Bayesian Models for Spatially Explicit Interactions Between Neighbouring Plants

Interactions between neighbouring plants drive population and community dynamics in terrestrial ecosystems. Understanding these interactions is critical for both fundamental and applied ecology. Spatial approaches to model neighbour interactions are necessary, as interaction strength depends on the distance between neighbouring plants. Recent Bayesian advancements, including the Hamiltonian Monte Carlo algorithm, offer the flexibility and speed to fit models of spatially explicit neighbour interactions. We present a guide for parameterizing these models in the Stan programming language and demonstrate how Bayesian computation can assist ecological inference on plant–plant interactions. Modelling plant neighbour interactions presents several challenges for ecological modelling. First, nonlinear models for distance decay can be prone to identifiability problems, resulting in lack of model convergence. Second, the pairwise data structure of plant–plant interaction matrices often leads to large matrices that demand high computational power. Third, hierarchical structure in plant–plant interaction data is ubiquitous, including repeated measurements within field plots, species and individuals. Hierarchical terms (e.g. ‘random effects’) can result in model convergence problems caused by correlations between coefficients. We explore modelling solutions for these challenges with examples representing spatial data on plant demographic rates: growth, survival and recruitment. We show that ragged matrices reduce computational challenges inherent to pairwise matrices, resulting in higher efficiency across data types. We also demonstrate how metrics for model convergence, including divergent transitions and effective sample size, can help diagnose problems that result from complex nonlinear structures. Finally, we explore when to use different model structures for hierarchical terms, including centred and non-centred parameterizations. We provide reproducible examples written in Stan to enable ecologists to fit and troubleshoot a broad range of neighbourhood interaction models. Spatially explicit models are increasingly central to many ecological questions. Our work illustrates how novel Bayesian tools can provide flexibility, speed and diagnostic capacity for fitting plant neighbour models to large, complex datasets. The methods we demonstrate are applicable to any dataset that includes a response variable and locations of observations, from forest inventory plots to remotely sensed imagery. Further developments in statistical models for neighbour interactions are likely to improve our understanding of plant population and community ecology across systems and scales

Forecasting Natural Regeneration of Sagebrush After Wildfires Using Population Models and Spatial Matching

Context Addressing ecosystem degradation in the Anthropocene will require ecological restoration across large spatial extents. Identifying areas where natural regeneration will occur without direct resource investment will improve scalability of restoration actions. Objectives An ecoregion in need of large scale restoration is the Great Basin of the Western US, where increasingly large and frequent wildfires threaten ecosystem integrity and its foundational shrub species. We develop a framework to forecast where postwildfire regeneration of sagebrush cover (Artemisia spp.) is likely to occur within the burnt areas across the region (\u3e900,000 km2). Methods First, we parameterized population models using Landsat satellite-derived time series of sagebrush cover. Second, we evaluated the out-of-sample performance by predicting natural regeneration in wildfres not used for model training. This model assessment reproduces a management-oriented scenario: making restoration decisions shortly after wildfires with minimal local information. Third, we asked how accounting for increasingly fine-scale spatial heterogeneity could improve model forecasting accuracy. Results Regional-level models revealed that sagebrush post-fire recovery is slow, estimating \u3e 80-year time horizon to reach an average cover at equilibrium of 16.6% (CI95% 9–25). Accounting for wildfre and within-wildfre spatial heterogeneity improved out-ofsample forecasts, resulting in a mean absolute error of 3.5 ± 4.3% cover, compared to the regional model with an error of 7.2 ± 5.1% cover. Conclusions We demonstrate that combining population models and non-parametric spatial matching provides a fexible framework for forecasting plant population recovery. Models for population recovery applied to Landsat-derived time series will assist restoration decision-making, including identifying priority targets for restoration

Socio-Ecological Interactions Promote Outbreaks of a Harmful Invasive Plant in an Urban Landscape

Urban landscapes often harbour organisms that harm people and threaten native biodiversity. These landscapes are characterized by differences in socioeconomic context, habitat suitability and patch connectedness. Identifying which spatial differences enable outbreaks of pests, pathogens and invasive species will improve targeted control efforts. We tested hypotheses to explain the distribution and demography of puncturevine Tribulus terrestris, a human-dispersed invasive plant in Boise, a city in the western United States. We hypothesized an increase in puncturevine infestations near low-valued properties with a high proportion of bare ground, the species\u27 preferred microhabitat, that are well connected on the urban road network. To test these hypotheses, we collected data on the abundance, emergence and persistence of reproductive plants in transects spanning \u3e100 km of our study city. We then used hierarchical Bayesian models to evaluate the impacts of spatial covariates on puncturevine distribution and demography. Bare ground cover consistently increased abundance, emergence and persistence of puncturevine, indicating the overarching importance of suitable establishment sites for this invasive species. Property value had the strongest impact on puncturevine abundance and was the most important main effect in the model for puncturevine emergence. In both models, lower-valued properties had a higher risk of puncturevine occurrence. The effects of road network connectivity depended on bare ground cover, with the highest predicted abundance and emergence of puncturevine in patches with low connectivity on the road network and high bare ground cover. Understanding these relationships will require data that can disentangle seed dispersal from establishment limitations

Unifying Community Detection Across Scales from Genomes to Landscapes

Biodiversity science encompasses multiple disciplines and biological scales from molecules to landscapes. Nevertheless, biodiversity data are often analyzed separately with discipline-specific methodologies, constraining resulting inferences to a single scale. To overcome this, we present a topic modeling framework to analyze community composition in cross-disciplinary datasets, including those generated from metagenomics, metabolomics, field ecology and remote sensing. Using topic models, we demonstrate how community detection in different datasets can inform the conservation of interacting plants and herbivores. We show how topic models can identify members of molecular, organismal and landscape-level communities that relate to wildlife health, from gut microbes to forage quality. We conclude with a future vision for how topic modeling can be used to design cross-scale studies that promote a holistic approach to detect, monitor and manage biodiversity

Intraspecific Variation in Plant-Plant Interactions and Belowground Zone of Influence of Big Sagebrush (\u3cem\u3eArtemisia tridentata\u3c/em\u3e)

Post-fire restoration of degraded sagebrush ecosystems over large areas of the Great Basin is challenging, in part due to unpredictable outcomes. Low rates of restoration success are attributed to increasing frequencies of wildfires, biological invasions, and climate variability. Quantifying restoration outcomes by accounting for sources of biotic and abiotic variability will improve restoration as a predictive science. One source of biotic variability is neighbor interactions, which can regulate demographic parameters of coexisting species and are an important determinant of community structure, ecosystem functions, and population dynamics. Our objective was to quantify how intraspecific variability in big sagebrush, Artemisia tridentata, including three subspecies and two ploidy levels, is related to subspecies’ reaction to conspecific neighbor presence. Neighbor interactions can alter population growth rate via competition or facilitation depending on specific environmental conditions. Using a long-term common garden experiment, we developed spatially-explicit hierarchical models to quantify the effects of size-structured crowding on plant growth and survival. We found that neighbor interactions can vary significantly over time and space, and tend to be more pronounced under wetter and cooler climate conditions. We further tested if water availability, one of the major limiting factors in arid ecosystems, can underlie competitive interactions in a common garden, including density dependence. We used a deuterium-tracer experiment to quantify belowground zone of influence and crowding effect on plant water uptake. The results suggest that intraspecific variability in lateral root extent may be linked to subspecies identity and ploidy level. We did not find strong evidence that neighbor presence and size can alter water uptake from a shallow soil horizon, potentially suggesting size-independent partitioning of water resources between neighboring plants. We further hypothesize that variability in root architecture may reflect an axis for ecohydrological niche segregation contributing to the process of plant coexistence and evolution in heterogeneous landscapes. Our study complements previous knowledge of belowground processes in big sagebrush populations, including patterns of resource acquisition, and indicates promising avenues for further research of the ecology and evolution of this species. The results highlight how local plant-plant interactions can be a source of variation in common garden experiments, which are used to evaluate adaptive capacity and seed transfer zone development for A. tridentata populations. Potential applications of our work include planting density recommendations for big sagebrush in applied and experimental contexts, and provide mechanistic understanding of intraspecific diversification and ecological tradeoffs related to local adaptations

Investigating Seasonal Growth Patterns and Drought in Big Sagebrush

As climate change continues to alter global temperatures and weather patterns, sagebrush ecosystems have been adversely affected by an increase in dry seasons and wildfires. We sought to investigate how seasonal growth patterns of big sagebrush (Artemisia tridentate) respond to droughts. Two growing conditions are the focus of this experiment: topographic aspect and plant age. We marked current year\u27s stems on 26 sagebrush plants for size change measurements every other week, and split the sample size evenly on south- and north-facing slopes. While the monitoring is still ongoing, our preliminary results show a difference depending on which slope the plants are growing on. Sagebrush stem length on north-facing slopes were on average 39% longer than sagebrush stems growing on south-facing slopes. This result was unexpected because south-facing sagebrush experienced more optimal growing conditions in early spring due to longer snow retention on the north slope. We hypothesize that past growing conditions and ecological memory may be affecting current growth patterns for south- and north-facing shrubs. Such information can aid future restoration decisions and help promote quicker recovery of this important plant that so many organisms depend on

Comparing Parameters in Growth Models

Parameters are of significant importance when using non-linearized models. A small shift in one parameter could change an accurate model to an inaccurate one. But to what extent can these parameters change and not affect the accuracy of the model? This question can be answered by setting a specific value in the model to reach and then output the time it took each model to arrive at that value holding all else equal. In these simulations the value used was K/2, which is the half the max population. The initial population and growth rate were the two parameters that were altered over the different simulations of the model. The models had to analytically be solved for time in order to generate the correct output. After running these simulations the relationships the parameters have to the time it takes to reach K/2 is shown. The Gompertz model has steeper contour lines, and seemed to depend less on the initial population and more on the growth rate, whereas the logarithmic model seemed to have a more balanced dependence on both parameters

Investigating Seasonal Growth Patterns and Drought in Big Sagebrush

As climate change continues to alter global temperatures and weather patterns, sagebrush ecosystems have been adversely affected by an increase in dry seasons and wildfires. We sought to investigate how seasonal growth patterns of big sagebrush (Artemisia tridentate) respond to droughts. Two growing conditions are the focus of this experiment: topographic aspect and plant age. We marked current year\u27s stems on 26 sagebrush plants for size change measurements every other week, and split the sample size evenly on south- and north-facing slopes. While the monitoring is still ongoing, our preliminary results show a difference depending on which slope the plants are growing on. Sagebrush stem length on north-facing slopes were on average 39% longer than sagebrush stems growing on south-facing slopes. This result was unexpected because south-facing sagebrush experienced more optimal growing conditions in early spring due to longer snow retention on the north slope. We hypothesize that past growing conditions and ecological memory may be affecting current growth patterns for south- and north-facing shrubs. Such information can aid future restoration decisions and help promote quicker recovery of this important plant that so many organisms depend on

Key business challenges under martial law

1. Ливч Д. Нове обличчя українського підприємництва. Економічна правда. 2022. URL: https://www.epravda.com.ua/columns/2022/08/29/690937/; 2. Жирій К. Час для роботи: як відновлювався та адаптувався український бізнес за рік війни. УНІАН в Google News. 2023. URL: https://www.unian.ua/economics/finance/chas-dlya-roboti-yak-vidnovlyuvavsya-ta-adaptuvavsya-ukrajinskiy-biznes-za-rik-viyni-12154170.html; 3. Biliavska, Yu., Mykytenko, N., Romat, Ye., & Biliavskyi, V. (2023). Category management: Industry vs trade. Scientific Horizons, 26 (1), 129–150.Для країни підприємства є запорукою стабільного фінансування й економічної стійкості, тому стан вітчизняних підприємств привертає не менш важливу уваги під час війни. Підприємства зіштовхуються з непередбачуваними ризиками з квітня 2022 року, що змушують їх відшуковувати нові методи й підходи щодо вирішення проблем які спричиненні воєнним вторгненням з боку РФ. Актуальність питання ефективного управління ризиками на підприємстві підкріплюється правовим режимом воєнного стану в України.For the country, enterprises are a guarantee of stable financing and economic stability, therefore the state of domestic enterprises attracts no less important attention during the war. Businesses are faced with unpredictable risks from April 2022, which force them to look for new methods and approaches to solving problems caused by the military invasion by the Russian Federation. The relevance of the issue of effective risk management at the enterprise is reinforced by the legal regime of martial law in Ukraine.Національний університет «Києво-Могилянська академія

Comparison of Image Processing Methods for Better Point Clouds of Sagebrush

Accurate and comprehensive monitoring, where information can be collected across multiple scales and be spatially referenced on a continual basis, is needed to create better models for sagebrush restoration efforts. By using remote sensing techniques like unoccupied aerial vehicles (UAV), researchers can collect in an afternoon flight equivalent data to field-based methods that would take days to accumulate. In this study, we used Agisoft Metashape software to process UAV imagery taken at the Soda common garden in 2019 and again in 2020. Our objective was to test the impacts of several image processing parameters on final products including point clouds. We found that changes to the parameters in Agisoft Metashape did not produce any large differences in point cloud products. However, we did find a noticeable difference in the quality of images from flights in June 2019 and September 2020. Because the images were taken at different times of year, we found the software had difficulty detecting the sagebrush in the 2020 images due to the lack of leaves, and the longer shadows cast in the fall, which resulted in a lower quality point cloud. Based on these results, our next steps will focus on testing other parameters to improve the final products generated from UAS flights in both spring and fall seasons