Types Of Environmental Variables Research Articles

Ensemble SDMs reveal the effect of environmental suitability and nature reserves on conserving Chinese pangolins in Guangdong, China

Identifying species distribution patterns and threatened factors is essential for wildlife conservation. For the critically endangered Chinese pangolin, most studies have emphasized negative human interference and ignored the effect of environmental suitability and positive human intervention on altering species distribution patterns. We took Guangdong Province as the research area and obtained census data of the Chinese pangolin. Combined with five types of environmental variables, including climatic, geographical, human, soil and vegetation, we used five algorithms in BIOMOD2 to construct an ensemble species distribution model (SDM) of the Chinese pangolin. The importance and response curves of environmental variables were also obtained. We used the cutoff as a threshold to classify habitat or non-habitat, calculated the habitat coverage of established nature reserves by overlay analysis, and visualized the conservation gaps. The results indicate that the potential distribution range of the Chinese pangolin is wide, fragmented and determined by multiple factors. The average annual temperature (highly correlated with elevation) and the highest temperature in the warmest month were the main influencing factors (contribution rate > 10 %), and the distance from nature reserves and human footprint index were secondary factors (contribution rate > 5 %). The established nature reserve network covers approximately 23 % of the potential habitat of the Chinese pangolin. The proposed Nanling National Park seems incapable of protecting the widely distributed Chinese pangolins. In eastern and northern Guangdong, including Meizhou, Heyuan, Shaoguan and Huizhou and Qingyuan cities, there is still a large area of habitat suitable for the Chinese pangolin that is not well protected. We suggest further expanding the current nature reserve network to absorb suitable habitats of Chinese pangolins from adjacent areas and optimizing the spatial arrangement to improve conservation efficiency.

Deciding where to put them: Sensitivity tests and independent evaluation are critical when using species distribution models to inform conservation translocations

Abstract Conservation translocations are an important tool for combating species declines and population losses. Species distribution models (SDMs) can facilitate the selection of suitable release sites for translocation programs. However, these models can be sensitive to several modelling decisions. In this study, we explore the impacts of three key modelling decisions on Maxent models developed to inform reintroductions of the long‐toed salamander (Ambystoma macrodactylum) in southwestern Alberta. We specifically test the sensitivity of model predictions to (1) the type of environmental variables used to generate models, (2) whether the background points used to calibrate the models reflects the potential bias in the input locality records and (3) the choice of geographic study extent. We use independent presence‐absence data from an extensive field survey to test the accuracy of models based on different decisions. Both model predictions and performance were sensitive to these modelling decisions. Models developed using local study extents were more accurate than those based on range‐wide extents. Both study extent and type of background points impacted model predictions and performance more than the set of environmental variables included in the models for this species. We further demonstrate the impacts of these decisions on the ranking of potential release sites and present a final set of recommendations that accounts for this uncertainty under both current and future climatic conditions. We specifically identify three sites that are expected to be suitable in both present and future time periods as potential release sites for salamander reintroductions in southwestern Alberta. Synthesis and applications: Our study adds to our understanding of how different modelling decisions impact SDMs and the downstream conclusions from these models while simultaneously demonstrating a rigorous approach for the use of SDMs in conservation translocation planning.

Identifying the Risk Regions of Wild Boar (Sus scrofa) Incidents in China.

The objectives of this study were to identify the risk regions of wild boar incidents in China and to draw a risk map. Risk maps can be used to plan the prioritization of preventive measures, increasing management effectiveness from both a short- and a long-term perspective. We used a web crawler (web information access technology) to obtain reports of wild boar incidents from China's largest search engine (Baidu) and obtained 196 valid geographic locations of wild boar incidents from the reports. Subsequently, a system of environmental variables-with climate, topography, landscape, and human disturbance as the main variable types-was constructed, based on human-land-system thinking. Finally, the Maxent model was applied to predict the risk space of wild boar incidents in China by integrating the geographic location information for wild boar incidents with the environmental variables. We observed that the types of environmental variables that contributed to wild boar incidents were in the descending order of climate (40.5%) > human disturbance (25.2%) > landscape (24.4%) > topography (9.8%). Among the 14 environmental variables, annual precipitation, the GDP index, and the mean annual temperature were the main environmental variables. The distance from woodland, distance from cultivated land, and elevation were the secondary environmental variables. The response curves of the environmental variables demonstrated that the highest probability of wild boar incidents occurred when the annual average temperature was 16 °C, the annual precipitation was 800 mm, and the altitudes were 150 m and 1800 m. The probability of wild boar incidents decreased with an increase in the distance from cultivated and forested land, and increased sharply and then levelled off with an increase in the GDP index. Approximately 12.18% of China was identified as being at a high risk of wild boar incidents, mainly on the eastern side of the Huhuanyong Line.

A CNN-LSTM Model for Soil Organic Carbon Content Prediction with Long Time Series of MODIS-Based Phenological Variables

The spatial distribution of soil organic carbon (SOC) serves as critical geographic information for assessing ecosystem services, climate change mitigation, and optimal agriculture management. Digital mapping of SOC is challenging due to the complex relationships between the soil and its environment. Except for the well-known terrain and climate environmental covariates, vegetation that interacts with soils influences SOC significantly over long periods. Although several remote-sensing-based vegetation indices have been widely adopted in digital soil mapping, variables indicating long term vegetation growth have been less used. Vegetation phenology, an indicator of vegetation growth characteristics, can be used as a potential time series environmental covariate for SOC prediction. A CNN-LSTM model was developed for SOC prediction with inputs of static and dynamic environmental variables in Xuancheng City, China. The spatially contextual features in static variables (e.g., topographic variables) were extracted by the convolutional neural network (CNN), while the temporal features in dynamic variables (e.g., vegetation phenology over a long period of time) were extracted by a long short-term memory (LSTM) network. The ten-year phenological variables derived from moderate-resolution imaging spectroradiometer (MODIS) observations were adopted as predictors with historical temporal changes in vegetation in addition to the commonly used static variables. The random forest (RF) model was used as a reference model for comparison. Our results indicate that adding phenological variables can produce a more accurate map, as tested by the five-fold cross-validation, and demonstrate that CNN-LSTM is a potentially effective model for predicting SOC at a regional spatial scale with long-term historical vegetation phenology information as an extra input. We highlight the great potential of hybrid deep learning models, which can simultaneously extract spatial and temporal features from different types of environmental variables, for future applications in digital soil mapping.

The contribution of landscape features, climate and topography in shaping taxonomical and functional diversity of avian communities in a heterogeneous Alpine region

Understanding the effects of landscape composition and configuration, climate, and topography on bird diversity is necessary to identify distribution drivers, potential impacts of land use changes, and future conservation strategies. We surveyed bird communities in a study area located in the Central Alps (Autonomous Province of South Tyrol, northeast Italy), by means of point counts and investigated taxonomic and functional diversity at two spatial scales along gradients of land use/land cover (LULC) intensity and elevation. We also explored how environmental variables influence bird traits and red-list categories. Models combining drivers of different types were highly supported, pointing towards synergetic effects of different types of environmental variables on bird communities. The model containing only LULC compositional variables was the most supported one among the single-group models: LULC composition plays a crucial role in shaping local biodiversity and hence bird communities, even across broad landscape gradients. Particularly relevant were wetlands, open habitats, agricultural mosaics made up of small habitat patches and settlements, ecotonal and structural elements in agricultural settings, and continuous forests. To conserve bird diversity in the Alps, planning and management practices promoting and maintaining small fields, structural elements, and a mosaic of different LULC types should be supported, while preserving continuous forests at the same time. Additionally, pastures, extensively used meadows, and wetlands are key to conservation. These strategies might mitigate the impacts of global change on bird diversity in the Alps and in other European mountain areas.

Exploring the relationship between window operation behavior and thermal and air quality factors: A case study of UK residential buildings

Occupants' window operations can exert a substantial influence on the indoor environment. Although many researchers have studied the relationship between this adaptive behaviour and various types of environmental variables, certain types of pollutants such as indoor particulate matter (PM) and volatile organic compounds (VOC) remain poorly investigated. A field study was conducted in seventeen newly-built low-energy apartments in the UK to collect data on window status and a range of indoor and outdoor environmental variables including temperature, relative humidity, CO2, PM2.5 and TVOCs. The indoor monitoring for each apartment varied between six months and nearly one year across free-running and heating periods, with about half of the apartments having a few months' monitoring during the pandemic lockdown period. A series of logistic regression models were developed for each monitored window of the living room in these apartments, aimed at identifying the key driving factors for window opening and closing behaviour. Results of statistical significance tests suggested that indoor temperature was the most important influencing factor for window opening and closing behaviours. In contrast, none of the studied air quality variables was found to have a large impact on people's operations of windows.

A STUDY ON ENVIRONMENTAL VARIABLES INFLUENCING DRUG ADDICTION

Drug addiction not only affects drug addicts but also affects families. The family members have to face many things like social pressure, mental stress and a huge financial burden. This affects the whole family and commonly members end up unassisted or depressed. In the present study we focused on the environmental variables influencing drug addiction. The paper is descriptive in nature and based on primary data gathered from various de-addiction centers and hospitals of Ajmer city. The study will be able to give glimpses of the various types of environmental variables so anyone can make a positive difference through individual or collective efforts and strengthen the fight against drug addiction.

Suitable habitat prediction of Sichuan snub-nosed monkeys (Rhinopithecus roxellana) and its implications for conservation in Baihe Nature Reserve, Sichuan, China.

As an endemic primate species with one of the highest priorities in wildlife conservation in China, Sichuan snub-nosed monkeys (Rhinopithecus roxellana) have undergone a sharp decline and range reduction in recent centuries. Here, we used maximum entropy modelling (MaxEnt) integrated with four types of environmental variables, including three biological climate variables (Bio17, precipitation of the driest quarter; Bio6, min. temperature of the coldest month; and Bio2, mean diurnal range), three topographic variables (altitude, slope, and aspect), two anthropogenic variables (Human Footprint Index and human disturbance), and three vegetation-related variables (enhanced vegetation index, normalized difference vegetation index, and Wet Index) to identify the spatial distribution of suitable habitats for Sichuan snub-nosed monkeys in Baihe Nature Reserve (BNR), which is located in the Minshan Mountains. The average training AUC of our model performance is 0.929 ± 0.003. The model predicted 9.6km2 of high suitability habitats and 14.1km2 of moderate suitability habitats for Sichuan snub-nosed monkeys, adding up to only 11.7% of the total area of concern for the study in the BNR. The top four variables ranked in the model (altitude, Human Footprint Index, human disturbance, and Bio17) accounted for relative gain contributions of 23.3%, 19.3%, 14.2%, and 13.4%, respectively. The predicted suitable habitats were confined to an altitude range of 1971-3198m, Human Footprint Index of mainly 3-5 values, low human disturbance (mainly livestock), and precipitation of the driest (or coldest) quarter of 9-22mm. Additionally, the suitable habitats were mainly distributed in the core zone (36.1%), buffer zone (26.8%), and experimental zone (29.5%). The remaining habitats (7.6%) were distributed in the 0.5-km buffer zone of the reserve border. The predicted suitable habitats indicated limited suitable habitat space for the Sichuan snub-nosed monkeys, with most of the suitable habitat distributed outside the core zone in the BNR. Our findings highlighted that human activities in all three functional zones could be the most negative factor on suitable habitat distribution of Sichuan snub-nosed monkeys in the BNR.

Testing the Poleotolerance Lichen Response Trait as an Indicator of Anthropic Disturbance in an Urban Environment

Urban environments are densely populated areas buzzing with a wide range of anthropic activities that cause disturbances like air pollution or the heat island effect, threatening both human and environmental health. Mitigating its impacts implies understanding the integrated effects that those disturbances exert on urban environments. Lichen biodiversity is frequently used as an ecological indicator, being able to integrate its effects in a quantifiable way. The poleotolerance response trait classifies lichens according to their tolerance to human disturbance, but it was developed for Italy’s flora and has seldom been applied outside Italy or in urban context studies. The aim of this work was to assess this trait suitability as an indicator of urban anthropic disturbance and test it outside Italy. For that, we sampled lichen diversity in 41 green spaces in Lisbon. Lichens were classified into the respective poleotolerance trait functional groups and their community weighted mean related with three type of environmental variables used as surrogates of urban disturbance. We showed that disturbance-tolerant functional groups could be used as an ecological indicator of the integrated effects of environmental disturbances. Some species were clearly misclassified, so we propose reclassification for those. Natural and seminatural functional groups did not behave as expected. Nevertheless, disturbance-tolerant functional groups have the potential to be used in in other Southern European cities.

Background matching in the brown shrimp Crangon crangon: adaptive camouflage and behavioural-plasticity

A combination of burrowing behaviour and very efficient background matching makes the brown shrimp Crangon crangon almost invisible to potential predators and prey. This raises questions on how shrimp succeed in concealing themselves in the heterogeneous and dynamic estuarine habitats they inhabit and what type of environmental variables and behavioural factors affect their colour change abilities. Using a series of behavioural experiments, we show that the brown shrimp is capable of repeated fast colour adaptations (20% change in dark pigment cover within one hour) and that its background matching ability is mainly influenced by illumination and sediment colour. Novel insights are provided on the occurrence of non-adaptive (possibly stress) responses to background changes after long-time exposure to a constant background colour or during unfavourable conditions for burying. Shrimp showed high levels of intra- and inter-individual variation, demonstrating a complex balance between behavioural-plasticity and environmental adaptation. As such, the study of crustacean colour changes represents a valuable opportunity to investigate colour adaptations in dynamic habitats and can help us to identify the mayor environmental and behavioural factors influencing the evolution of animal background matching.

Improving the use of environmental diversity as a surrogate for species representation.

The continuous p‐median approach to environmental diversity (ED) is a reliable way to identify sites that efficiently represent species. A recently developed maximum dispersion (maxdisp) approach to ED is computationally simpler, does not require the user to reduce environmental space to two dimensions, and performed better than continuous p‐median for datasets of South African animals. We tested whether maxdisp performs as well as continuous p‐median for 12 datasets that included plants and other continents, and whether particular types of environmental variables produced consistently better models of ED. We selected 12 species inventories and atlases to span a broad range of taxa (plants, birds, mammals, reptiles, and amphibians), spatial extents, and resolutions. For each dataset, we used continuous p‐median ED and maxdisp ED in combination with five sets of environmental variables (five combinations of temperature, precipitation, insolation, NDVI, and topographic variables) to select environmentally diverse sites. We used the species accumulation index (SAI) to evaluate the efficiency of ED in representing species for each approach and set of environmental variables. Maxdisp ED represented species better than continuous p‐median ED in five of 12 biodiversity datasets, and about the same for the other seven biodiversity datasets. Efficiency of ED also varied with type of variables used to define environmental space, but no particular combination of variables consistently performed best. We conclude that maxdisp ED performs at least as well as continuous p‐median ED, and has the advantage of faster and simpler computation. Surprisingly, using all 38 environmental variables was not consistently better than using subsets of variables, nor did any subset emerge as consistently best or worst; further work is needed to identify the best variables to define environmental space. Results can help ecologists and conservationists select sites for species representation and assist in conservation planning.

Behavioral Study of Users When Interacting with Active Honeytokens

Active honeytokens are fake digital data objects planted among real data objects and used in an attempt to detect data misuse by insiders. In this article, we are interested in understanding how users (e.g., employees) behave when interacting with honeytokens, specifically addressing the following questions: Can users distinguish genuine data objects from honeytokens? And, how does the user's behavior and tendency to misuse data change when he or she is aware of the use of honeytokens? First, we present an automated and generic method for generating the honeytokens that are used in the subsequent behavioral studies. The results of the first study indicate that it is possible to automatically generate honeytokens that are difficult for users to distinguish from real tokens. The results of the second study unexpectedly show that users did not behave differently when informed in advance that honeytokens were planted in the database and that these honeytokens would be monitored to detect illegitimate behavior. These results can inform security system designers about the type of environmental variables that affect people's data misuse behavior and how to generate honeytokens that evade detection.

Validating a method for transferring social values of ecosystem services between public lands in the Rocky Mountain region

With growing pressures on ecosystem services, social values attributed to them are increasingly important to land management decisions. Social values, defined here as perceived values the public ascribes to ecosystem services, particularly cultural services, are generally not accounted for through economic markets or considered alongside economic and ecological values in ecosystem service assessments. Social-values data can be elicited through public value and preference surveys; however, limitations prevent them from being regularly collected. These limitations led to our three study objectives: (1) demonstrate an approach for applying benefit transfer, a nonmarket-valuation method, to spatially explicit social values; (2) validate the approach; and (3) identify potential improvements. We applied Social Values for Ecosystem Services (SolVES) to survey data for three national forests in Colorado and Wyoming. Social-value maps and models were generated, describing relationships between the maps and various combinations of environmental variables. Models from each forest were used to estimate social-value maps for the other forests via benefit transfer. Model performance was evaluated relative to the locally derived models. Performance varied with the number and type of environmental variables used, as well as differences in the forests׳ physical and social contexts. Enhanced metadata and better social-context matching could improve model transferability.

Spatial nonstationarity and the scale of species–environment relationships in the Mojave Desert, California, USA

Species distribution models (SDM) have become a fertile area of research interest at the confluence of spatial ecology and GIScience and have been used to study a wide range of biogeographical phenomena, including invasive species, vector-borne diseases, and biological diversity. Scale is one of the most important considerations in any spatial analysis study because different spatial patterns emerge at different scales. An issue related to the ‘extent’ concept of scale that has more recently been recognized as important is spatial nonstationarity, which exists when processes or models of processes vary across space. This research examined the scale of species–environment relationships by a relatively new (in SDM) statistical method, geographically weighted regression (GWR). We tested four different types of species and 10 different types of environmental (climate and topography) variables in univariate GWR models to explore how stationarity and explanatory power varied with scale (as a function of GWR bandwidth size). The results suggest that the scale of species–environment relationships varies for both different types of species and different types of environmental variables. The two metrics used here – stationarity index and explained variance – did not show congruity in terms of a ‘characteristic scale.’ Species' relationships with climate and elevation variables became stationary at broader scales, and in some cases the models did not become stationary at the largest bandwidth tested. The complex topographic variables used here operate at finer scales and were often stationary across all scales or became stationary at small bandwidths. In addition to being instrumental for examining the effects of scale on spatial nonstationarity and a model's explanatory ability, GWR can also be used to explore potential geographical factors that result in nonstationarity.

Patterns of biotic community organization and reorganization: A conceptual framework and a case study

Reorganization is an important concept for confronting complex adaptive systems (CAS) theory with ecological reality. However, little work has been done to translate the reorganization idea into a practical conceptual framework. This paper focuses on community-level reorganization, the process of re-forming patterns in relation to the distribution of species density (abundance) and their frequency of occurrence in space (incidence). We assert that changes in species positions in the incidence–abundance (IA) phase plane depict community reorganization in response to environmental changes. This is because species positions in the IA plane represent the most prominent organizational features of a biotic community. We identified four sequential levels of species reorganization in the IA phase plane: reshuffling of species, species appearance, species disappearance and whole-assemblage shift. We propose that the sequential levels represent an increase in reorganization intensity depending on the extent of environmental change. We formulate the Environmental Change and Re-Organization (ECRO) hypothesis specifying that ecosystems respond predictably in terms of community reorganization in the IA phase plane to external drivers. The predictable reorganization follows the four sequential levels of organization in response to intensity of environmental change. We suggest that the response of a species assemblage to external drivers in sequential levels of reorganization is independent of ecosystem properties, type of environmental variables, taxa, and spatio-temporal scales. We tested the predictability of reorganization according to the ECRO hypothesis, using annual plant assemblages of a semiarid shrubland. These assemblages exhibit CAS properties as suggested by Levin: high species diversity, high numbers of individuals, local interactions among individuals in relation to water consumption, and annually selected species subsets for replication. We investigated annual plant assemblage organization and reorganization for 12 years in response to disturbance and resource input, using the IA phase plane. The field study supports our assertion that reorganization of species assemblages in response to environmental change can be represented by species repositioning in the IA phase plane, and that community reorganization follows the four sequential levels of reorganization in response to the intensity of environmental change. Our conceptual framework and experimental studies demonstrate that hypotheses related to core CAS concepts of organization and reorganization can be tested by linking them with community ecology concepts of species patterns in the IA phase plane. We also discuss the relationship between reorganization in the IA phase plane and resilience, regime shift and ecosystem functioning as affected by species response and effects traits.

Ecological linkages between community and genetic diversity in zooplankton among boreal shield lakes

Ecological linkages between species diversity in communities and genetic diversity in populations have potential to influence the assembly of communities in habitats recovering from human disturbance, but few studies have attempted to synthesize relationships between these levels of biological organization, especially for locally adapted species. No such studies have been done in freshwater ecosystems despite the plethora of environmental stressors plaguing aquatic communities around the world. We present the first study to test (1) whether diversity and dissimilarity among communities and populations of a locally adapted species are correlated and (2) whether communities and population haplotypes respond differently to environmental selection and spatial structure of habitats. We used a fragment of mitochondrial DNA (mtDNA) belonging to the gene cytochrome oxidase subunit I (COI) as a neutral tag to discriminate among different population haplotype variants. In boreal lakes with different histories of exposure to anthropogenic acidification, diversity and dissimilarity metrics for crustacean zooplankton communities and locally adapted populations of an abundant and broadly distributed calanoid copepod species, Leptodiaptomus minutus, did not correlate. This discord was likely because zooplankton communities responded more strongly to acidity and acidity-related environmental variables than spatial structure of lakes, whereas the distribution of L. minutus haplotypes was more strongly governed by spatial structure of lakes than environmental selection. Although spatial structure was the dominant driver of haplotype structure among L. minutus lake populations, there were similarities in the types of environmental variables that influenced the distributions of species in communities and haplotypes in populations. How haplotype diversity among populations relates to community diversity depends on the relative influence of spatial structure of habitats and selection at each of these scales of biological organization.

The relationship between land-use, hydromorphology and river biota at different spatial and temporal scales: a synthesis of seven case studies

The aim of this paper is to summarize seven case studies that were analysed as part of research theme the interaction between land-use and climate forming part of EU-funded Euro-limpacs project Evaluating impacts of global change on European freshwater ecosystems. Different aspects of relationships between climate change, land-use at different spatial scales, in-stream hydrology and morphology, and lotic biota were studied. The papers included analyses of: i) effects of historical changes in stream hydromorphology and biota, ii) influences at different spatial scales on physical features (hydrology and morphology), and iii) influences at different spatial scales on lotic biota. A multitude of environmental variables and gradients at different spatial scales affect in-stream physical as well as biological parameters. Disentangling which of these variables extort strongest influence on hydromorphology and lotic biota seems to differ among studies as can be seen from seven papers summarized here. The studies showed a general pattern with gradients from up-stream headwaters to down-stream stream reaches with predictable changes in vegetation and hydromorphological relationships along gradient. This is of course nothing new, but rather it was surprising how many different spatial scales and types of environmental variables were found to be important in structuring in-stream physical and biotic variables in different studies. Further investigations using standardised sets of environmental variables and possibly also standardised biotic sampling methods as well as clear definitions of which spatial scales are investigated are needed to further disentangle importance of land-use at different spatial scales on in-stream hydromorphology and biota. It is also clearly necessary to widen analysis methods from correlative to such methods that investigate mechanistic relationships among variables within a catchment.

Where is the worm? Predictive modelling of the habitat preferences of the tube-building polychaete Lanice conchilega

Grab samples to monitor the distribution of marine macrobenthic species (animals >1 mm, living in the sand) are time consuming and give only point based information. If the habitat preference of a species can be modelled, the spatial distribution can be predicted on a full coverage scale from the environmental variables. The modelling techniques Generalized Linear Models (GLM) and Artificial Neural Networks (ANN) were compared in their ability to predict the occurrence of Lanice conchilega, a common tube-building polychaete along the North-western European coastline. Although several types of environmental variables were in the data set (granulometric, currents, nutrients) only three granulometric variables were used in the final models (median grain-size, % mud and % coarse fraction). ANN slightly outperformed GLM for a number of performance indicators (% correct predictions, specificity and sensitivity), but the GLM were more robust in the crossvalidation procedure.

A model of e-commerce use by internationalizing SMEs

Abstract Globalization and e-commerce pose new challenges and provide new competitive opportunities, especially for small- and medium-sized enterprises (SMEs) seeking to broaden their involvement into new international markets. Yet, SMEs are only beginning to embrace these new opportunities. The authors derive a model describing the use of e-commerce by internationalizing SMEs, developed by integrating findings from case studies of 12 Canadian SMEs exporting to Japan with a broad range of management theories. The cases represent two very different sectors: hospitality/tourism and high tech. The research first describes how the firms use e-commerce internationally, by examining resource commitment, web function and cultural adaptation. It then explains why the firms use e-commerce, by relating two types of environmental variables — market changes and industry norms — and three firm factors — technical capability, cultural capability and firm size — to the firms' adoption of e-commerce. Implications for SME managers are discussed and nine propositions are put forth to guide and focus future research.