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Although the importance of edaphic factors and habitat structure for plant growth and survival is known, both are often neglected in favor of climatic drivers when investigating the spatial patterns of plant species and diversity. Yet, especially in mountain ecosystems with complex topography, missing edaphic and habitat components may be detrimental for a sound understanding of biodiversity distribution. Here, we compare the relative importance of climate, soil and land cover variables when predicting the distributions of 2,616 vascular plant species in the European Alps, representing approximately two-thirds of all European flora. Using presence-only data, we built point-process models (PPMs) to relate species observations to different combinations of covariates. We evaluated the PPMs through block cross-validations and assessed the independent contributions of climate, soil, and land cover covariates to predict plant species distributions using an innovative predictive partitioning approach. We found climate to be the most influential driver of spatial patterns in plant species with a relative influence of ~58.5% across all species, with decreasing importance from low to high elevations. Soil (~20.1%) and land cover (~21.4%), overall, were less influential than climate, but increased in importance along the elevation gradient. Furthermore, land cover showed strong local effects in lowlands, while the contribution of soil stabilized at mid-elevations. The decreasing influence of climate with elevation is explained by increasing endemism, and the fact that climate becomes more homogeneous as habitat diversity declines at higher altitudes. In contrast, soil predictors were found to follow the opposite trend. Additionally, at low elevations, human-mediated land cover effects appear to reduce the importance of climate predictors. We conclude that soil and land cover are, like climate, principal drivers of plant species distribution in the European Alps. While disentangling their effects remains a challenge, future studies can benefit markedly by including soil and land cover effects when predicting species distributions.

Environmental filtering and dispersal limitation can both maintain diversity in plant communities by aggregating conspecifics, but parsing the contribution of each process has proven difficult empirically. Here, we assess the contribution of filtering and dispersal limitation to the spatial aggregation patterns of 456 tree species in a hyperdiverse Amazonian forest and find distinct functional trait correlates of interspecific variation in these processes. Spatial point process model analysis revealed that both mechanisms are important drivers of intraspecific aggregation for the majority of species. Leaf drought tolerance was correlated with species topographic distributions in this aseasonal rainforest, showing that future increases in drought severity could significantly impact community structure. In addition, seed mass was associated with the spatial scale and density of dispersal-related aggregation. Taken together, these results suggest environmental filtering and dispersal limitation act in concert to influence the spatial and functional structure of diverse forest communities.

Neuronal activity correlations are key to understanding how populations of neurons collectively encode information. While two-photon calcium imaging has created a unique opportunity to record the activity of large populations of neurons, existing methods for inferring correlations from these data face several challenges. First, the observations of spiking activity produced by two-photon imaging are temporally blurred and noisy. Secondly, even if the spiking data were perfectly recovered via deconvolution, inferring network-level features from binary spiking data is a challenging task due to the non-linear relation of neuronal spiking to endogenous and exogenous inputs. In this work, we propose a methodology to explicitly model and directly estimate signal and noise correlations from two-photon fluorescence observations, without requiring intermediate spike deconvolution. We provide theoretical guarantees on the performance of the proposed estimator and demonstrate its utility through applications to simulated and experimentally recorded data from the mouse auditory cortex.

Settlement systems are often characterized by a mixture of different site types, each with potentially different locational properties reflected by differences in their functions and uses. Prehistoric settlements in China are commonly known for their wooden defense structures and rammed earth. However, from the late Neolithic period, ca. 2800 BCE, a new type of stone-wall site emerged in northern China, coexisting with earth-wall sites. Examining differences in the locational properties of stone-wall and earth-wall settlements is essential for understanding regional settlement patterns and human–environment interactions in prehistoric northern China. Studies of this topic have so far been limited to descriptive qualitative accounts, and formal statistical comparisons of their differences have yet to be carried out. This paper contributes to this research agenda by examining, via point process models (PPMs), stone-wall and earth-wall sites associated with the Lower Xiajiadian Culture (2000–1400 BCE) in the Aohan Banner, northern China. We fitted log-linear and generalized additive models (GAMs) and identified the relevance of key spatial covariates via information criterion importance for both site types. Our results highlight not only the spatial preferences of stone-wall and earth-wall sites but also some differences, suggesting a defensive function of the former site type.

The three aftershock sequences that occurred in Al Hoceima, Morocco, in May 1994 (Mw 6.0), February 2004 (Mw 6.4) and January 2016 (Mw 6.3) were stochastically modeled to investigate their temporal and energetic behavior. A form of the restricted trigger model known as the restricted epidemic type aftershock sequence (RETAS) was used for the temporal analysis of the selected series. The best-determined fit models for each sequence differ based on the Akaike information criteria. The revealed discrepancies suggest that, although the activated fault systems are close (within 10 to 20 km), their stress regimes change and shift across each series. In addition, a stochastic model was presented to study the strain release following a specific strong earthquake. This model was constructed using a compound Poisson process and depicted the progression of the strain release during the aftershock sequence. The proposed model was then applied to the data. After the RETAS model was used to evaluate the behavior of the aftershock decay rate, the best-fit model was obtained and integrated into the strain-release stochastic analysis. By detecting the potential disparities between the observed data and model, the applied stochastic model of strain release allows for a more comprehensive examination. Furthermore, comparing the observed and expected cumulative energy release numbers revealed some variations at the start of all three sequences. This demonstrates that significant aftershock clusters occur more frequently shortly after the mainshock at the start of the sequence rather than if they are assumed to occur randomly.

Introduction: Revealing the spatial distribution pattern and formation mechanism of species in a community can provide important clues for community renewal, succession, and diversity maintenance mechanisms.Methods: In this study, we employed spatial point process modeling to identify and quantify the processes contributing to the spatial distribution of species. Simultaneously, we explored the relationship between functional traits and species spatial distribution characteristics in conjunction with phylogenetic studies.Results: The results revealed that the LGCP model effectively described all species, indicating that the spatial pattern of species may be influenced by a combination of environmental filtering and dispersal limitation. Disparities in species spatial distribution were elucidated by characterizing functional traits, such as body size and resource conservation. Incorporating phylogenetic information enhanced the predictive capacity of functional traits in explaining species spatial distribution.Discussion: This study underscores the significance of the joint effects of environmental filtering and dispersal limitation in generating species spatial distribution patterns. Integrating spatial point process models with considerations of functional traits and phylogeny proves to be an effective approach for comprehending the mechanisms governing species combinations.

This study attempts to understand the dependence on abiotic factors and on the biotic process of the population development. We used three spatial point process models (Poisson, Area-Interaction and shot-noise Cox processes) in both homogenous and inhomogeneous versions to model the distribution of three Carex remota cohorts in wet zones of a temperate forest in the north of Spain. The cohorts studied were adults and seedlings born in two consecutive years. With the use of these models we are able to simulate separately and jointly the effect on plant distribution of a homogeneous or heterogeneous habitat, and the absence or presence of some biotic processes, as seed dispersal and/or density-dependent interactions. The result of the bivariate function analysis does not reveal sufficient evidences, but suggests a weak positive relation between adults and seedlings that survived a dry period in the first summer. Models from the three cohorts show a decreasing degree of clustering from seedlings to adults. Besides, the results show that the importance of the main factors that explain the population structure changes along the development of Carex stages. Compared to seedlings, the adults pattern shows an increasing dependence on abiotic factors.

We describe a class of models for the investigation of possible raised risk of disease around putative sources of environmental pollution. An adaptation of the point process method suggested by Diggle and Rowlingson is presented, allowing the use of routinely available aggregated data and incorporating the more general distance–risk model suggested by Elliott and co‐workers. An application to data on cancers of the stomach around municipal solid waste incinerators is presented.

Species distribution modeling (SDM) is widely used in ecology and conservation. Currently, the most available data for SDM are species presence-only records (available through digital databases). There have been many studies comparing the performance of alternative algorithms for modeling presence-only data. Among these, a 2006 paper from Elith and colleagues has been particularly influential in the field, partly because they used several novel methods (at the time) on a global data set that included independent presence–absence records for model evaluation. Since its publication, some of the algorithms have been further developed and new ones have emerged. In this paper, we explore patterns in predictive performance across methods, by reanalyzing the same data set (225 species from six different regions) using updated modeling knowledge and practices. We apply well-established methods such as generalized additive models and MaxEnt, alongside others that have received attention more recently, including regularized regressions, point-process weighted regressions, random forests, XGBoost, support vector machines, and the ensemble modeling framework biomod. All the methods we use include background samples (a sample of environments in the landscape) for model fitting. We explore impacts of using weights on the presence and background points in model fitting. We introduce new ways of evaluating models fitted to these data, using the area under the precision-recall gain curve, and focusing on the rank of results. We find that the way models are fitted matters. The top method was an ensemble of tuned individual models. In contrast, ensembles built using the biomod framework with default parameters performed no better than single moderate performing models. Similarly, the second top performing method was a random forest parameterized to deal with many background samples (contrasted to relatively few presence records), which substantially outperformed other random forest implementations. We find that, in general, nonparametric techniques with the capability of controlling for model complexity outperformed traditional regression methods, with MaxEnt and boosted regression trees still among the top performing models. All the data and code with working examples are provided to make this study fully reproducible.