Overview of the model types available for development of ecological models

Temporal-Spatial Fluctuations of a Phytoplankton Community and Their Association with Environmental Variables Based on Classification and Regression Tree in a Shallow Temperate Mountain River

The effects of environmental factors on phytoplankton are not simply positive or negative but complex and dependent on the combination of their concentrations in a fluctuating environment. Traditional statistical methods may miss some of the complex interactions between the environment and phytoplankton. In this study, the temporal-spatial fluctuations of phytoplankton diversity and abundance were investigated in a shallow temperate mountain river. The machine learning method classification and regression tree (CART) was used to explore the effects of environmental variables on the phytoplankton community. The results showed that both phytoplankton species diversity and abundance varied fiercely due to environmental fluctuation. Microcystis aeruginosa, Amphiprora sp., Anabaena oscillarioides, and Gymnodinium sp. were the dominant species. The CART analysis indicated that dissolved oxygen, oxidation-reduction potential, total nitrogen (TN), total phosphorus (TP), and water temperature (WT) explained 36.00%, 13.81%, 11.35%, 9.96%, and 8.80%, respectively, of phytoplankton diversity variance. Phytoplankton abundance was mainly affected by TN, WT, and TP, with variance explanations of 39.40%, 15.70%, and 14.09%, respectively. Most environmental factors had a complex influence on phytoplankton diversity and abundance: their effects were positive under some conditions but negative under other combinations. The results and methodology in this study are important in quantitatively understanding and exploring aquatic ecosystems.

Multi-model projections of tree species performance in Quebec, Canada under future climate change

Many modelling approaches have been developed to project climate change impacts on forests. By analysing 'comparable' yet distinct variables (e.g. productivity, growth, dominance, biomass, etc.) through different structures, parameterizations and assumptions, models can yield different outcomes to rather similar initial questions. This variability can lead to some confusion for forest managers when developing strategies to adapt forest management to climate change. In this study, we standardized results from seven different models (Habitat suitability, trGam, StandLEAP, Quebec Landscape Dynamics, PICUS, LANDIS-II and LPJ-LMfire) to provide a simple and comprehensive assessment of the uncertainty and consensus in future performance (decline, status quo, improvement) for six tree species in Quebec under two radiative forcing scenarios (RCP 4.5 and RCP 8.5). Despite a large diversity of model types, we found a high level of agreement (73.1%) in projected species' performance across species, regions, scenarios and time periods. Low agreements in model outcomes resulted from small dissensions among models. Model agreement was much higher for cold-tolerant species (up to 99.9%), especially in southernmost forest regions and under RCP 8.5, indicating that these species are especially sensitive to increased climate forcing in the southern part of their distribution range. Lower agreement was found for thermophilous species (sugar maple, yellow birch) in boreal regions under RCP 8.5 mostly as a result of the way the different models are handling natural disturbances (e.g. wildfires) and lags in the response of populations (forest inertia or migration capability) to climate change. Agreement was slightly higher under high anthropogenic climate forcing, suggesting that important thresholds in species-specific performance might be crossed if radiative forcing reach values as high as those projected under RCP 8.5. We expect that strong agreement among models despite their different assumptions, predictors and structure should inspire the development of forest management strategies to be better adapted to climate change.

Responses of marine ecosystems to climate change impacts and their treatment in biogeochemical ecosystem models

To predict the effects of climate change on marine ecosystems and the effectiveness of intervention and mitigation strategies, we need reliable marine ecosystem response models such as biogeochemical models that reproduce climate change effects. We reviewed marine ecosystem parameters and processes that are modified by climate change and examined their representations in biogeochemical ecosystem models. The interactions among important aspects of marine ecosystem modelling are not often considered due to complexity: these include the use of multiple IPCC scenarios, ensemble modelling approach, independent calibration datasets, the consideration of changes in cloud cover, ocean currents, wind speed, sea-level rise, storm frequency, storm intensity, and the incorporation of species adaptation to changing environmental conditions. Including our recommendations in future marine modelling studies could help improve the accuracy and reliability of model predictions of climate change impacts on marine ecosystems.

Development of a wheat aphid population dynamics model based on cusp catastrophe theory

Aphids are a major global wheat pest that can cause considerable loss of yield. Modeling of aphid population dynamics is an integral part of management strategies to manage or control aphid populations. In this paper, first, a wheat aphid population dynamics model was developed based on a logistic model and the Holling III functional response, which includes three factors: temperature, natural enemies and insecticide. Second, this model fitted with a cusp catastrophe model to describe how abrupt changes in the wheat aphid population were influenced by these factors. Finally, the system was validated with field data from 2016 to 2018. The bifurcation set of the cusp catastrophe model was deemed to be the quantified dynamic control threshold, so an outbreak of aphid’s population can be explained according to the variation of control variables. In short, this aphid population model was successfully validated on survey data, which can be used to guide the prevention and control of aphids.

Conceptual Model of Medication Adherence in Older Adults with High Blood Pressure-An Integrative Review of the Literature

BACKGROUND

Medication adherence (MA) is the most important controlling factor of high blood pressure (HBP). There are a few MA models, but they have not been successful in predicting MA completely. Thus, this study aimed to expand a conceptual model of MA based on an ecological approach.

METHODS

An integrative review of the literature based on theoretical and empirical studies was completed. Data source comprised: Medline (including PubMed and Ovid), ISI, Embase, Google scholar, and internal databases such as Magiran, Google, SID, and internal magazines. Primary English and Persian language studies were collected from 1940 to 2018. The steps of study included: (a) problem identification, (b) literature review and extracting studies, (c) appraising study quality, (d) gathering data, (e) data analysis using the directed content analysis, (f) concluding.

RESULTS

Thirty-six articles were finally included and analyzed. After analysis, predictors of MA in older adults with hypertension were categorized into personal, interpersonal, organizational, and social factors. Although the personal factors have the most predictors in sub-categories of behavioral, biological, psychological, knowledge, disease, and medication agents, social, organizational and interpersonal factors can have indirect and important effects on elderly MA.

CONCLUSION

There are many factors influencing MA of elderly with HBP. The personal factor has the most predictors. The designed model of MA because of covering all predictor factors, can be considered as a comprehensive MA model. It is suggested that future studies should select factors for study from all levels of the model.

Model application niche analysis: Assessing the transferability and generalizability of ecological models

The use of models by ecologists and environmental managers, to inform environmental management and decision-making, has grown exponentially in the past 50 years. Due to logistical, economical, and theoretical benefits, model users frequently transfer preexisting models to new sites where data are scarce. Modelers have made significant progress in understanding how to improve model generalizability during model development. However, models are always imperfect representations of systems and are constrained by the contextual frameworks used during their development. Thus, model users need better ways to evaluate the possibility of unintentional misapplication when transferring models to new sites. We propose a method of describing a model's application niche for use during the model selection process. Using this method, model users synthesize information from databases, past studies, and/or past model transfers to create model performance curves and heat maps. We demonstrated this method using an empirical model developed to predict the ecological condition of plant communities in riverine wetlands of the Appalachian Highland physiographic region, U.S.A. We assessed this model's transferability and generalizability across (1) riverine wetlands in the contiguous U.S.A., (2) wetland types in the Appalachian Highland physiographic region, and (3) wetland types in the contiguous U.S.A. With this methodology and a discussion of its critical steps, we set the stage for further inquiries into the development of consistent and transparent practices for model selection when transferring a model.

Dynamic models in research and management of biological invasions

Invasive species are increasing in number, extent and impact worldwide. Effective invasion management has thus become a core socio-ecological challenge. To tackle this challenge, integrating spatial-temporal dynamics of invasion processes with modelling approaches is a promising approach. The inclusion of dynamic processes in such modelling frameworks (i.e. dynamic or hybrid models, here defined as models that integrate both dynamic and static approaches) adds an explicit temporal dimension to the study and management of invasions, enabling the prediction of invasions and optimisation of multi-scale management and governance. However, the extent to which dynamic approaches have been used for that purpose is under-investigated. Based on a literature review, we examined the extent to which dynamic modelling has been used to address invasions worldwide. We then evaluated how the use of dynamic modelling has evolved through time in the scope of invasive species management. The results suggest that modelling, in particular dynamic modelling, has been increasingly applied to biological invasions, especially to support management decisions at local scales. Also, the combination of dynamic and static modelling approaches (hybrid models with a spatially explicit output) can be especially effective, not only to support management at early invasion stages (from prevention to early detection), but also to improve the monitoring of invasion processes and impact assessment. Further development and testing of such hybrid models may well be regarded as a priority for future research aiming to improve the management of invasions across scales.

A Minimal Invasive Method to Forecast the Effects of Anthropogenic Disturbance on Tropical Cave Beetle Communities

Many tropical landscapes are changing rapidly, with uncertain outcomes for biodiversity, landscape function, and the corresponding landscape services. Therefore, monitoring and adaptively managing the drivers and consequences of landscape change while sustaining the production of essential resources have become research and policy priorities. In this perspective, we have applied a recent framework, the stochastic dynamic methodology (StDM), with the purpose of understanding the effects of natural and anthropogenic disturbances on caves' integrity using cave beetle communities (Coleoptera) as ecological indicators. The proposed method was preceeded by a generalized linear model for discriminating significant relationships between the selected indicators, the structural changes in the caves, and the epigean habitats associated. The obtained results showed different ecological trends in response to the environmental changes. Overall, the simulation results seem to demonstrate the StDM reliability in determining the effects of habitat dynamics, that is, the expansion of agricultural activities, in areas near the caves in the structure of cave beetle communities. The applied method, based on universal information-theoretic principles, can be easily implemented and interpreted by environmental managers and decision makers, enabling anticipating impacts and supporting the development of measures aimed at minimizing the identified problems.

Gene flow, invasiveness, and ecological impact of genetically modified crops

The main environmental concerns about genetically modified (GM) crops are the potential weediness or invasiveness in the crop itself or in its wild or weedy relatives as a result of transgene movement. Here we briefly review evidence for pollen- and seed-mediated gene flow from GM crops to non-GM or other GM crops and to wild relatives. The report focuses on the effect of abiotic and biotic stress-tolerance traits on plant fitness and their potential to increase weedy or invasive tendencies. An evaluation of weediness and invasive traits that contribute to the success of agricultural weeds and invasive plants was of limited value in predicting the effect of biotic and abiotic stress-tolerance GM traits, suggesting context-specific evaluation rather than generalizations. Fitness data on herbicide, insect, and disease resistance, as well as cold-, drought-, and salinity-tolerance traits, are reviewed. We describe useful ecological models predicting the effects of gene flow and altered fitness in GM crops and wild/weedy relatives, as well as suitable mitigation measures. A better understanding of factors controlling population size, dynamics, and range limits in weedy volunteer GM crop and related host or target weed populations is necessary before the effect of biotic and abiotic stress-tolerance GM traits can be fully assessed.