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Students' solutions of enumerative combinatorial problems may be assessed along two main dimensions: the correctness of the solution and the method of enumeration. This study looks at the second dimension with reference to the Cartesian product of two sets, and at the 'odometer' combinatorial strategy defined by English (1991). Since we are not aware of any algorithm-based methods suitable for analysing combinatorial strategies on a large-scale sample, in this study we endeavour to formalize the odometer strategy and recommend a method of algorithm-based classification of solutions according to the strategy used. In the paper (1) odometer thinking is described using a formula based on its definition, and (2) constancy and cyclicity are characterized using mathematical formulae, which are then used to describe odometer thinking in a computationally efficient manner ('odometricality'). Our hypothesis, i.e. that odometer thinking may be approximated by the odometricality index, is successfully tested on a random sample of automatically generated solutions (n=10,000) by calculating the correlation between odometricality and the formal measure of odometer thinking. Finally, we offer a method (and R script) for classifying strategy use.

Agriculture faces several challenges to use the available resources in a more environmentally sustainable manner. One of the most significant is to develop sustainable water management. The modern Internet of Things (IoT) techniques with real-time data collection and visualisation can play an important role in monitoring the readily available moisture in the soil. An automated Arduino-based low-cost capacitive soil moisture sensor has been calibrated and developed for data acquisition. A sensor- and soil-specific calibration was performed for the soil moisture sensors (SKU:SEN0193 - DFROBOT, Shanghai, China). A Repeatability and Reproducibility study was conducted by range of mean methods on clay loam, sandy loam and silt loam soil textures. The calibration process was based on the data provided by the capacitive sensors and the continuously and parallelly measured soil moisture content by the thermogravimetric method. It can be stated that the response of the sensors to changes in soil moisture differs from each other, which was also greatly influenced by different soil textures. Therefore, the calibration according to soil texture was required to ensure adequate measurement accuracy. After the calibration, it was found that a polynomial calibration function (R2 ≥ 0.89) was the most appropriate way for modelling the behaviour of the sensors at different soil textures.

Modeling how climate change may affect the potential distribution of species and communities typically utilizes bioclimatic variables. Distribution predictions rely on the values of the bioclimatic variable (e.g., precipitation of the wettest quarter). However, the ecological meaning of most of these variables depends strongly on the within‐year position of a specific climate period (SCP), for example, the wettest quarter of the year, which is often overlooked. Our aim was to determine how the within‐year position of the SCPs would shift (SCP shift) in reaction to climate change in a global context. We calculated the deviations of the future within‐year position of the SCPs relative to the reference period. We used four future time periods, four scenarios, and four CMIP6 global climate models (GCMs) to provide an ensemble of expectations regarding SCP shifts and locate the spatial hotspots of the shifts. Also, the size and frequency of the SCP shifts were subjected to linear models to evaluate the importance of the impact modeler's decision on time period, scenario, and GCM. We found ample examples of SCP shifts exceeding 2 months, with 6‐month shifts being predicted as well. Many areas in the tropics are expected to experience both temperature and precipitation‐related shifts, but precipitation‐related shifts are abundantly predicted for the temperate and arctic zones as well. The combined shifts at the Equator reinforce the likelihood of the emergence of no‐analogue climates there. The shifts become more pronounced as time and scenario progress, while GCMs could not be ranked in a clear order in this respect. For most SCPs, the modeler's decision on the GCM was the least important, while the choice of time period was typically more important than the choice of scenario. Future predictive distribution models should account for SCP shifts and incorporate the phenomenon in the modeling efforts. [ABSTRACT FROM AUTHOR]

Aim: Climate change is affecting the geographic distributions of many species and researchers are increasingly relying on species distribution models (SDMs) to forecast species' redistributions under climate change. Such modelling studies, however, often ignore biotic interactions that shape species' geographic ranges. This is especially problematic for coral reefs, which host a high diversity of species and interactions. We tested how biotic interactions affect the distribution patterns of obligate coral‐dwelling Trapezia crabs. Location: Global coastal ocean. Time Period: 2000–2014, 2040–2050, 2090–2100. Major Taxa Studied: Corals and coral‐dwelling Trapezia crabs. Methods: We determined the symbiotic relationships between 22 crab species in the genus Trapezia and corals via field survey and extensive literature review. We first developed SDMs for coral and crab species using exclusively abiotic variables (abiotic‐only models for corals and crabs). Then we constructed a second set of models where we accounted for coral distributions into crab predictions by combining model predictions for the two taxa a posteriori (abiotic‐plus‐biotic models for crabs only). Results: We obtained 30 commonly accepted coral‐crab symbiotic relationships from nine Trapezia crab and six stony coral species. The abiotic‐only model predictions showed that six corals may lose approximately one‐sixth of suitable ranges under RCP 8.5 in 2040–2050. The two types of models for crabs yielded largely different habitat suitability predictions and accounting for biotic interactions into SDM predictions exacerbates the predicted impacts of climate change on coral‐dwelling crabs. Main Conclusions: Our results show large discrepancies in crab spatial distribution patterns with and without accounting for symbiotic interactions. Our findings highlight the important role of modeller's decision on accounting for biotic interactions when predicting the geographical ranges of coral‐dwelling species, with important implications for designing future conservation and management strategies for marine species. [ABSTRACT FROM AUTHOR]

Regional climate models (RCMs) provide reliable climatic predictions for the next 90 years with high horizontal and temporal resolution. In the 21st century northward latitudinal and upward altitudinal shift of the distribution of plant species and phytogeographical units is expected. It is discussed how the modeling of phytogeographical unit can be reduced to modeling plant distributions. Predicted shift of the Moesz line is studied as case study (with three different modeling approaches) using 36 parameters of REMO regional climate dataset, ArcGIS geographic information software, and periods of 1961-1990 (reference period), 2011-2040, and 2041-2070. The disadvantages of this relatively simple climate envelope modeling (CEM) approach are then discussed and several ways of model improvement are suggested. Some statistical and artificial intelligence (AI) methods (logistic regression, cluster analysis and other clustering methods, decision tree, evolutionary algorithm, artificial neural network) are able to provide development of the model. Among them artificial neural networks (ANN) seems to be the most suitable algorithm for this purpose, which provides a black box method for distribution modeling.

High-resolution ecosystem maps increase the efficiency of policy implementation. However, due to challenges related to both data and methods, such maps of appropriate scale and quality are still rarely available for nationwide analyses. We present solutions to some typical challenges of national-scale ecosystem mapping through the new Ecosystem Map of Hungary. It is a comprehensive, spatially and thematically detailed map with a hierarchical typology. The mapping methodology combined several novel elements from the integration of various large-scale databases in a (theoretical) data cube to the use of image-based predictive mapping (with a Random Forest classifier, using Sentinel-1 and -2 and environmental data). A participatory method involving local experts was used for validation, addressing the lack of suitable reference data as well as improving map-maker and map-user interaction. Besides the original objective of supporting conservation-related decision-making, further uses emerged from a variety of fields including spatial planning, education and recreation. [ABSTRACT FROM AUTHOR]