介绍,并对3种模型做了对比研究。PoFM ISE模型充分考虑大池火直径时不完全燃烧的因素以及风速对火焰高度的影响,建议当风速大于1.5 m/s,池火直径大于20 m时采用。同时,进一步研究影响LNG池火热辐射安全距离的各种因素,包括池火直径、风速、环境温度和湿度,从而得出不同条件下池火热辐射安全距离的要求。     

Ecological‐Niche Modeling and Prioritization of Conservation‐Area Networks for Mexican Herpetofauna

Abstract: One of the most important tools in conservation biology is information on the geographic distribution of species and the variables determining those patterns. We used maximum‐entropy niche modeling to run distribution models for 222 amphibian and 371 reptile species (49% endemics and 27% threatened) for which we had 34,619 single geographic records. The planning region is in southeastern Mexico, is 20% of the country's area, includes 80% of the country's herpetofauna, and lacks an adequate protected‐area system. We used probabilistic data to build distribution models of herpetofauna for use in prioritizing conservation areas for three target groups (all species and threatened and endemic species). The accuracy of species‐distribution models was better for endemic and threatened species than it was for all species. Forty‐seven percent of the region has been deforested and additional conservation areas with 13.7% to 88.6% more native vegetation (76% to 96% of the areas are outside the current protected‐area system) are needed. There was overlap in 26 of the main selected areas in the conservation‐area network prioritized to preserve the target groups, and for all three target groups the proportion of vegetation types needed for their conservation was constant: 30% pine and oak forests, 22% tropical evergreen forest, 17% low deciduous forest, and 8% montane cloud forests. The fact that different groups of species require the same proportion of habitat types suggests that the pine and oak forests support the highest proportion of endemic and threatened species and should therefore be given priority over other types of vegetation for inclusion in the protected areas of southeastern Mexico.     

Ability of Matrix Models to Explain the Past and Predict the Future of Plant Populations

Uncertainty associated with ecological forecasts has long been recognized, but forecast accuracy is rarely quantified. We evaluated how well data on 82 populations of 20 species of plants spanning 3 continents explained and predicted plant population dynamics. We parameterized stage‐based matrix models with demographic data from individually marked plants and determined how well these models forecast population sizes observed at least 5 years into the future. Simple demographic models forecasted population dynamics poorly; only 40% of observed population sizes fell within our forecasts’ 95% confidence limits. However, these models explained population dynamics during the years in which data were collected; observed changes in population size during the data‐collection period were strongly positively correlated with population growth rate. Thus, these models are at least a sound way to quantify population status. Poor forecasts were not associated with the number of individual plants or years of data. We tested whether vital rates were density dependent and found both positive and negative density dependence. However, density dependence was not associated with forecast error. Forecast error was significantly associated with environmental differences between the data collection and forecast periods. To forecast population fates, more detailed models, such as those that project how environments are likely to change and how these changes will affect population dynamics, may be needed. Such detailed models are not always feasible. Thus, it may be wiser to make risk‐averse decisions than to expect precise forecasts from models. Habilidad de los Modelos Matriciales para Explicar el Pasado y Predecir el Futuro de las Poblaciones de Plantas     

Parameterising competing zooplankton for survival in plankton functional type models

Marine plankton ecosystems are an important component of biogeochemical cycling in the oceans. Operational plankton functional type (PFT) models, that group plankton according to their biogeochemical properties, are currently being developed to resolve biogenic gas exchange between the ocean and atmosphere, and to model the lowest trophic levels in fisheries models. A fundamental problem with these models is that PFTs often go extinct in computer simulations, effectively removing the biogeochemical processes from the models. Cropp and Norbury [Cropp, R., Norbury, J., 2009a. Parameterizing plankton functional type models: insights from a dynamical systems perspective. J. Plankton Res. 31, 939-963] demonstrated that parameter combinations that allowed all PFTs to stay extant for all time in stable, homogeneous environments were rare in a PFT model with two competing phytoplankton and one zooplankton (NP₁P₂Z). In this paper, we examine the dynamical properties of a generic predator-predator-prey PFT model, and apply the analysis techniques developed by Cropp and Norbury to a simple example PFT model with one phytoplankton and two zooplankton (NPZ₁Z₂) in order to explore its properties and parameter space. We find that the properties of predator-predator-prey PFT systems are fundamentally different from those of predator-prey-prey PFT systems. The likelihood of parameter combinations for which all PFTs stay extant for all time in predator-predator-prey PFT systems depends critically on the process formulations used, and the properties of co-existing zooplankton (as defined by their parameter values) are quite different to those of co-existing phytoplankton.     

Neighborhood Conditions and Helping Behavior in Late Life

The purpose of this study is to develop and test a latent variable model that explores the ways in which social structural factors influence the amount of social support that older adults provide to their social network members. Neighborhood conditions play a key role in this conceptual scheme. The findings provide support for the following conceptual linkages: (1) low parental education is associated with low respondent education; (2) older people with less education encounter more economic difficulty; (3) greater financial problems are associated with living in a rundown neighborhood; (4) older individuals who live in dilapidated neighborhoods are more hostile; and (5) older adults who are hostile are less likely to provide social support to their social network members. Research indicates that helping others is a key to successful aging. Ways must be found to help economically disadvantaged elders provide support to their social network members.     

Predicting Recovery Criteria for Threatened and Endangered Plant Species on the Basis of Past Abundances and Biological Traits

Recovery plans for species listed under the U.S. Endangered Species Act are required to specify measurable criteria that can be used to determine when the species can be delisted. For the 642 listed endangered and threatened plant species that have recovery plans, we applied recursive partitioning methods to test whether the number of individuals or populations required for delisting can be predicted on the basis of distributional and biological traits, previous abundance at multiple time steps, or a combination of traits and previous abundances. We also tested listing status (threatened or endangered) and the year the recovery plan was written as predictors of recovery criteria. We analyzed separately recovery criteria that were stated as number of populations and as number of individuals (population‐based and individual‐based criteria, respectively). Previous abundances alone were relatively good predictors of population‐based recovery criteria. Fewer populations, but a greater proportion of historically known populations, were required to delist species that had few populations at listing compared with species that had more populations at listing. Previous abundances were also good predictors of individual‐based delisting criteria when models included both abundances and traits. The physiographic division in which the species occur was also a good predictor of individual‐based criteria. Our results suggest managers are relying on previous abundances and patterns of decline as guidelines for setting recovery criteria. This may be justifiable in that previous abundances inform managers of the effects of both intrinsic traits and extrinsic threats that interact and determine extinction risk. Predicción de Criterios de Recuperación para Especies de Plantas en Peligro y Amenazadas con Base en Abundancias Pasadas y Atributos Biológicos