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We are interested in the problem of classifying commuting and foraging behaviour of bats at delimited geographical areas, namely sites, throughout France. To predict the majority behaviour on these sites, we use echolocation call data recorded as part of Vigie-Chiro participatory project. As the temporal distribution of calls is a relevant indicator of behaviour, providing an adequate model of this distribution is a matter of great interest. Given the self-exciting dynamics observed in foraging behaviour, we propose to model bat calls by Hawkes processes. Specifically, we consider that the start time of each call emitted on a site is an event of a Hawkes process. Taking advantage of this modelling, we use a suitable procedure that relies on the empirical risk minimization principle to discriminate between the 2 classes. Then, the performance of the procedure is assessed on synthetic data through comprehensive numerical experiments. The overall methodology is evaluated with a goodness-of-fit test. Finally, we present the obtained results on the real data set. The classification results are convincing and show the relevance of our method, which could contribute to a better understanding of behavioural determinants and open up broad perspectives in spatial ecology. [ABSTRACT FROM AUTHOR]

Point process provides a mathematical framework for characterizing neuronal spiking activities. Classical point process methods often focus on the conditional intensity function, which describes the likelihood at any time point given its spiking history. However, these models do not describe the central tendency or importance of the spike train observations. Based on the recent development on the notion of center-outward rank for point process, we propose a new modeling framework on spike train data. The new likelihood of a spike train is a product of the marginal probability on the number of spikes and the probability of spike timings conditioned on the same number. In particular, the conditioned distribution is calculated by adopting the well-known Isometric Log-Ratio transformation. We systematically compare the new likelihood with the state- of-the-art point process likelihoods in terms of ranking, outlier detection, and classification using simulations and real spike train data. This new framework can effectively identify templates as well as outliers in spike train data. It also provides a reasonable model, and the parameters can be efficiently estimated with conventional maximum likelihood methods. It is found that the proposed likelihood provides an appropriate ranking on the spike train observations, effectively detects outliers, and accurately conducts classification tasks in the given data. [ABSTRACT FROM AUTHOR]

Marine protected areas (MPAs) and associated management measures are being implemented to conserve marine benthic species. To make effective decisions, marine managers need data on the distributions of species of interest and anthropogenic pressures, but also the potential connectivity between habitat patches.To explore how predictive modelling can be utilized in such a process, a model was developed to predict the distribution of northern sea fan, Swiftia pallida, habitat around parts of Scotland, Northern Ireland, and Ireland.The predicted distribution of suitable habitat was compared with spatial data on mobile bottom‐contacting fishing activity, and the location of MPAs designated for benthic features, and the management measures within MPAs that restrict mobile bottom‐contacting fishing activity, which is the main pressure‐causing activity that S. pallida are sensitive to.Over 20% of predicted suitable habitat is within MPAs, over 10% is within MPA management measures and over 48% is within areas that have experienced no bottom contacting‐fishing activity, which is significantly higher than the equivalent values for the study region as a whole. However, patches were identified that potentially experience above average levels of fishing activity and remain unmanaged, including within MPAs designated for associated features. The analysis also highlighted patches that could be candidates for monitoring recovery and the locations of unknown populations.For each patch of suitable habitat, the number of other patches within 22 km, a previously published estimate of dispersal distance for S. pallida, was used as a proxy for connectedness. Connectedness was estimated to be greatest for patches towards the centre and west of the study region.The results indicate how the outputs of predictive distribution models can be used in conjunction with other data to prioritize areas for surveys and identify locations where effective management may facilitate conservation or the recovery of benthic species. [ABSTRACT FROM AUTHOR]

Point process provides a mathematical framework for characterizing neuronal spiking activities. Classical point process methods often focus on the conditional intensity function, which describes the likelihood at any time point given its spiking history. However, these models do not describe the central tendency or importance of the spike train observations. Based on the recent development on the notion of center-outward rank for point process, we propose a new modeling framework on spike train data. The new likelihood of a spike train is a product of the marginal probability on the number of spikes and the probability of spike timings conditioned on the same number. In particular, the conditioned distribution is calculated by adopting the well-known Isometric Log-Ratio transformation. We systematically compare the new likelihood with the state-of-the-art point process likelihoods in terms of ranking, outlier detection, and classification using simulations and real spike train data. This new framework can effectively identify templates as well as outliers in spike train data. It also provides a reasonable model, and the parameters can be efficiently estimated with conventional maximum likelihood methods. It is found that the proposed likelihood provides an appropriate ranking on the spike train observations, effectively detects outliers, and accurately conducts classification tasks in the given data.

Future global energy demand may be met through increased extraction of fossil fuels and production of renewable energy such as biofuels. Renewable energy from biofuels is often proposed as an environmentally friendly alternative to fossil fuels; however, impacts of renewable energy sources on wildlife populations have rarely been evaluated in working landscapes. We used North American Breeding Bird Survey data (1998 to 2021) to assess whether the joint effects of oil and gas and biofuel crop production explained grassland bird population declines. We modeled location‐specific effects of land use on grassland bird habitat use for four grassland bird species (bobolink [Dolichonyx oryzivorus], grasshopper sparrow [Ammodramus savannarum], Savannah sparrow [Passerculus sandwichensis], and western meadowlark [Sturnella neglecta]) in North Dakota, a state experiencing rapid growth in both energy sectors. Our analysis showed that grassland birds responded more negatively to biofuel feedstocks (i.e., corn and soybeans) on the landscape compared with oil and gas development. Furthermore, we found that the effect of feedstocks was not generalizable to other forms of agricultural land use. When combined, these land use changes resulted in distributional shifts of grassland birds, with use by birds being lower in regions dominated by biofuel production, which appears partially responsible for observed abundance trends at the state level. Our results indicate that expansion of oil and gas development has negatively affected habitat use by some grassland birds, but this impact was more localized when compared to biofuel crops. Conservation practitioners may need to adapt their conservation strategies to account for widespread and rapid land use change driven by United States energy policies. [ABSTRACT FROM AUTHOR]

The ever-growing popularity of citizen science, as well as recent technological and digital developments, have allowed the collection of data on species' distributions at an extraordinary rate. In order to take advantage of these data, information of varying quantity and quality needs to be integrated. Point process models have been proposed as an elegant way to achieve this for estimates of species distributions. These models can be fitted efficiently using Bayesian methods based on integrated nested Laplace approximations (INLA) with stochastic partial differential equations (SPDEs). This approach uses an efficient way to model spatial autocorrelation using a Gaussian random field and a triangular mesh over the spatial domain. The mesh is constructed by user-defined variables, so effectively represents a free parameter in the model. However, there is a lack of understanding about how to set these mesh parameters, and their effect on model performance. Here, we assess how mesh parameters affect predictions and model fit to estimate the distribution of the serotine bat, Eptesicus serotinus, in Great Britain. A Bayesian INLA model was fitted using five meshes of varying densities to a dataset comprising both structured observations from a national monitoring programme and opportunistic records. We demonstrate that mesh density impacted spatial predictions with a general loss of accuracy with increasing mesh coarseness. However, we also show that the finest mesh was unable to overcome spatial biases in the data. In addition, the magnitude of the covariate effects differed markedly between meshes. This confirms that mesh parameterisation is an important and delicate process with implications for model inference. We discuss how species distribution modellers might adapt their use of INLA in the light of these findings. [ABSTRACT FROM AUTHOR]

Habitat loss and fragmentation are major threats to primate populations globally. The Endangered golden monkey Cercopithecus mitis kandti is only found in two small forest fragments: the Virunga massif in Rwanda, Uganda and the Democratic Republic of the Congo, and the Gishwati Forest in Rwanda. Little is known about the abundance and distribution of this subspecies, or threats to its survival. During 2007–2018, we collected data along 893.7 km of line transects and 354.2 km of recce trails in Volcanoes National Park and in Gishwati–Mukura National Park to estimate golden monkey density and examine any threats. In Volcanoes National Park, golden monkeys were found almost exclusively in the bamboo zone, and in Gishwati–Mukura National Park they occurred only in the remnant tropical montane Gishwati Forest. In Volcanoes National Park, density was estimated to be 7.89 (95% CI: 3.85–16.19), 5.41 (2.64–11.08), and 5.47 (3.68–8.14) groups per km2 in 2007, 2011 and 2017–2018, respectively. This corresponds to a total of 4,331 individuals (95% CI: 2,723–5,938) in 2007, 4,487 (2,903–6,071) in 2011 and 4,626 (4,165–5,088) in 2017–2018. In Gishwati Forest, group density averaged 1.98 (95% CI: 1.27–3.16) per km2, corresponding to 172 (95% CI: 154–190) individuals in 2017–2018. Survey results from Volcanoes National Park suggest that the golden monkey population has been stable during 2007–2018. Limited habitat, illegal activities such as harvesting of bamboo and firewood, and the presence of feral dogs, threaten the golden monkey in Rwanda and require continued monitoring. The development of a conservation action plan is a priority to protect this species.

Habitat loss and fragmentation are major threats to primate populations globally. The Endangered golden monkey Cercopithecus mitis kandti is only found in two small forest fragments: the Virunga massif in Rwanda, Uganda and the Democratic Republic of the Congo, and the Gishwati Forest in Rwanda. Little is known about the abundance and distribution of this subspecies, or threats to its survival. During 2007–2018, we collected data along 893.7 km of line transects and 354.2 km of recce trails in Volcanoes National Park and in Gishwati–Mukura National Park to estimate golden monkey density and examine any threats. In Volcanoes National Park, golden monkeys were found almost exclusively in the bamboo zone, and in Gishwati–Mukura National Park they occurred only in the remnant tropical montane Gishwati Forest. In Volcanoes National Park, density was estimated to be 7.89 (95% CI: 3.85–16.19), 5.41 (2.64–11.08), and 5.47 (3.68–8.14) groups per km2 in 2007, 2011 and 2017–2018, respectively. This corresponds to a total of 4,331 individuals (95% CI: 2,723–5,938) in 2007, 4,487 (2,903–6,071) in 2011 and 4,626 (4,165–5,088) in 2017–2018. In Gishwati Forest, group density averaged 1.98 (95% CI: 1.27–3.16) per km2, corresponding to 172 (95% CI: 154–190) individuals in 2017–2018. Survey results from Volcanoes National Park suggest that the golden monkey population has been stable during 2007–2018. Limited habitat, illegal activities such as harvesting of bamboo and firewood, and the presence of feral dogs, threaten the golden monkey in Rwanda and require continued monitoring. The development of a conservation action plan is a priority to protect this species. [ABSTRACT FROM AUTHOR]

Aim: Rapid advances in technology have created tools able to explore how animals differ in their use of the environment based on behaviour, which can provide insights into the ecology of endangered animals and the threats they face. Though threatened, we know little of vultures' spatial ecology. We examine the external variables that affect vultures' decisions regarding space use, specifically during foraging and feeding when they are at greatest risk for encountering threats, and use this to inform effective conservation interventions. Location: Tanzania, specifically Southern Tanzanian protected area networks. Taxon: Gyps africanus. Methods: We tracked 26 African white‐backed vultures between 2015 and 2019 in Southern Tanzania. Using hidden Markov models to identify behaviour from raw GPS points and point process models to spatially analyse these behaviours, we tested resource selection decisions in vultures during foraging and feeding, when they are at greatest risk of poisoning. Results: African white‐backed vultures are most likely to forage early in the day outside National Parks, specifically in Game Reserves and Wildlife Management Areas, but avoid areas with high livestock numbers to feed. Eastern and Western populations showed slight differences in preferences, highlighting the need for population level considerations. When not selecting for behaviour, closeness to rivers and habitat openness were more important than protected area status for predicting vulture use. Main conclusions Through combining complex analyses, we identified results which simple statistical analyses could not offer. Hidden Markov models and point process models are complementary and can be used to define specific behaviours and associated resource selection. These results provide insight into how animals use their habitat explicitly rather than describing where they spend most of their time. This adds to the growing evidence that although National Parks are important, the management of surrounding areas must be included in conservation efforts. [ABSTRACT FROM AUTHOR]

Monumental rectangular stone structures called mustatils are an important emerging feature of the Holocene archaeological record of northwestern Arabia. To date, few have been excavated, with available radiocarbon dates suggesting an age range of ca. 5400–4200 BC. Here we present a rigorous spatial analysis to identify the patterning and landscape context of 169 mustatils in the southern and western margins of the Nefud Desert. This included: (1) a systematic survey of satellite imagery to identify mustatils; (2) viewshed analysis to examine location and landscape visibility; (3) a point process model to understand how diverse environmental and landscape variables affect mustatil locations; (4) mark correlation function to assess spatial patterning of mustatils based on their size. Results indicate that mustatil locations are determined most by proximity to water (likely locations of enhanced surface water occurrence under the enhanced humidity of the Mid-Holocene), on east facing slopes, close to rocky areas, at elevations between 880 and 950 masl, and on or near topographic ridges (positive topographic position index). Viewshed analysis showed that mustatils are preferentially located in areas that have good views, but not the best that are available, indicating complex landscape positioning that balances a range of topographic and behavioural factors. Using a rank permutation method with size (length) of mustatils as a proxy for labour mobilization we show that mustatils within clusters are not arranged hierarchically based on size, and were likely built by non-stratified groups of people. Our analyses show that people were choosing multiple factors when deciding where to build mustatils, and that the distribution of mustatils may relate to different groups of people resulting in the construction of complex ritual landscapes. As one of the earliest examples of large-scale monumental stone structure construction in global prehistory, understanding mustatils can enlighten us on human-environment interaction during the Neolithic. [ABSTRACT FROM AUTHOR]

Group living in species can have complex consequences for individuals, populations, and ecosystems. Therefore, estimating group density and size is often essential for understanding population dynamics, interspecific interactions, and conservation needs of group‐living species. Spatial capture–recapture (SCR) has been used to model both individual and group density in group‐living species, but modeling either individual‐level or group‐level detection results in different biases due to common characteristics of group‐living species, such as highly cohesive movement or variation in group size. Furthermore, no SCR method currently estimates group density, individual density, and group size jointly. Using clustered point processes, we developed a cluster SCR model to estimate group density, individual density, and group size. We compared the model to standard SCR models using both a simulation study and a data set of detections of African wild dogs (Lycaon pictus), a group‐living carnivore, on camera traps in northern Botswana. We then tested the model's performance under various scenarios of group movement in a separate simulation study. We found that the cluster SCR model outperformed a standard group‐level SCR model when fitted to data generated with varying group sizes, and mostly recovered previous estimates of wild dog group density, individual density, and group size. We also found that the cluster SCR model performs better as individuals' movements become more correlated with their groups' movements. The cluster SCR model offers opportunities to investigate ecological hypotheses relating group size to population dynamics while accounting for cohesive movement behaviors in group‐living species. [ABSTRACT FROM AUTHOR]

Objective . Oscillations figure prominently as neurological disease hallmarks and neuromodulation targets. To detect oscillations in a neuron's spiking, one might attempt to seek peaks in the spike train's power spectral density (PSD) which exceed a flat baseline. Yet for a non-oscillating neuron, the PSD is not flat: The recovery period ('RP', the post-spike drop in spike probability, starting with the refractory period) introduces global spectral distortion. An established 'shuffling' procedure corrects for RP distortion by removing the spectral component explained by the inter-spike interval (ISI) distribution. However, this procedure sacrifices oscillation-related information present in the ISIs, and therefore in the PSD. We asked whether point process models (PPMs) might achieve more selective RP distortion removal, thereby enabling improved oscillation detection. Approach . In a novel 'residuals' method, we first estimate the RP duration ( n r ) from the ISI distribution. We then fit the spike train with a PPM that predicts spike likelihood based on the time elapsed since the most recent of any spikes falling within the preceding n r milliseconds. Finally, we compute the PSD of the model's residuals. Main results . We compared the residuals and shuffling methods' ability to enable accurate oscillation detection with flat baseline-assuming tests. Over synthetic data, the residuals method generally outperformed the shuffling method in classification of true- versus false-positive oscillatory power, principally due to enhanced sensitivity in sparse spike trains. In single-unit data from the internal globus pallidus (GPi) and ventrolateral anterior thalamus (VLa) of a parkinsonian monkey-in which alpha-beta oscillations (8-30 Hz) were anticipated-the residuals method reported the greatest incidence of significant alpha-beta power, with low firing rates predicting residuals-selective oscillation detection. Significance . These results encourage continued development of the residuals approach, to support more accurate oscillation detection. Improved identification of oscillations could promote improved disease models and therapeutic technologies., (Creative Commons Attribution license.)

In mast‐seeding species, strong annual variation in seed production is assumed to drive seed fate and ultimately plant recruitment. However, the effects of temporal variation in seed crops on spatial patterns of seed rain and recruitment are poorly understood, in part because of limited data on fine‐scale spatial variation of seed deposition. To investigate how mast‐seeding affects spatial variation in seed rain, we analysed a 15‐year data set on seed rain and population dynamics in a montane old‐growth forest (2 plots with 81 seed traps per ha), dominated by European beech Fagus sylvatica and, to a lesser degree, Norway spruce Picea abies and European silver fir Abies alba. Using geostatistical methods and point process modelling, we modeled 1) the relationship between annual seed crop and spatial heterogeneity of seed rain, 2) the temporal stability of seed rain hotspots and 3) the spatial relation of seed rain and recruitment into the population. The results illustrate the interaction between temporal and spatial variation in seed production and its consequences for population dynamics of mast‐seeding trees. The signature of annual variation in seed rain fades with seedling mortality over time in all three focal species. For beech, the cumulative probability of seed arrival over the whole study period drives spatial seedling and sapling dispersion patterns – mediated by light competition with older trees; seedling density was most strongly reduced by sapling density, while saplings were most frequently found in areas of low adult density. Such decoupling of spatial processes across life history stages, combined with spatial heterogeneity may play a larger role in driving stand‐level spatial dynamics than previously assumed. [ABSTRACT FROM AUTHOR]

Aim: While species distribution models (SDMs) are standard tools to predict species distributions, they can suffer from observation and sampling biases, particularly presence‐only SDMs, which often rely on species observations from non‐standardized sampling efforts. To address this issue, sampling background points with a target‐group strategy is commonly used, although more robust strategies and refinements could be implemented. Here, we exploited a dataset of plant species from the European Alps to propose and demonstrate efficient ways to correct for observer and sampling bias in presence‐only models. Innovation: Recent methods correct for observer bias by including covariates related to accessibility in model calibrations (classic bias covariate correction, Classic‐BCC). However, depending on how species are sampled, accessibility covariates may not sufficiently capture observer bias. Here, we introduced BCCs more directly related to sampling effort, as well as a novel corrective method based on stratified resampling of the observational dataset before model calibration (environmental bias correction, EBC). We compared, individually and jointly, the effect of EBC and different BCC strategies, when modelling the distributions of 1,900 plant species. We evaluated model performance with spatial block split‐sampling and independent test data, and assessed the accuracy of plant diversity predictions across the European Alps. Main conclusions: Implementing EBC with BCC showed best results for every evaluation method. Particularly, adding the observation density of a target group as a bias covariate (Target‐BCC) gave the most realistic modelled species distributions, with a clear positive correlation (r ≃.5) found between predicted and expert‐based species richness. Although EBC must be carefully implemented in a species‐specific manner, such limitations may be addressed via automated diagnostics included in a provided R function. Implementing EBC and bias covariate correction together may allow future studies to address efficiently observer bias in presence‐only models, and overcome the standard need of an independent test dataset for model evaluation. [ABSTRACT FROM AUTHOR]

Extensive urbanization in coastal southern California has reduced natural habitat in this biodiversity hotspot. To better conserve ecological communities, state and federal agencies, along with local jurisdictions and private stakeholders, developed regional conservation plans for southern California. Although many protected areas exist within this region, the patchwork nature of these protected areas might not provide good coverage for species that require multiple habitat components, such as amphibians with complex life histories. Because of declines in the past century, the status of the western spadefoot (Spea hammondii) in southern California is of concern to state and federal wildlife agencies. Species distribution models (SDMs) can aid in determining the conservation status of imperiled species by projecting where suitable habitat remains and how much is protected from further development. We built SDMs that integrated site-occupancy data from systematic pitfall trapping surveys and presence-only data from biodiversity databases and citizen science platforms to project the current distribution of western spadefoots in southern California. Western spadefoot occurrence was positively related to the cover of grassland or shrub/scrub and the % sand in the soil within a 1000 m buffer, and was negatively related to slope, elevation, and distance to ephemeral streams or vernal pools. Most of the remaining unprotected habitat for western spadefoots is in the southern half of its historical range in western San Diego and Riverside counties. A few large tracts of spadefoot habitat exist on U.S. Department of Defense lands and smaller tracts remain on ecological reserves owned by state and local government agencies. Only small patches of habitat remain in the northern half of this clade’s historical range in Ventura, Orange, Los Angeles, and San Bernardino counties. Existing regional conservation plans provide ostensible spatial coverage of the majority of extant habitat for western spadefoots in southern California, but most of the habitat within the jurisdiction of these plans lacks formal protection, exposing this species to further declines as urbanization continues in the 21st century.

The spatial analysis of traffic accidents has long been a useful tool for authorities to implement effective preventive measures. Initial studies were conducted at the areal level considering administrative or traffic-related units, but a more precise analysis at the street level is necessary for developing targeted interventions. In recent years, there has been a significant increase in studies conducted at the road network level, which require using new statistical techniques that are suitable for linear networks. However, modeling accident counts at the street level presents several challenges, primarily due to the need for accurate georeferenced data to correctly assign events to specific streets or road segments. Despite advancements in geocoding methods, discrepancies can still arise between the true event locations and the locations mapped by a geocoding method. In this paper, we propose a model to deal with the presence of location uncertainty and enable an analysis of accident intensity constrained to the road network. The model does not assume any specific mechanism for location uncertainty, as this reflects the most common practical scenario. By tackling this inherent problem, the proposed model aims to enhance the accuracy of accident analysis and contribute to the development of effective preventive measures for traffic safety. The model is evaluated with both a simulation study and a case study on the city of Valencia, Spain. For the latter, the proposed model reveals a greater association of road intersections with accident rates than that estimated by the standard model. [ABSTRACT FROM AUTHOR]

Some large-seeded plants lack effective seed dispersal agents when they are introduced as ornamental plants to new areas, but can rapidly colonize a landscape if seed dispersal functions are restored. We examined whether Gopherus polyphemus (Gopher Tortoise) facilitated the spread of Chrysobalanus icaco (Cocoplum; Chrysobalanaceae) over a 14-year period in a suburban nature preserve (in Jupiter, FL, USA) by: (i) comparing germination patterns among gut-passed, hand-depulped and whole fruit treatments, and (ii) testing hypotheses about environmental predictors of the spatial distribution of C. icaco , including information about G. polyphemus movement pathways and burrow locations. While we did not find a significant difference in the total proportion of C. icaco seeds that germinated in each treatment, time to event analysis revealed that seeds that were found in faeces germinated significantly earlier than seeds that were hand-depulped or that were planted as whole fruits, supporting a lone scarification effect. Point process modeling revealed that the density of C. icaco bushes was higher near G. polyphemus movement pathways and was lower inside Serenoa repens (Saw Palmetto) patches, supporting a positive effect of tortoise movement patterns on plant distributions. The density of C. icaco increased from west to east, consistent with westward dispersal from the four founder bushes on the east side of the study area. After removal of outliers, we also detected a negative association between C. icaco spatial density and G. polyphemus burrow density that was presumably explained by the fact that seeds defecated deep within burrows were unlikely to germinate and establish without secondary movement. The results suggest that G. polyphemus contributed to the rapid dispersal of C. icaco by scatter dispersal of seeds (via faeces) in areas where tortoises were active and that movement pathways provided suitable conditions for colonization. The spread of C. icaco by G. polyphemus over a relatively short period of time provides a valuable window into the earliest stages of the colonization process and further supports the role of Chelonians as effective seed dispersal agents for large-seeded plants. [ABSTRACT FROM AUTHOR]

Point process models have made a significant contribution to the prediction of football association outcomes. It is conventionally the case that defence and attack capabilities have been assumed to be constant during a match and estimated against the average performance of all other teams in history. Drawing upon a Bayesian method, this paper proposes a dynamic strength model which relaxes assumption of the constant teams' strengths and permits applying in-match performance information to calibrate them. An empirical study demonstrates that although the Bayesian model fails to achieve improvement in goal difference prediction, it registers clear achievements with regard to the prediction of the total number of goals and Win/Draw/Loss outcome prediction. When the Bayesian model bets against the SBOBet bookmaker, one of the most popular gaming companies among Asian handicaps fans, whose odds data were obtained from both the Win/Draw/Loss market and over–under market, it may obtain positive returns; this clearly contrasts with the process model with constant strengths, which fails to win money from the bookmaker. [ABSTRACT FROM AUTHOR]

Research Highlights: The spatial distribution of trees results from several ecological processes that can be difficult to measure. We applied a point process modelling approach that uses the diameter and species of neighbouring trees to represent inter-tree interactions through repulsive and attractive processes. Thinning treatments slightly influence the tree spatial distribution of trees in white spruce plantations. Integrating this “spatialiser” into growth models could help improve stand simulations following various thinning treatments over larger areas and longer periods. It could also allow for the use of spatially explicit models when tree position is not available. Background and Objectives: Tree spatial patterns result from several ecological processes and have important implications in forest ecology and management. The use of spatial information can significantly improve our understanding of forest structures. However, this implies intensive field work that is rarely integrated into forest inventories. The aims of this study were to develop a spatial distribution simulator of trees in white spruce plantations and to evaluate the influence of thinning treatments. Materials and Methods: A point process modelling approach was used to represent inter-tree interactions through repulsive and attractive process in white spruce (Picea glauca (Moench) Voss) plantations in eastern Quebec, Canada, that had been commercially thinned five years ago. Balsam fir (Abies balsamea (L.) Mill.) and hardwoods together can represent 30–40% of the basal area of these plantations. Results: The diameter and species of each tree’s two closest neighbours were found to be the most important predictors in explaining the observed distances between trees. Despite the short period since thinning treatments, results showed that the treatment had slight significant effects on tree interactions. However, their impact on the global spatial distribution of stands is quite limited. Conclusions: Using only a few readily-available variables (species and diameter of trees), this “spatialiser” will make it possible to assign spatial coordinates to trees and generate realistic stand spatial structures even after various silvicultural treatments.

Rainfall data play an important role in hydrological sciences. Lack of sufficient or qualified data usually leads to unreliable and inadequate results. In such cases, synthetic time series, e.g., generated rainfall time series, are of great importance for performing the watershed management activities. In most previous studies, statistical methods such as those of Markov chain and ARIMA methods have been applied for the rainfall generation at a single site. As the rainfall spatial pattern has a significant effect on the flow hydrograph characteristics, multisite rainfall generation plays its vital role in hydrology. In the present research, the Spatio-temporal Neyman-Scott rectangular pulses (STNSRP) method was applied and fitted to 50-year daily data taken from 20 Rain gauges in the Walnut Gulch watershed, USA. Rain gauges were divided into real rain gauges (14) and virtual rain gauges (6), and the performance of STNSRP model was assessed. Different statistics such as mean, variance and probability of dry days were calculated and compared with both the generated and the observed rainfall time series. The IDW method was used to produce rainfall maps using the generated rainfall values at real and virtual rain gauges. Results revealed that the STNSRP model has the ability to match the observed statistics adequately. Also, the rainfall map generated using the virtual rain gauges has less error as compared with the map generated from the rainfall values only at the real rain gauges.

Summary: Species distribution models (SDMs) are statistical tools used to develop continuous predictions of species occurrence. 'Integrated SDMs' (ISDMs) are an elaboration of this approach with potential advantages that allow for the dual use of opportunistically collected presence-only data and site-occupancy data from planned surveys. These models also account for survey bias and imperfect detection through the use of a hierarchical modelling framework that separately estimates the species–environment response and detection process. This is particularly helpful for conservation applications and predictions for rare species, where data are often limited and prediction errors may have significant management consequences. Despite this potential importance, ISDMs remain largely untested under a variety of scenarios. We performed an exploration of key modelling decisions and assumptions on an ISDM using the endangered Baird's tapir (Tapirus bairdii) as a test species. We found that site area had the strongest effect on the magnitude of population estimates and underlying intensity surface and was driven by estimates of model intercepts. Selecting a site area that accounted for the individual movements of the species within an average home range led to population estimates that coincided with expert estimates. ISDMs that do not account for the individual movements of species will likely lead to less accurate estimates of species intensity (number of individuals per unit area) and thus overall population estimates. This bias could be severe and highly detrimental to conservation actions if uninformed ISDMs are used to estimate global populations of threatened and data-deficient species, particularly those that lack natural history and movement information. However, the ISDM was consistently the most accurate model compared to other approaches, which demonstrates the importance of this new modelling framework and the ability to combine opportunistic data with systematic survey data. Thus, we recommend researchers use ISDMs with conservative movement information when estimating population sizes of rare and data-deficient species. ISDMs could be improved by using a similar parameterization to spatial capture–recapture models that explicitly incorporate animal movement as a model parameter, which would further remove the need for spatial subsampling prior to implementation. [ABSTRACT FROM AUTHOR]

Models for accurately predicting species distributions have become essential tools for many ecological and conservation problems. For many species, presence-background (PB) data is the most commonly available type of spatial data. A number of important methods have been proposed to model PB data, and there have been debates on the connection between these seemingly disparate methods. The paper studies the close relationship between the LI (Lancaster and Imbens), LK (Lele and Keim), scaled binomial (SB), expectation-maximization (EM), partial likelihood based Lele method, MAXENT, and the point process models. We reveal that all these methods are the same in their ability to estimate the relative probability (or intensity) of presence from PB data, and the absolute probability of presence, when extra information of the species' prevalence is known. A new unified constrained LK (CLK) method is also proposed as a generalization of the better known existing approaches, with less theory involved and greater ease of implementation. [ABSTRACT FROM AUTHOR]

Point process models have made a significant contribution to the prediction of football association outcomes. It is conventionally the case that defence and attack capabilities have been assumed to be constant during a match and estimated against the average performance of all other teams in history. Drawing upon a Bayesian method, this paper proposes a dynamic strength model which relaxes assumption of the constant teams’ strengths and permits applying in-match performance information to calibrate them. An empirical study demonstrates that although the Bayesian model fails to achieve improvement in goal difference prediction, it registers clear achievements with regard to the prediction of the total number of goals and Win/Draw/Loss outcome prediction. When the Bayesian model bets against the SBOBet bookmaker, one of the most popular gaming companies among Asian handicaps fans, whose odds data were obtained from both the Win/Draw/Loss market and over–under market, it may obtain positive returns; this clearly contrasts with the process model with constant strengths, which fails to win money from the bookmaker.

Aim We test a new species distribution modelling ( SDM) framework, while comparing results to more common distribution modelling techniques. This framework allows for the combination of presence-only ( PO) and presence-absence ( PA) data and accounts for imperfect detection and spatial bias in presence data. The new framework tested here is based on a Poisson point process model, which allows for predictions of population size. We compared these estimates to those provided by experts on the species. Species and Location Presence data on Baird's tapir ( Tapirus bairdii) throughout its range from southern México to northern Colombia were used in this research, primarily from the years 2000 to 2016. Methods Four SDM frameworks are compared as follows: (1) Maxent, (2) a presence-only ( PO) SDM based on a Poisson point process model ( PPM), (3) a presence-absence ( PA) SDM also based on a PPM and (4) an Integrated framework which combines the previous two models. Model averaging was used to produce a single set of coefficient estimates and predictive maps for each model framework. A hotspot analysis (Gi*) was used to identify habitat cores from the predicted intensity of the Integrated model framework. Results Important variables to model the distribution of Baird's tapir included land cover, human pressure and topography. Accounting for spatial bias in the presence data affected which variables were important in the model. Maxent and the Integrated model produced predictive maps with similar patterns and were considered to be more in agreement with expert knowledge compared to the PO and PA models. Main conclusions Total abundance as predicted by the model was higher than expert opinion on the species, but local density estimates from our model were similar to available independent assessments. We suggest that these results warrant further validation and testing through collection of independent test data, development of more precise predictor layers and improvements to the model framework. [ABSTRACT FROM AUTHOR]

Aim: To understand how the integration of contextual spatial data on land cover and human infrastructure can help reduce spatial bias in sampling effort, and improve the utilization of citizen science-based species recording schemes. By comparing four different citizen science projects, we explore how the sampling design's complexity affects the role of these spatial biases. Location: Denmark, Europe. Methods: We used a point process model to estimate the effect of land cover and human infrastructure on the intensity of observations from four different citizen science species recording schemes. We then use these results to predict areas of under- and oversampling as well as relative biodiversity 'hotspots' and 'deserts', accounting for common spatial biases introduced in unstructured sampling designs. Results: We demonstrate that the explanatory power of spatial biases such as infrastructure and human population density increased as the complexity of the sampling schemes decreased. Despite a low absolute sampling effort in agricultural landscapes, these areas still appeared oversampled compared to the observed species richness. Conversely, forests and grassland appeared undersampled despite higher absolute sampling efforts. We also present a novel and effective analytical approach to address spatial biases in unstructured sampling schemes and a new way to address such biases, when more structured sampling is not an option. Main conclusions: We show that citizen science datasets, which rely on untrained amateurs, are more heavily prone to spatial biases from infrastructure and human population density. Objectives and protocols of mass-participating projects should thus be designed with this in mind. Our results suggest that, where contextual data is available, modelling the intensity of individual observation can help understand and quantify how spatial biases affect the observed biological patterns. [ABSTRACT FROM AUTHOR]

Defining the oceanic habitats of migratory marine species is important for both single species and ecosystem-based fisheries management, particularly when the distribution of these habitats vary temporally. This can be achieved using species distribution models that include physical environmental predictors. In the present study, species distribution models that describe the seasonal habitats of two pelagic fish (dolphinfish, Coryphaena hippurus and yellowtail kingfish, Seriola lalandi), are developed using 19 yr of presence-only data from a recreational angler-based catch-and-release fishing programme. A Poisson point process model within a generalized additive modelling framework was used to determine the species distributions off the east coast of Australia as a function of several oceanographic covariates. This modelling framework uses presence-only data to determine the intensity of fish (fish km−2), rather than a probability of fish presence. Sea surface temperature ( SST), sea level anomaly, SST frontal index and eddy kinetic energy were significant environmental predictors for both dolphinfish and kingfish distributions. Models for both species indicate a greater fish intensity off the east Australian coast during summer and autumn in response to the regional oceanography, namely shelf incursions by the East Australian Current. This study provides a framework for using presence-only recreational fisheries data to create species distribution models that can contribute to the future dynamic spatial management of pelagic fisheries. [ABSTRACT FROM AUTHOR]

Point process model keyword spotting (KWS) system has attracted considerable attentions in the areas of keyword spotting by its capacity that can generalize from a relatively small numbers of training examples. But unfortunately, the accuracy level of the point process model is not comparable with the state-of-the-art KWS systems because of the poor modeling capacity of the phoneme detector, which are based on Gaussian Mixture Models. In this paper, focus on improving the performance of detector in point process model, we propose an enhanced version of point process model, which is based on hierarchical deep belief networks (DBNs). Hierarchical DBNs are used as the phoneme detector in this system, and they combine the advantages of both the DBN and the hierarchical architecture for capturing complex statistical patterns in speech while overcoming the inherent flaws of conventional hidden Markov models and multilayer layer perceptron. Experiments results on TIMIT database show that the proposed method can yield 2% improvement. Furthermore, in the case when training examples are extremely limited, it can achieve better results over state-of-the-art KWS systems. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In British Columbia, large-scale salvage harvesting has been underway to recover timber value from forest stands infested by mountain pine beetle during the current outbreak. Understanding the response of beetles to clearcut edges particularly at the landscape scale is crucial to understanding the impacts of increased habitat fragmentation due to salvage harvesting on the spread of the beetle infestations. A novel proximity analysis approach based on null models of complete spatial randomness with three different spatial extents was developed to examine the spatial patterns of infestations in relation to cutblocks. Inhomogeneous Poisson point process models were fitted to predict how intensities of infestations varied with distances to the nearest cutblocks. Marked Poisson point process models were also fitted to evaluate the effects of the variables associated with the nearest cutblocks and adjacent infested pine stands on the edge response of beetles. The results clearly illustrated a significant positive edge response of beetles at the landscape scale. The intensities of infestations decreased non-linearly with distances to the nearest cutblocks. The results also suggested that the quality and distribution of key habitat resources could not fully explain the fundamental mechanisms underlying the edge response. The behavioural change of beetle dispersal at edges may also be an important factor contributing to a positive edge response. The results from this study may be useful in improving the efficacy of mountain pine beetle management efforts. [ABSTRACT FROM AUTHOR]

Aims Recent mechanistic explanations for community assembly focus on the debates surrounding niche-based deterministic and dispersal-based stochastic models. This body of work has emphasized the importance of both habitat filtering and dispersal limitation, and many of these works have utilized the assumption of species spatial independence to simplify the complexity of the spatial modeling in natural communities when given dispersal limitation and/or habitat filtering. One potential drawback of this simplification is that it does not consider species interactions and how they may influence the spatial distribution of species, phylogenetic and functional diversity. Here, we assess the validity of the assumption of species spatial independence using data from a subtropical forest plot in southeastern China. Methods We use the four most commonly employed spatial statistical models—the homogeneous Poisson process representing pure random effect, the heterogeneous Poisson process for the effect of habitat heterogeneity, the homogenous Thomas process for sole dispersal limitation and the heterogeneous Thomas process for joint effect of habitat heterogeneity and dispersal limitation—to investigate the contribution of different mechanisms in shaping the species, phylogenetic and functional structures of communities. Important Findings Our evidence from species, phylogenetic and functional diversity demonstrates that the habitat filtering and/or dispersal-based models perform well and the assumption of species spatial independence is relatively valid at larger scales (50×50 m). Conversely, at local scales (10×10 and 20×20 m), the models often fail to predict the species, phylogenetic and functional diversity, suggesting that the assumption of species spatial independence is invalid and that biotic interactions are increasingly important at these spatial scales. [ABSTRACT FROM AUTHOR]

Spatial pattern has a key role in the interactions between species in plant communities. These interactions influence ecological processes involved in the species dynamics: growth, regeneration and mortality. In this study, we investigated the effect of spatial pattern on productivity in mature mixed forests of sessile oak and Scots pine. We simulated tree locations with point process models and tree growth with spatially explicit individual growth models. The point process models and growth models were fitted with field data from the same stands. We compared species productivity obtained in two types of mixture: a patchy mixture and an intimate mixture. Our results show that the productivity of both species is higher in an intimate mixture than in a patchy mixture. Productivity difference between the two types of mixture was 11.3 % for pine and 14.7 % for oak. Both species were favored in the intimate mixture because, for both, intraspecific competition was more severe than interspecific competition. Our results clearly support favoring intimate mixtures in mature oak–pine stands to optimize tree species productivity; oak is the species that benefits the most from this type of management. Our work also shows that models and simulations can provide interesting results for complex forests with mixtures, results that would be difficult to obtain through experimentation. [ABSTRACT FROM AUTHOR]

Linear receptive field (RF) models of area 3b neurons reveal a three-component structure: a central excitatory region flanked by two inhibitory regions that are spatially and temporally nonoverlapping with the excitation. Previous studies also report that there is an "infield" inhibitory region throughout the neuronal RF, which is a nonlinear interactive (second order) effect whereby stimuli lagging an input to the excitatory region are suppressed. Thus linear models may be inaccurate approximations of the neurons' true RFs. In this study, we characterize the RFs of area 3b neurons, using a second-order quadratic model. Data were collected from 80 neurons of two awake, behaving macaque monkeys while a random dot pattern was scanned simultaneously across the distal pads of digits D2, 3, and 4. We used an iterative method derived from matching pursuit to identify a set of linear and nonlinear terms with significant effects on the neuronal response. For most neurons (65/80), the linear component of the quadratic RF was characterized by a single excitatory region on the dominant digit. Interactions within the dominant digit were characterized by two quadratic filters that capture the spatial aspects of the interactive infield inhibition. Interactions between the dominant (most responsive) digit and its adjacent digit(s) formed the largest class of cross-digit interactions. The results demonstrate that a significant part of area 3b responses is due to nonlinear mechanisms, and furthermore, the data support the notion that area 3b neurons have "nonclassical RF"-like input from adjacent fingers, indicating that area 3b plays a role in integrating shape inputs across digits. [ABSTRACT FROM AUTHOR]

Cochlear implant speech processors stimulate the auditory nerve by delivering amplitude-modulated electrical pulse trains to intracochlear electrodes. Studying how auditory nerve cells encode modulation information is of fundamental importance, therefore, to understanding cochlear implant function and improving speech perception in cochlear implant users. In this paper, we analyze simulated responses of the auditory nerve to amplitude-modulated cochlear implant stimuli using a point process model. First, we quantify the information encoded in the spike trains by testing an ideal observer’s ability to detect amplitude modulation in a two-alternative forced-choice task. We vary the amount of information available to the observer to probe how spike timing and averaged firing rate encode modulation. Second, we construct a neural decoding method that predicts several qualitative trends observed in psychophysical tests of amplitude modulation detection in cochlear implant listeners. We find that modulation information is primarily available in the sequence of spike times. The performance of an ideal observer, however, is inconsistent with observed trends in psychophysical data. Using a neural decoding method that jitters spike times to degrade its temporal resolution and then computes a common measure of phase locking from spike trains of a heterogeneous population of model nerve cells, we predict the correct qualitative dependence of modulation detection thresholds on modulation frequency and stimulus level. The decoder does not predict the observed loss of modulation sensitivity at high carrier pulse rates, but this framework can be applied to future models that better represent auditory nerve responses to high carrier pulse rate stimuli. The supplemental material of this article contains the article’s data in an active, re-usable format. [ABSTRACT FROM AUTHOR]

Question: The drivers of spatial patterning among plant species and the implications of those patterns for the structure and function of plant communities are of ongoing interest and debate. Here we explore the spatial patterning shown by individual species in species-rich plant communities. We (1) compare the levels of aggregation in these communities to those observed in other species-rich communities, in particular tropical rain forests, and (2) consider how abiotic conditions might influence the levels of aggregation observed. Location: We describe the spatial structure of four species-rich Mediterranean-type shrubland communities near Eneabba, Western Australia. The four sites each contain > 10 000 plants and up to 113 species, and differ in substrate-type, species richness and composition. Methods: We analysed the spatial patterning of all species with more than 20 individuals (233 species patterns), and used point process models for aggregated patterns to separate first-order gradient effects from second-order clustering. Results: Aggregated distributions were most common at all sites, but especially at the site with the highest resource availability and heterogeneity and lowest species richness. A Poisson cluster process best described the majority of aggregated species, suggesting that local interactions drive fine-scale patterns in these communities. Conclusions: As with many previous studies, we found that most species showed strong local aggregation. The proportion of species showing aggregation was less than has been described in species-rich tropical rainforests but was higher than observed in many temperate plant communities. The highest proportion of aggregated species was seen at the most resource-abundant site; this is in direct contrast to conceptual models that suggest that competition should be weakest, and aggregation most prevalent, in the most resource-limited sites. [ABSTRACT FROM AUTHOR]