Spatial Subsets Research Articles

Empirical assessment of external enablers on new venture creation: the effect of technologies and non-technological change in Iran digital entrepreneurship

Purpose The purpose of this study is to investigate the influence of external enablers (EEs) – factors that are exogenous and independent of the actors – on the rate of new venture creation (NVC) in digital entrepreneurship. Design/methodology/approach The authors established a unique data panel comprising all formal digital entrepreneurial firms and startups licensed to operate in the digital space from Iran E-Commerce Development Center, from 2012 to 2022, totaling 124,751 businesses classified in 16 sectors. The authors used Poisson regression with robust standard errors to analyze the relationship between external factors and NVC. Additionally, the authors segmented the data set along demographic, spatial and sectoral subsets to compare them using the average treatment effect model. Findings This analysis confirms that technology readiness and technology intensity, as EEs, significantly boost digital entrepreneurship in a developing country. The authors also explored the impact of the COVID-19 pandemic as a natural environmental enabler and explored its facilitating effects and the enduring impacts post-pandemic. Moreover, the authors assessed the heterogeneous impact of the EEs across different groups and derived implications for each. Research limitations/implications While this study is comprehensive, it focuses exclusively on formal digital entrepreneurship within a single developing country, namely, Iran. Although the authors explored various EEs, they did not address all possible types, highlighting the potential for further research in other entrepreneurial sectors for a comparative analysis of EEs. Practical implications The initial findings affirm that most NVC in Iran stems from external factors. These findings have been widely used by policymakers to define the goals and objectives of EEs concerning their nature and impact. This research reveals that EEs have heterogeneous effects on NVC, varying across different characteristics such as gender, geography and industry sectors. The results of this study suggest that governments should reassess the role of “macro” policies to support vulnerable groups. EEs disproportionately benefit male founders, which exacerbates existing gender gaps and necessitates ongoing attention and intervention. Additionally, EEs have diverse impacts across industries, potentially favoring some while disadvantaging others because of varying social, cultural and economic factors. Originality/value NVC, central to entrepreneurship in developing nations, encounters numerous challenges, yet the role of EEs remains underexplored in this context. EEs are opportunities that are not limited to a subset of entrepreneurial actors; they act as levers influencing a wide range of entrepreneurial activities. This research distinctively investigates the empirical impact of EEs in a Middle Eastern developing country context, contributing to the growing literature on the external enablers framework.

Examining the Impact of Assimilating Surface, PBL, and Free Atmosphere Observations from TORUS on Analyses and Forecasts of Two Supercells on 8 June 2019

Abstract This study describes data-denial experiments conducted to examine the impact of assimilating subsets of data from the TORUS project on storm-scale ensemble forecasts of two supercells on 8 June 2019. Assimilated data from TORUS includes mobile mesonet, UAS, and radiosonde observations. The TORUS data are divided into three spatial subsets to evaluate the importance of observing different parts of the atmosphere on forecasts of this case: the SFC subset consisting of just the near-surface mobile mesonet observations, the PBL subset consisting of UAS observations and radiosonde profiles below 762 m, and the FREE subset consisting of radiosonde profiles above 762 m. Data denial experiments are then conducted by comparing analyses and free forecasts generated using a cycled EnKF data assimilation system assimilating conventional observations, radar observations, and all of the TORUS observations at once with experiments where one of the three subsets is removed in turn as well as a control experiment assimilating only conventional and radar observations. Our results show that assimilating all of the TORUS observations at once in the ALL experiment improves the storm-scale ensemble forecasts much more often than it degrades them, and that no one subset of the TORUS data was consistently most important for improving the analyses or forecasts.

Spatial subsetting enables integrative modeling of oral squamous cell carcinoma multiplex imaging data

Oral squamous cell carcinoma (OSCC), a prevalent and aggressive neoplasm, poses a significant challenge due to poor prognosis and limited prognostic biomarkers. Leveraging highly multiplexed imaging mass cytometry, we investigated the tumor immune microenvironment (TIME) in OSCC biopsies, characterizing immune cell distribution and signaling activity at the tumor-invasive front. Our spatial subsetting approach standardized cellular populations by tissue zone, improving feature reproducibility and revealing TIME patterns accompanying loss-of-differentiation. Employing a machine-learning pipeline combining reliable feature selection with multivariable modeling, we achieved accurate histological grade classification (AUC= 0.88). Three model features correlated with clinical outcomes in an independent cohort: granulocyte MAPKAPK2 signaling at the tumor front, stromal CD4+ memory Tcell size, and the distance of fibroblasts from the tumor border. This study establishes a robust modeling framework for distilling complex imaging data, uncovering sentinel characteristics of the OSCC TIME to facilitate prognostic biomarkers discovery for recurrence risk stratification and immunomodulatory therapy development.

Weak lensing tomographic redshift distribution inference for the Hyper Suprime-Cam Subaru Strategic Program three-year shape catalogue

ABSTRACTWe present posterior sample redshift distributions for the Hyper Suprime-Cam Subaru Strategic Program Weak Lensing three-year (HSC Y3) analysis. Using the galaxies’ photometry and spatial cross-correlations, we conduct a combined Bayesian Hierarchical Inference of the sample redshift distributions. The spatial cross-correlations are derived using a subsample of Luminous Red Galaxies (LRGs) with accurate redshift information available up to a photometric redshift of z < 1.2. We derive the photometry-based constraints using a combination of two empirical techniques calibrated on spectroscopic and multiband photometric data that cover a spatial subset of the shear catalogue. The limited spatial coverage induces a cosmic variance error budget that we include in the inference. Our cross-correlation analysis models the photometric redshift error of the LRGs to correct for systematic biases and statistical uncertainties. We demonstrate consistency between the sample redshift distributions derived using the spatial cross-correlations, the photometry, and the posterior of the combined analysis. Based on this assessment, we recommend conservative priors for sample redshift distributions of tomographic bins used in the three-year cosmological Weak Lensing analyses.

Spatiotemporal trends in floral visitation and interaction networks reveal shifting niches for bats in a Neotropical savanna.

Flower-vising bats are important components of tropical pollinator communities, yet little is known about the structure of their pollination networks and how resource availability through time (seasons) and space (habitat heterogeneity) affects the extent to which bats interact with plants within a community-wide context. This information is key for the conservation of threatened nectarivore species, such as the Cerrado-endemic Lonchophylla dekeyseri, for which data on its specialization on floral-resources is scarce. Within a seasonal and heterogeneous savanna in the central Brazilian Cerrado, we performed a year-round assessment of an inclusive assemblage of flower-visiting bats (both nectarivores and other guilds that can also feed on nectar) within a savanna-edge-forest gradient, the phenological trends and spatial distribution of bat and their resource plants, and the resultant temporal and spatial interaction networks between bats and plants in order to associate network structure to resource availability. Clear spatiotemporal trends emerged in the community. Nectarivores dominated the flower-visiting niche outside forests and were prolific floral visitors, resulting in networks with lower specialization and modularity. These bats diverged into savanna foragers active during the wet season and the wet-dry transition, and edge foragers active mostly during the dry season. The latter group encompassed L. dekeyseri, which visited mostly Bauhinia species. Frugivores took over as main floral visitors within forests, as well as during peak dry season, when fewer fruits were available, resulting in more specialized and modular networks. Our work shows that the turnover of floral resources across seasons and vegetation types has a defining role in bat-plant interactions and relates to network structure, as bat trophic guilds interact with plants in distinct habitats and times of the year. Frugivores dominate the flower-visiting niche in certain temporal and spatial subsets of the network, which calls for the inclusion of this guild in future studies. Moreover, the high visitation to Bauhinia species by L. dekeyseri during the dry season might reduce competition with other nectarivores and is relevant to the management of the species, although more data is needed on its resource consumption on a larger time frame and across its geographic range.

A 29-year time series of annual 300 m resolution plant-functional-type maps for climate models

Abstract. The existing medium-resolution land cover time series produced under the European Space Agency's Climate Change Initiative provides 29 years (1992–2020) of annual land cover maps at 300 m resolution, allowing for a detailed study of land change dynamics over the contemporary era. Because models need two-dimensional parameters rather than two-dimensional land cover information, the land cover classes must be converted into model-appropriate plant functional types (PFTs) to apply this time series to Earth system and land surface models. The first-generation cross-walking table that was presented with the land cover product prescribed pixel-level PFT fractional compositions that varied by land cover class but that lacked spatial variability. Here we describe a new ready-to-use data product for climate modelling: spatially explicit annual maps of PFT fractional composition at 300 m resolution for 1992–2020, created by fusing the 300 m medium-resolution land cover product with several existing high-resolution datasets using a globally consistent method. In the resulting data product, which has 14 layers for each of the 29 years, pixel values at 300 m resolution indicate the percentage cover (0 %–100 %) for each of 14 PFTs, with pixel-level PFT composition exhibiting significant intra-class spatial variability at the global scale. We additionally present an updated version of the user tool that allows users to modify the baseline product (e.g. re-mapping, re-projection, PFT conversion, and spatial sub-setting) to meet individual needs. Finally, these new PFT maps have been used in two land surface models – Organising Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) and the Joint UK Land Environment Simulator (JULES) – to demonstrate their benefit over the conventional maps based on a generic cross-walking table. Regional changes in the fractions of trees, short vegetation, and bare-soil cover induce changes in surface properties, such as the albedo, leading to significant changes in surface turbulent fluxes, temperature, and vegetation carbon stocks. The dataset is accessible at https://doi.org/10.5285/26a0f46c95ee4c29b5c650b129aab788 (Harper et al., 2023).

From sunrise to sunset: Exploring landscape preference through global reactions to ephemeral events captured in georeferenced social media.

Events profoundly influence human-environment interactions. Through repetition, some events manifest and amplify collective behavioral traits, which significantly affects landscapes and their use, meaning, and value. However, the majority of research on reaction to events focuses on case studies, based on spatial subsets of data. This makes it difficult to put observations into context and to isolate sources of noise or bias found in data. As a result, inclusion of perceived aesthetic values, for example, in cultural ecosystem services, as a means to protect and develop landscapes, remains problematic. In this work, we focus on human behavior worldwide by exploring global reactions to sunset and sunrise using two datasets collected from Instagram and Flickr. By focusing on the consistency and reproducibility of results across these datasets, our goal is to contribute to the development of more robust methods for identifying landscape preference using geo-social media data, while also exploring motivations for photographing these particular events. Based on a four facet context model, reactions to sunset and sunrise are explored for Where, Who, What, and When. We further compare reactions across different groups, with the aim of quantifying differences in behavior and information spread. Our results suggest that a balanced assessment of landscape preference across different regions and datasets is possible, which strengthens representativity and exploring the How and Why in particular event contexts. The process of analysis is fully documented, allowing transparent replication and adoption to other events or datasets.

Impact of disturbance signatures on tree-ring width and blue intensity chronology structure and climatic signals in Carpathian Norway spruce

Tree radial growth is influenced by climatic and various non-climatic factors, which can complicate the extraction of climate signals from tree rings. We investigated the influence of disturbance on tree-ring width (RW) and latewood blue intensity (BI) chronologies of Norway spruce from the Carpathian Mountains to explore the extent to which disturbance can affect temperature signals in tree rings. Overall, ∼15000 high-elevation Norway spruce tree cores from 34 sites grouped into four regions (Slovakia, Ukraine, North and South Romania) were analyzed. The curve intervention detection (CID) method was applied to detect and correct identified disturbance trends. RW chronology structural comparisons were performed among disturbance-affected and disturbance-corrected chronologies for various spatial (regional / site) scales and sampling subsets. Structural comparisons were also performed for RW and BI chronologies developed from separate groups of series (i.e., disturbed, and undisturbed) for five sites exhibiting clear disturbance trends. Temperature sensitivity was assessed for all chronology variants of both parameters. We found that disturbance trends only affected RW chronologies at the site/subset scale with relatively small series replication and were not detected at the regional scale. Unlike RW, BI chronologies were generally unaffected by disturbance. BI data also contained much stronger growing season temperature signals, which appeared to be both spatially and temporally more coherent. Whereas highly replicated and spatially extensive datasets can help minimize or eliminate disturbance trends in RW chronologies, this potential influence should be considered when interpreting climatic signals in tree rings and reconstructing historical climate in weakly replicated periods. On the other hand, BI is a promising alternative tree ring parameter with stronger and more stable growing season temperature signals, whose seemingly disturbance-free chronology structure does not appear to suffer from this ecological bias, and therefore represents a more suitable parameter for dendroclimatological research.

Deep learning high resolution burned area mapping by transfer learning from Landsat-8 to PlanetScope

High spatial resolution commercial satellite data provide new opportunities for terrestrial monitoring. The recent availability of near-daily 3 m observations provided by the PlanetScope constellation enables mapping of small and spatially fragmented burns that are not detected at coarser spatial resolution. This study demonstrates, for the first time, the potential for automated PlanetScope 3 m burned area mapping. The PlanetScope sensors have no onboard calibration or short-wave infrared bands, and have variable overpass times, making them challenging to use for large area, automated, burned area mapping. To help overcome these issues, a U-Net deep learning algorithm was developed to classify burned areas from two-date Planetscope 3 m image pairs acquired at the same location. The deep learning approach, unlike conventional burned area mapping algorithms, is applied to image spatial subsets and not to single pixels and so incorporates spatial as well as spectral information. Deep learning requires large amounts of training data. Consequently, transfer learning was undertaken using pre-existing Landsat-8 derived burned area reference data to train the U-Net that was then refined with a smaller set of PlanetScope training data. Results across Africa considering 659 PlanetScope radiometrically normalized image pairs sensed one day apart in 2019 are presented. The U-Net was first trained with different numbers of randomly selected 256 × 256 30 m pixel patches extracted from 92 pre-existing Landsat-8 burned area reference data sets defined for 2014 and 2015. The U-Net trained with 300,000 Landsat patches provided about 13% 30 m burn omission and commission errors with respect to 65,000 independent 30 m evaluation patches. The U-Net was then refined by training on 5,000 256 × 256 3 m patches extracted from independently interpreted PlanetScope burned area reference data. Qualitatively, the refined U-Net was able to more precisely delineate 3 m burn boundaries, including the interiors of unburned areas, and better classify “faint” burned areas indicative of low combustion completeness and/or sparse burns. The refined U-Net 3 m classification accuracy was assessed with respect to 20 independently interpreted PlanetScope burned area reference data sets, composed of 339.4 million 3 m pixels, with low 12.29% commission and 12.09% omission errors. The dependency of the U-Net classification accuracy on the burned area proportion within 3 m pixel 256 × 256 patches was also examined, and patches <6.5% burned were less accurately classified. A regression analysis between the proportion of 30 m grid cells classified as burned against the proportion labelled as burned in the 3 m reference maps showed high agreement (r2 = 0.91, slope = 0.93, intercept <0.001), indicating that the commission and omission errors largely compensate at 30 m resolution.

Open Geospatial System for LUCAS In Situ Data Harmonization and Distribution

The use of in situ references in Earth observation monitoring is a fundamental need. LUCAS (Land Use and Coverage Area frame Survey) is an activity that has performed repeated in situ surveys over Europe every three years since 2006. The dataset is unique in many aspects; however it is currently not available through a standardized interface, machine-to-machine. Moreover, the evolution of the surveys limits the performance of change analysis using the dataset. Our objective was to develop an open-source system to fill these gaps. This paper presents a developed system solution for the LUCAS in situ data harmonization and distribution. We have designed a multi-layer client-server system that may be integrated into end-to-end workflows. It provides data through an OGC (Open Geospatial Consortium) compliant interface. Moreover, a geospatial user may integrate the data through a Python API (Application Programming Interface) to ease the use in workflows with spatial, temporal, attribute, and thematic filters. Furthermore, we have implemented a QGIS plugin to retrieve the spatial and temporal subsets of the data interactively. In addition, the Python API includes methods for managing thematic information. The system provides enhanced functionality which is demonstrated in two use cases.

Global Registration of Multiview Unordered Forest Point Clouds Guided by Common Subgraphs

To register multiview, unordered point clouds from forest scenes, we must establish how scans are associated. The proposed method, called RegisMUF, can register arbitrary forest point clouds from aiming scenarios without knowledge of initial position and orientation, without requiring artificial targets, and without recording the order of the scanning sequents. One of the novel contributions of the proposed method is the optimization of a scanning network. We exploit common subgraphs to connect data between spatial subsets and subsequently predict the overlapping areas between adjacent scenarios. In parallel, we propose a rapid coarse strategy and an accurate tree-oriented refinement strategy. Finally, all the scenarios converge to an anchor by combining the minimum loop expansion approach and a parallel merging approach. We experimentally evaluate three challenging data sets: one data set from the FGI benchmark Evo, Finland with five scans, and two other forest data sets from Jiangxi province, China, with 15 and 23 scans. For each data set, the network-building module of RegisMUF produced 100% correct connections to associate all the scans. Together, 108 pairwise cases are exploited to evaluate the matching module in RegisMUF, and the results reveal that the proposed method is superior to or on par with state-of-the-art practices in terms of registration accuracy and successful-registration rate. In end-to-end tests, RegisMUF performs impressively both in terms of registration accuracy and computational cost.

A Topological Data Analysis approach for retrieving Local Climate Zones patterns in satellite data

In the context of geospatial studies, meaningful information may be hidden in the aspects of form and connectivity inscribed in the measurements. Therefore, here is proposed the use of H0 Persistent Homology (PH), a Topological Data Analysis tool to automatically summarize and quantify relevant spatial features in satellite data. With that aim, we extend the algebraic concepts of cubical complexes to the satellite data perspective and describe homology groups portrayal. As a proof by example, we present an inter-site comparison of Enhanced Vegetation Index from MODerate-resolution Imaging Spectroradiometer over fifteen regions worldwide. There, the Local Climate Zone (LCZ) framework is used to examine the outcomes of the PH filtration. Then, the features from every region that were encapsulated by the PH were compared against each other with the aid of the Bottleneck Distance metric. After that, it was performed a dimensionality reduction with a multi-dimensional scaling to build a 2-D geometry of the level of similarity among them. Thereby, the common aspects of the regions became explicit by their coordinates’ proximity in space. Then, with the use of the K-means algorithm, we were able to cluster those areas belonging to the same LCZ class. The results indicate that the proposed methods are robust to missing data in the satellite data and insensitive to a certain level of inhomogeneity in the spatial subsetting of data. Furthermore, the outcomes provide insights on several viable applications for future research.

Drivers of parasite β-diversity among anuran hosts depend on scale, realm and parasite group.

A robust understanding of what drives parasite β-diversity is an essential step towards explaining what limits pathogens' geographical spread. We used a novel global dataset (latitude -39.8 to 61.05 and longitude -117.84 to 151.49) on helminths of anurans to investigate how the relative roles of climate, host composition and spatial distance to parasite β-diversity vary with spatial scale (global, Nearctic and Neotropical), parasite group (nematodes and trematodes) and host taxonomic subset (family). We found that spatial distance is the most important driver of parasite β-diversity at the global scale. Additionally, we showed that the relative effects of climate concerning distance increase at the regional scale when compared with the global scale and that trematodes are generally more responsive to climate than nematodes. Unlike previous studies done at the regional scale, we did not find an effect of host composition on parasite β-diversity. Our study presents a new contribution to parasite macroecological theory, evidencing spatial and taxonomic contingencies of parasite β-diversity patterns, which are related to the zoogeographical realm and host taxonomic subset, respectively. This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.

A Fractal Approach to Urban Boundary Delineation Based on Raster Land Use Maps: A Case of Shanghai, China

Given the diverse socioecological consequences of rapid urban sprawl worldwide, the delineation and monitoring of urban boundaries have been widely used by local governments as a planning instrument for promoting sustainable development. This study demonstrates a fractal method to delineate urban boundaries based on raster land use maps. The basic logic is that the number of built-up land clusters and their size at each dilation step follows a power-law function. It is assumed that two spatial subsets with distinct fractal characteristics would be obtained when the deviation between the dilation curve and a straight line reaches the top point. The top point is regarded to be the optimum threshold for classifying the built-up land patches, because the fractality of built-up land would no longer exist beyond the threshold. After that, all the built-up land patches are buffered with the optimum threshold and the rank-size distribution of new clusters can be re-plotted. Instead of artificial judgement, hierarchical agglomerative clustering is utilized to automatically classify the urban and rural clusters. The approach was applied to the case of Shanghai, the most rapidly urbanizing megacity in China, and the dynamic changes of the urban boundaries from 1994 to 2016 were analyzed. On this basis, urban–rural differences were further explored through several fractal or nonfractal indices. The results show that the proposed fractal approach can accurately distinguish the urban boundary without subjective choice of thresholds. Extraordinarily different fractal dimensions, aggregation and density and similar average compactness were further identified between built-up land in urban and rural areas. The dynamic changes in the urban boundary indicated rapid urban sprawl within Shanghai during the study period. In view of the popularization and global availability of raster land use maps, this paper adds fuels to the cutting-edge topic of distinguishing the morphological criteria to universally describe urban boundaries.

Sensitivity of migratory connectivity estimates to spatial sampling design

BackgroundThe use of statistical methods to quantify the strength of migratory connectivity is commonplace. However, little attention has been given to their sensitivity to spatial sampling designs and scales of inference.MethodsWe examine sources of bias and imprecision in the most widely used methodology, Mantel correlations, under a range of plausible sampling regimes using simulated migratory populations.ResultsAs Mantel correlations depend fundamentally on the spatial scale and configuration of sampling, unbiased inferences about population-scale connectivity can only be made under certain sampling regimes. Within a contiguous population, samples drawn from smaller spatial subsets of the range generate lower connectivity metrics than samples drawn from the range as a whole, even when the underlying migratory ecology of the population is constant across the population. Random sampling of individuals from contiguous subsets of species ranges can therefore underestimate population-scale connectivity. Where multiple discrete sampling sites are used, by contrast, overestimation of connectivity can arise due to samples being biased towards larger between-individual pairwise distances in the seasonal range where sampling occurs (typically breeding). Severity of all biases was greater for populations with lower levels of true connectivity. When plausible sampling regimes were applied to realistic simulated populations, accuracy of connectivity measures was maximised by increasing the number of discrete sampling sites and ensuring an even spread of sites across the full range.ConclusionsThese results suggest strong potential for bias and imprecision when making quantitative inferences about migratory connectivity using Mantel statistics. Researchers wishing to apply these methods should limit inference to the spatial extent of their sampling, maximise their number of sampling sites, and avoid drawing strong conclusions based on small sample sizes.

A multiscale model via single-cell transcriptomics reveals robust patterning mechanisms during early mammalian embryo development.

During early mammalian embryo development, a small number of cells make robust fate decisions at particular spatial locations in a tight time window to form inner cell mass (ICM), and later epiblast (Epi) and primitive endoderm (PE). While recent single-cell transcriptomics data allows scrutinization of heterogeneity of individual cells, consistent spatial and temporal mechanisms the early embryo utilize to robustly form the Epi/PE layers from ICM remain elusive. Here we build a multiscale three-dimensional model for mammalian embryo to recapitulate the observed patterning process from zygote to late blastocyst. By integrating the spatiotemporal information reconstructed from multiple single-cell transcriptomic datasets, the data-informed modeling analysis suggests two major processes critical to the formation of Epi/PE layers: a selective cell-cell adhesion mechanism (via EphA4/EphrinB2) for fate-location coordination and a temporal attenuation mechanism of cell signaling (via Fgf). Spatial imaging data and distinct subsets of single-cell gene expression data are then used to validate the predictions. Together, our study provides a multiscale framework that incorporates single-cell gene expression datasets to analyze gene regulations, cell-cell communications, and physical interactions among cells in complex geometries at single-cell resolution, with direct application to late-stage development of embryogenesis.

Analysis of point cloud data of tunnel cross-section using cubic B-spline curve

In order to overcome the problem of extraction and deformation information analysis of tunnel cross-section by 3D laser scanning technology in tunnel engineering application, a method of extraction and fitting of tunnel cross-section based on cubic B-spline curve was proposed. First, obtain the initial cross-sectional point cloud by using the cross-sectional intercept method based on the spatial point cloud subset;Then, the cross-section was fitted with a cubic B-spline curve, and the missing part of the point cloud was repaired automatically.In the end, specific points are extracted from the fitted cross-section point cloud to realize cross-section deformation analysis.And cross-section analysis of point cloud data of a section of subway tunnel shows that this method can quickly and accurately analyze the deformation information of the tunnel cross-section.

Quantification of heat wave occurrences over the Indian region using long-term (1979–2017) daily gridded (0.5° × 0.5°) temperature data—a combined heat wave index approach

In the changing climate scenario, the changing heat wave frequency and magnitude have a direct impact on the agriculture, society, economic, and public health. Hence, development of easy and effective tools is essential for quantifying the heat wave incidences for better planning and management towards reducing the impacts of heat waves. In the present study, Climate Prediction Centre (CPC) global daily maximum temperature data along with the long-term normal data for the period 1979–2017 were used for quantification of heat wave conditions. Spatial and temporal sub-setting was carried out to restrict our study within the Indian region and March–July period, respectively. Based on heat wave criteria prescribed by the India Meteorological Department, different heat wave parameters, viz. frequency, magnitude, and extent, were estimated. A new approach, i.e. combined heat-wave index (CHI), was proposed to quantify the impact of heat wave in a profound manner. The efficacy of the overall method of assessing the impact of heat wave had increased by using this new approach. It was found that Rajasthan, Punjab Haryana, and Madhya Pradesh experienced high frequency as well as magnitude of heat wave, while it was lower in north-east, peninsular, and parts of northern India during our study period. The trend analysis for the heat wave parameters along with CHI was carried out over the Indian region during the last 39 years. The increasing trends were found over Rajasthan, Haryana, Punjab, East Madhya Pradesh, and Orissa, while places like Uttar Pradesh, Bihar, Chhattisgarh, Telangana, and Andhra Pradesh showed decreasing trend over the last 39 years. Peninsular and north-east India showed almost no trend regarding heat wave. Further, the month with the maximum contribution towards the seasonal heat wave and its deviation was also estimated. It was observed that the month of May was contributing most over parts of north-western and central India, while it was June in Punjab, Haryana, and western Rajasthan. Hence, the present methodology may be adopted for better planning and management of heat waves.

A novel tool to assess the effect of intraspecific spatial niche variation on species distribution shifts under climate change

AbstractAimNiche‐based models often ignore spatial variation in the climatic niche of a species across its occupied range and the related variation in the response to changing climate conditions. This assumption may lead to inaccurate predictions of species distribution shifts under climate change. Models have been developed to address this issue, but most of them depend upon prior knowledge on evolutionary lineages, phenotypic traits or ecological processes underlying local adaptation or adaptive plasticity. As such information is often lacking, these models are not frequently used to predict distribution shifts for many species. This limits our ability to explore general patterns of change across species.InnovationHere, we propose a modelling framework that can be applied across a large sample of species to assess their distribution shifts under future climate while exploring the effect of intraspecific spatial variation in the response to climate conditions. The proposed approach does not require a detailed understanding of the processes underlying such variation. The geographical distribution of a species is split into spatial subsets along the gradient of occupied climate conditions. These subsets are considered as proxies for intraspecific spatial niche variation. Local models are built with each subset and their predictions are assembled across the study area under different dispersal assumptions. Using European butterflies as an example, we show that this approach can be used to explore the uncertainty about predicted distribution shifts arising from intraspecific spatial variation in sensitivity and response to changing climate conditions.Main conclusionsOur modelling approach is not intended to replace advanced modelling methods based on species‐specific knowledge of ecological and evolutionary processes, but it is useful as an exploratory tool to detect species for which detailed information on intraspecific responses to climate conditions is likely to make a difference for prediction of future distribution shifts.

Life-history traits inform population trends when assessing the conservation status of a declining tiger shark population

The assessment of the conservation status of wide ranging species depends on estimates of the magnitude of their population trends. The accuracy of trend estimates will depend on where and how many locations within a species' range are sampled. We ask how the spatial extent of sampling interacts with non-linear patterns in long-term trends to affect estimates of decline in standardised catch of tiger sharks (Galeocerdo cuvier) on the east coast of Australia. We apply a Bayesian trend model that uses prior information on life-history traits to estimate trends where we use data from all regions versus spatial subsets of the data. As more regions were included in the model the trend estimates converged toward an overall decline of 71% over three generations. Trends estimated from data only from northern regions or southern regions underestimated and overestimated the regional decline, respectively. When a subset of regions was modelled, rather than the full data-set, the prior informed by life-history traits performed well, as did a weakly informed prior that allowed for high variation. The rate of decline in tiger sharks is consistent with a listing East Coast Australia tiger sharks as endangered under local legislation. Monitoring programs that aim to estimate population trends should attempt to cover the extremes and mid-points of a population's range. Life-history information can be used to inform priors for population variation and may give more accurate estimates of trends that can be justified in debates about the status of threatened species, particularly when sampling is limited.