Spatial Output Research Articles

Integrated Full-Range Droplet Actuation for Inkjet-Printed Digital Microfluidic Chip on Flexible Substrates.

Flexible printed electronic technology makes it possible to fabricate low-cost digital microfluidic (DMF) chips. Inkjet printing on flexible substrates is one of the most cost-effective fabrication processes for DMF chips. Based on inkjet printing technology and simplified coating methods of dielectric and hydrophobic layers, we fabricated low-cost flexible DMF chips (FDMFCs) on PET sheet and on matte photo paper. The surface quality, conductivity and spatial output resolution of the silver lines under different number of printings, different line widths and line gaps on the two types of FDMFCs were comprehensively analyzed. The traditional square dispensing electrodes were optimized to reduce the volume error of the droplets generated during the repeated dispensing operations. Droplets can be driven to implement all the operations on various configurations of FDMFCs by electrowetting-on-dielectric, including closed configuration, open configuration, hybrid configuration composed of closed and open regions on a single chip, and open curved configuration, which are defined as full-range droplet actuation. The droplet motion between closed and open regions in two position modes of the top plate was deeply studied. Droplet operation experiments prove that the motion performance of droplets can be comparable to that of chips processed by traditional technology, and the rapid prototyping technology of the FDMFCs can make the performance of droplet operation mainly depend on the conductivity of the electrode layer and the electrode gap and greatly weaken the influence of the substrate surface quality on the FDMFCs.

Habitat connectivity and resource selection in an expanding bobcat (Lynx rufus) population.

Terrestrial carnivores are among the most imperiled species worldwide, yet some species are resilient and are recovering in human-dominated landscapes after decades or centuries of absence. Bobcat (Lynx rufus) populations were extirpated from much of Midwestern US in the mid-1800’s, and are currently expanding and recolonizing their former range. In this study, we investigated multi-scale habitat selection for Ohio’s expanding bobcat population, and examined habitat connectivity in order to evaluate the conduits for dispersal statewide. We used citizen observations collected between 1978 and 2019 and logistic regression to evaluate population-level habitat selection, and GPS telemetry data for 20 individuals collected between 2012 and 2014 and a distribution-weighted exponential Resource Selection Function to evaluate individual-level habitat selection within home ranges. At the population level, bobcats selected for higher amounts of forest and pasture (at a 50 km2 scale) and herbaceous vegetation (at 15–50 50 km2 scales), thus overall heterogeneous forested habitat. At individual (home range) level, bobcats selected for forested habitats with low road density and farther away from high traffic roads; they also showed weak selection for open habitat at the home range level. Male home ranges were significantly greater than female home ranges. Lastly, we used the population-level spatial outputs (i.e. habitat suitability map) to parameterize habitat connectivity models using circuit theory in the program Circuitscape. We tested three relationships between habitat suitability and resistance to movement and used a subset of data on potential dispersing individuals to evaluate which relationship performed best. All three relationships performed almost equally well, and we calculated a weighted averaged connectivity map as our final map. Habitat was highly permeable to movements between core areas of two genetically distinct subpopulations located in southeastern Ohio. We also identified potential dispersal corridors from the core areas to other regions of Ohio dominated by agriculture and suburban development via forested riparian corridors. Overall, our analysis offers new information on habitat selection and connectivity in a rebounding felid population and offers important ecological information for wildlife management strategies. We recommend that the suitability and connectivity models should be periodically updated until the population reaches an equilibrium, and be integrated with data from neighboring states for a comprehensive assessment of a conservation success story.

Landscape-scale drivers of endangered Cape Sable Seaside Sparrow (Ammospiza maritima mirabilis) presence using an ensemble modeling approach

The Florida Everglades is a vast and iconic wetland ecosystem in the southern United States that has undergone dramatic changes from habitat degradation, development encroachment, and water impoundment. Starting in the past few decades, large restoration projects have been undertaken to restore the landscape, including improving conditions for threatened and imperiled taxa. One focus of restoration has been the marl prairie ecosystem, where the federally endangered Cape Sable Seaside Sparrow (Ammospiza maritima mirabilis; CSSS) resides. The CSSS is endemic to the Everglades where populations have been steadily declining, signaling the importance of decision support tools for natural resource managers for evaluating water management and restoration scenarios. Here we developed an ensemble logistic regression, combining a frequentist and Bayesian approach, to model CSSS presence and measure how environmental factors such as hydrometrics, fire occurrence, and vegetation structure impact CSSS habitat suitability. This is the first analysis to quantitatively assess the interdependent relationships between a broad range of environmental factors and CSSS presence across the landscape. Our results show that the probability of CSSS presence was highest in areas with dry conditions, hydroperiods between 80 and 120 days, percentages of canopy cover and woody vegetation less than 10%, and more than six years post-fire where 75% or more of the area was burned. Because the frequentist and Bayesian models had nearly identical spatial outputs with the Bayesian model having slightly higher validation metrics, we used the Bayesian approach as our final model (EverSparrow). The results from our analysis can provide a valuable decision support tool as natural resource managers work to restore the Everglades landscape.

High spectral purity GaSb-based blazed grating external cavity laser with tunable single-mode operation around 1940nm.

In this article, we present a tunable GaSb-based blazed grating external cavity laser (BG-ECL) with high spectral purity and high output power single-mode operation around 1940nm. The drastic increase in spectral selectivity and optical power results from the employment of a single-transverse-mode operating narrow ridge waveguide laser diode with an optimized AR coating on the front facet. The stable fundamental spatial mode output beam from the laser diode enables efficient collimation and high coupling efficiency with the blazed grating, leading to stronger wavelength-selective feedback. The AR coating with proper low reflectivity on the straight waveguide effectively suppresses the internal cavity mode lasing without causing extra optical loss. As a result, the BG-ECL device exhibits excellent comprehensive performance with a side mode suppression ratio (SMSR) over 50 dB with optical power exceeding 30 mW within a 70 nm tuning range. A maximum SMSR of 56.26 dB with 35.12 mW output power was observed in continuous-wave operation. By increasing the working temperature of the diode laser, the tuning range can be further extended to over 100 nm without noticeable degradation in spectral and output power performance.

Switchable transverse mode operation of a fiber laser with an external feedback cavity

In this paper, we demonstrate a switchable transverse mode operation fiber laser. Lasing in a specific transverse mode is enabled by employing a Fabry–Pérot (F-P) cavity forming an external feedback cavity architecture as an effective transverse mode selector. High spatial quality tunable output modes, including vector vortex beam, orbital angular momentum (OAM) vortex beam and flat-topped beam, could be achieved by optimizing the cavity length and appropriate polarization control. The purity of the vector vortex beam is more than 95%. The topological charge of the OAM mode can be tuned from −1 to +1. The purity of the ±1 OAM vortex beams are all more than 95%. The normalized root mean square of the flat-topped beam is 0.035 and the steep degree is 0.815. Furthermore, this approach has great scalability and can be applied to other modes and spectral regimes.

GPR clutter reduction by multi-resolution based tensor RPCA

ABSTRACT A new clutter reduction method which utilizes the multi-resolution and multi-directional information of the ground-penetrating radar (GPR) image is proposed. Sub-images obtained by stationary wavelet transform (SWT) or nonsubsampled counterlet transform (NSCT) are cast into a tensor structure presenting higher information compared to the spatial input data. A tensor-robust principal component analysis (TRPCA) algorithm is used for low-rank and sparse decomposition (LRSD) followed by inverse transform of the sparse tensor component to provide the clutter reduction results. The proposed methods TRPCA-SWT and TRPCA-NSCT are compared both visually and quantitatively to robust principal component analysis (RPCA) and TRPCA-bandpass filter (TRPCA-BPF), which employ the spatial raw GPR data and outputs of simple low-pass and high-pass filters respectively. Visual and quantitative results demonstrate that the clutter reduction performance increases when a higher number of scales and directions are used prior to the LRSD decomposition. Moreover, one of the proposed methods, TRPCA-NSCT, removes the background noise more efficiently due to its higher multi-resolution and multi-direction investigation capability, increasing the performance of the target detection algorithms.

Groundwater quality for drinking purpose in Pennagaram block, Dharmapuri district, Tamilnadu, India

Groundwater is an essential source of water due to its substantial use of domestic, irrigation and industrial purpose. The present study was pointed to examine the groundwater quality of groundwater in pre-monsoon and post-monsoon seasons on the year of 2013 at Pennagaram block of Dharmapuri district, Tamilnadu. A few groundwater samples of the study area were analyzed for their physico-chemical parameters (APHA, 1998). The spatial distribution map provides a clear explanation for hydrochemical characteristics of groundwater quality for domestic and irrigational purpose in both the seasons. The results of showed the most of the groundwater samples in the study area showed that the pH is slightly acidic to alkaline in nature. The TDS and EC content of the samples in PRM and POM were measured and found 80% of the samples within the WHO limit (1500 mg/L). The water constituents are highly adjustable with respect to cationic and anionic constituents. However, to summarize the average of cations is Ca2+ ˃ Na+ ˃ Mg2+ ˃ K+ and anions are Cl-> HCO3 > SO4 > NO3 > F. The spatial analysis output indicates that in some stations there is need for some degree of treatment for groundwater in the study area before consumption.

The Role of Visual and Auditory Communication in the Performance of a Joint Team Task.

This study examines visual, auditory, and the combination of both (bimodal) coupling modes in the performance of a two-person perceptual-motor task, in which one person provides the perceptual inputs and the other the motor inputs. Parking a plane or landing a helicopter on a mountain top requires one person to provide motor inputs while another person provides perceptual inputs. Perceptual inputs are communicated either visually, auditorily, or through both cues. One participant drove a remote-controlled car around an obstacle and through a target, while another participant provided auditory, visual, or bimodal cues for steering and acceleration. Difficulty was manipulated using target size. Performance (trial time, path variability), cue rate, and spatial ability were measured. Visual coupling outperformed auditory coupling. Bimodal performance was best in the most difficult task condition but also high in the easiest condition. Cue rate predicted performance in all coupling modes. Drivers with lower spatial ability required a faster auditory cue rate, whereas drivers with higher ability performed best with a lower rate. Visual cues result in better performance when only one coupling mode is available. As predicted by multiple resource theory, when both cues are available, performance depends more on auditory cueing. In particular, drivers must be able to transform auditory cues into spatial actions. Spotters should be trained to provide an appropriate cue rate to match the spatial ability of the driver or pilot. Auditory cues can enhance visual communication when the interpersonal task is visual with spatial outputs.

Technical note: Hydrology modelling R packages – a unified analysis of models and practicalities from a user perspective

Abstract. Following the rise of R as a scientific programming language, the increasing requirement for more transferable research and the growth of data availability in hydrology, R packages containing hydrological models are becoming more and more available as an open-source resource to hydrologists. Corresponding to the core of the hydrological studies workflow, their value is increasingly meaningful regarding the reliability of methods and results. Despite package and model distinctiveness, no study has ever provided a comparison of R packages for conceptual rainfall–runoff modelling from a user perspective by contrasting their philosophy, model characteristics and ease of use. We have selected eight packages based on our ability to consistently run their models on simple hydrology modelling examples. We have uniformly analysed the exact structure of seven of the hydrological models integrated into these R packages in terms of conceptual storages and fluxes, spatial discretisation, data requirements and output provided. The analysis showed that very different modelling choices are associated with these packages, which emphasises various hydrological concepts. These specificities are not always sufficiently well explained by the package documentation. Therefore a synthesis of the package functionalities was performed from a user perspective. This synthesis helps to inform the selection of which packages could/should be used depending on the problem at hand. In this regard, the technical features, documentation, R implementations and computational times were investigated. Moreover, by providing a framework for package comparison, this study is a step forward towards supporting more transferable and reusable methods and results for hydrological modelling in R.

Abstract 3172: 1 Unsupervised cellular phenotypic hierarchy enables spatial intratumor heterogeneity characterization, recurrence-associated microdomains discovery, and harnesses network biology from hyperplexed in-situ fluorescence images of colorectal carcinoma

Abstract Background: Tumor cells and their stromal cell counterparts that comprise the tumor microenvironment (TME) reciprocally coevolve to generate heterocellular communication networks. A distinctive characteristic of the functional organization of this continuously evolving ecosystem is spatial intratumoral heterogeneity (ITH), a key determinant of disease progression landmarks in multiple carcinomas that include colorectal cancer. To optimize diagnosis, prognosis, therapeutic strategies and to identify novel therapeutic targets it is important to define spatial ITH in the tumors of individual patients and determine the mechanistic underpinnings of its relationship to metastatic potential, immune evasion, recurrence, therapeutic response and drug resistance. Methods: The first step in investigating spatial ITH is to identify the cell phenotypes within the TME. This, however, is a challenging task owing not only to the diversity of well-defined cell types within the TME but also the intrinsic plasticity of many of these cell types in response to the selection pressures within their particular confines (i.e., microdomains). There has been a recent explosion of hyperplexed (> 9 fluorescence or mass spec-based) biomarker labeling and imaging modalities utilizing various reagent technologies to probe the same tissue sections with several dozens of biomarkers at cellular and subcellular resolutions. The challenge now is to accurately characterize the complex spatial and high-dimensional output of these hyperplexed techniques. Results: We propose LEAPH an unsupervised machine learning algorithm for characterizing in situ phenotypic heterogeneity in tissue samples. LEAPH builds a phenotypic hierarchy of cell types, cell states and their spatial configurations. The recursive modeling steps involve determining cell types with low-ranked mixtures of factor analyzers and optimizing cell states with spatial regularization. We applied LEAPH to hyperplexed (51 biomarkers) immunofluorescence images of colorectal carcinoma primary tumors (N=213). LEAPH, combined with pointwise mutual information (PMI), enables the discovery of phenotypically distinct microdomains, composed of spatially configured computational phenotypes. Harnessing network biology. LEAPH identified a subset of microdomains composed of cancer stem cells driving a Wnt signaling- based immunosuppressive program in patients who exhibited recurrence within 3 years of surgical resection. The LEAPH framework, when combined with microdomain discovery and microdomain-specific network biology, has the potential to provide insights into pathophysiological mechanisms, identify novel drug targets and inform therapeutic strategies for individual patients. Citation Format: Samantha Furman, Andrew Stern, Shikhar Uttam, Taylor D. Lansing, Pullara Filippo, S. CHAKRA Chennubhotla. 1 Unsupervised cellular phenotypic hierarchy enables spatial intratumor heterogeneity characterization, recurrence-associated microdomains discovery, and harnesses network biology from hyperplexed in-situ fluorescence images of colorectal carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3172.

The influence of model frameworks in spatial planning of regional climate-adaptive connectivity for conservation planning

Landscape connectivity improves species’ capacity to adapt to climate change. These models are increasingly needed and available for climate-change conservation planning. However, their relative strengths and weaknesses are unclear. We asked how well do the spatial outputs from four connectivity models intended to support climate change conservation agree? To understand the implications of selecting one or several approaches, we compared various combinations of four connectivity models for ecoregions in California, U.S.A. Two models are based on landscape structure, Land Facet Corridors and Omniscape, while two other models, Meta-Corridor Approach and Network Flow Analysis (NFA), use focal species’ range dynamics to determine connectivity. We also describe how each approach integrates climate-adaptive connectivity concepts. Variation in modeling methods, objectives, inputs, and landscape representations strongly affects the modeled connectivity patterns. For the region where all four models were run, almost three quarters of the landscape was selected by one or more models, but three or more agree for only 9.5% of the area, all of which is riparian. This emphasizes the importance of riparian areas for climate adaptation. We found NFA prioritized connections close to protected areas, while Meta-Corridor avoided higher cost agricultural and developed areas. The structural models agreed in areas with low human impact but Omniscape avoided areas of low topographic diversity and Land Facet Corridors avoided connections in areas with no protected areas.Connectivity models should be selected based on the conservation objectives, such as spatial scale to be implemented, and a combination of models may be best.

China's CO2 emission intensity and its drivers: An evolutionary Geo-Tree approach

Current studies have focused on China's CO2 emission intensity (CEI); however, its geospatial temporal effect is still not clear. Here, we constructed an evolutionary Geo-tree model in combination with spatial econometrics to examine geospatial temporal processes affecting CEI. The Geo-tree provides a schema to geovisualize the effects of CEI's drivers from 1990 to 2016. Spatial econometric outputs indicate that affluence or economic development as well as low-carbon technology has significant inhibitory effects on CEI. Furthermore, the direct inhibitory effect of affluence strengthened over time. In contrast, energy consumption structure and innovation represented by patent raise CEI. While the positive effect decreased over time for energy consumption as coal consumption fell, it remained relatively stable for patent level. Urbanization level as well as industrial development are also positive but not significant in 2006–2016. When the spatial model analysis is combined with the evolutionary Geo-tree, the geovisualization model depicts a taller green tree over time with denser foliage at the crown. As the spatial econometrics results suggest, such evolution towards a lower carbon economy among a number of provinces is likely explained by affluence and, to a lesser extent, the adoption of low-carbon technology. Geo-tree also shows that many provinces are relatively urbanized by 2016 which explains why this factor has no effect in the second period.

Managing Agricultural Water Considering Water Allocation Priority Based on Remote Sensing Data

To fairly distribute limited irrigation water resources in arid regions, a water allocation priority evaluation method based on remote sensing data was proposed and integrated with an optimization model. First, the water supply response unit was divided according to canal system conditions. Then, a spatialization method was used for generating spatial agricultural output value (income from planting industry) and grain yield (yield of food crops) with the help of NDVI and the potential yield of farmland. Third, the AHP-TOPSIS method was employed to calculate the water allocation priority based on the above information. Finally, the evaluation results were integrated with a nonlinear multiobjective model to optimally allocate agricultural land and water resources, considering the combined objective of minimum envy and proportional fairness. The method was applied to Hetao irrigation area, an arid agriculture-dominant region in Northwest China. After solving the model, optimization alternatives were obtained, which indicate that: (1) the spatial method of agricultural output value can improve the accuracy by around 16% compared with the traditional method, and the spatial method of grain yield also have good accuracy (MAPE = 14.66%); (2) the rank of water allocation priority can reflect more spatial information, and provide practical decision support for the distribution of water resources; (3) the envy index can better improve the efficiency of an allocation system compared to the Gini coefficient method.

Cluster-based GSA: Global sensitivity analysis of models with temporal or spatial outputs using clustering

A new method named cluster-based GSA is proposed to enhance the sensitivity analysis of models with temporal or spatial outputs. It is based on a tight coupling between Global Sensitivity Analysis (GSA) and clustering procedures. Clustering is introduced to characterize the different behaviors of the model outputs by grouping them into clusters. The cluster-based GSA produces variance-based indices that quantify how the model inputs drive the model outputs toward a given cluster or how they influence variation along a direction defined by two clusters. Aggregated indices are proposed to summarize the overall influence of model inputs on changes of clusters. The method is applied on two models having temporal outputs: a toy example and an environmental model simulating the decomposition of soil organic matter (CANTIS). In both cases, the influence of the model inputs on the different behaviors of model outputs was efficiently reported by this approach.

Evaluation of a popular spatial cumulative impact assessment method for marine systems: A seagrass case study.

Human activities put stress on our oceans and with a growing global population, the impact is increasing. Stressors rarely act in isolation, with the majority of marine areas being impacted by multiple, concurrent stressors. Marine spatial cumulative impact assessments attempt to estimate the collective impact of multiple stressors on marine environments. However, this is difficult given how stressors interact with one another, and the variable response of ecosystems. As a result, assumptions and generalisations are required when attempting to model cumulative impacts. One fundamental assumption of the most commonly applied, semi-quantitative cumulative impact assessment method is that a change in modelled cumulative impact is correlated with a change in ecosystem condition. However, this assumption has rarely been validated with empirical data. We tested this assumption using a case study of seagrass in a large, inverse estuary in South Australia (Spencer Gulf). We compared three different seagrass condition indices, based on survey data collected in the field, to scores from a spatial cumulative impact model for the study area. One condition index showed no relationship with cumulative impact, whilst the other two indices had very small, negative relationships with cumulative impact. These results suggest that one of the most commonly used methods for assessing cumulative impacts on marine systems is not robust enough to accurately reflect the effect of multiple stressors on seagrasses; possibly due to the number and generality of assumptions involved in the approach. Future methods should acknowledge the complex relationships between stressors, and the impact these relationships can have on ecosystems. This outcome highlights the need for greater evaluation of cumulative impact assessment outputs and the need for data-driven approaches. Our results are a caution for marine scientists and resource managers who may rely on spatial cumulative impact assessment outputs for informing policy and decision-making.

Otopark etütlerinin farklı istatiksel yöntemler ile analizi ve CBS ile modellenmesi: Erzincan ili örneği

Rapidly changing lifestyles, rising expectations, and increased private vehicle ownership have caused increases in the number of urban trips. All these changes that have appeared in time have made the parking requirement more important every day. Parking surveys have been required to determine what actions to be taken leadingly during the location selection and building processes of the parking areas to be built. The failure on planning parking areas correctly reveals environmental and economic problems as well as social problems. In this study, parking surveys were carried out primarily in order to determine location, types and capacities of existing car parks. The capacity and locations of the new parking areas have been determined considering five-year traffic increases. The points where parking needs were most needed were determined counting the vehicles parked at 44 separate on-road parking areas in the city center of Erzincan, where parking fees are charged by the municipality. In determined parking areas, statistical analyses were performed with spatial analysis outputs, artificial neural networks and time series analysis methods counting the vehicles parked regularly during daylight hours for one year. Solutions were offered to reduce parking load in traffic identifying new correctly located car parking areas in Erzincan city center. In the light of the analysis results, the location and capacities of the new parking areas suggested to be built identifying 6 different points in the city center were determined. In conclusion, an additional 1420-vehicle parking capacity in suggested new parking spaces was proposed as a solution-oriented approach for the parking problem in the city center.

Functional principal component analysis for global sensitivity analysis of model with spatial output

Motivated by risk assessment of coastal flooding, we consider time-consuming simulators with a spatial output. The aim is to perform sensitivity analysis (SA), quantifying the influence of input parameters on the output. There are three main issues. First, due to computational time, standard SA techniques cannot be directly applied on the simulator. Second, the output is infinite dimensional, or at least high dimensional if the output is discretized. Third, the spatial output is non-stationary and exhibits strong local variations.We show that all these issues can be addressed all together by using functional PCA (FPCA). We first specify a functional basis, such as wavelets or B-splines, designed to handle local variations. Secondly, we select the most influential basis terms, either with an energy criterion after basis orthonormalization, or directly on the original basis with a penalized regression approach. Then FPCA further reduces dimension by doing PCA on the most influential basis coefficients, with an ad-hoc metric. Finally, fast-to-evaluate metamodels are built on the few selected principal components. They provide a proxy on which SA can be done. As a by-product, we obtain analytical formulas for variance-based sensitivity indices, generalizing known formula assuming orthonormality of basis functions.

Using ECHO as a decision support tool to manage coastal systems subject to physical changes

Physical changes in coastal systems, caused by human or natural processes, lead to changes in flow patterns and salinity, temperature and sediment distributions. These changes can affect the health of an ecosystem. This paper demonstrates the use of the model ECHO (coastal ECosystem Health assessment model using Oysters as the indicator species) for the assessment of the impacts on spatial distribution of the oyster zones. ECHO uses outputs of a 3-D hydrodynamic model to delineate zones hospitable to oysters; its outputs are much more concise than the extensive spatial and temporal outputs of the hydrodynamic models typically employed to simulate physical changes in a coastal environment. This study used ECHO to investigate the impacts of two hypothetical physical changes which altered hydrodynamics in a bay; the impacts were determined by examining the changes in the spatial extents of the delineated oyster zones. For the test cases, a region of a decreased water depth results in improved conditions for oysters, whereas adding an increased amount of freshwater inflow to the bay decreases the prevalence of oyster zones throughout the bay. ECHO reveals that the predominant reason for changes in the spatial extents of oyster zones in the latter case is prolonged stratification. By examining the spatial and temporal behavior of shear and density effects, we conclude that the Richardson number is a better means of quantifying stratification in a complicated tidal system than density gradients alone.

Constraints of using historical data for modelling the spatial distribution of helminth parasites in ruminants.

Dicrocoelium dendriticum is a trematode that infects ruminant livestock and requires two different intermediate hosts to complete its lifecycle. Modelling the spatial distribution of this parasite can help to improve its management in higher risk regions. The aim of this research was to assess the constraints of using historical data sets when modelling the spatial distribution of helminth parasites in ruminants. A parasitological data set provided by CREMOPAR (Napoli, Italy) and covering most of Italy was used in this paper. A baseline model (Random Forest, VECMAP®) using the entire data set was first used to determine the minimal number of data points needed to build a stable model. Then, annual distribution models were computed and compared with the baseline model. The best prediction rate and statistical output were obtained for 2012 and the worst for 2016, even though the sample size of the former was significantly smaller than the latter. We discuss how this may be explained by the fact that in 2012, the samples were more evenly geographically distributed, whilst in 2016 most of the data were strongly clustered. It is concluded that the spatial distribution of the input data appears to be more important than the actual sample size when computing species distribution models. This is often a major issue when using historical data to develop spatial models. Such data sets often include sampling biases and large geographical gaps. If this bias is not corrected, the spatial distribution model outputs may display the sampling effort rather than the real species distribution.

Implementation of a single-photon fully quantum router with cavity QED and linear optics

Both cavity quantum electrodynamics and linear optics are promising platforms for the implementation of coherent manipulations of quantum information, and have potential applications in the development of practical quantum devices. In this paper, we propose a simple and deterministic scheme for implementing a single-photon fully quantum router by a combination of both platforms. With this router, a polarization encoded signal photonic qubit can be coherently routed to one of two distinct spatial output ports or their coherent superposition according to the state of a control photonic qubit, while keeping the signal information stored in the signal qubit unaltered under the quantum routing operation. Our scheme has several advantages in contrast to previously published ones, and on top of that, this device meets all the requirements imposed on a fully featured quantum router and is easily scalable for multiqubit quantum routing. Moreover, it exhibits high experimental feasibility with current or near-future technologies. Therefore, this router can be integrated with protocols for the realization of quantum information processing and future complex quantum networks.