e-book The Social and Spatial Ecology of Work: The Case of a Survey Research Organization

Free download. Book file PDF easily for everyone and every device. You can download and read online The Social and Spatial Ecology of Work: The Case of a Survey Research Organization file PDF Book only if you are registered here. And also you can download or read online all Book PDF file that related with The Social and Spatial Ecology of Work: The Case of a Survey Research Organization book. Happy reading The Social and Spatial Ecology of Work: The Case of a Survey Research Organization Bookeveryone. Download file Free Book PDF The Social and Spatial Ecology of Work: The Case of a Survey Research Organization at Complete PDF Library. This Book have some digital formats such us :paperbook, ebook, kindle, epub, fb2 and another formats. Here is The CompletePDF Book Library. It's free to register here to get Book file PDF The Social and Spatial Ecology of Work: The Case of a Survey Research Organization Pocket Guide.

An evaluation of crowdsourced information for assessing the importance of protected areas: Applied Geography Applied Geography. Capability of meteorological drought indices for detecting soil moisture droughts Journal of Hydrology : Regional Studies. Applicability of earth observation for identifying small-scale mining footprints in a wet tropical region: Remote Sensing Remote Sensing. Surface-strip coal mine land rehabilitation planning in South Africa and Australia : Maturity and opportunities for improvement: Resources Policy Resources Policy. Spatial assessment of open cut coal mining progressive rehabilitation to support the monitoring of rehabilitation liabilities Resources Policy.

Ecoregionalization classification of wetlands based on a cluster analysis of environmental data Applied Vegetation Science. Reliability of map accuracy assessments: A reply to Roff et al. CANE, I. From static connectivity modelling to scenario-based planning at local and regional scales Journal for Nature Conservation. Private land manager capacity to conserve threatened communities under climate change Journal of environmental management. A framework for incorporating fine-scale dispersal behaviour into biodiversity conservation planning Landscape and Urban Planning.

Using social data in strategic environmental assessment to conserve biodiversity Land Use Policy. Drought severity-duration-frequency curves: a foundation for risk assessment and planning tool for ecosystem establishment in post-mining landscapes Hydrology and Earth System Sciences. Modeling the impact of future development and public conservation orientation on landscape connectivity for conservation planning Landscape Ecology. Design droughts: A new planning tool for ecosystem rehabilitation Int. A jurisdictional maturity model for risk management, accountability and continual improvement of abandoned mine remediation programs Resources Policy.

Maturity of jurisdictional abandoned mine programs in Australia based on web-accessible information In: Life-of-Mine A tool for simulating and communicating uncertainty when modelling species distributions under future climates Ecology and evolution. Characterizing spatial uncertainty when integrating social data in conservation planning Conservation biology. Object-based classification of semi-arid vegetation to support mine rehabilitation and monitoring Journal of Applied Remote Sensing.

Spoting long-term changes in vegetation over short-term variability International Journal of Mining, Reclamation and Environment. Site-specific climate analysis elucidates revegetation challenges for post-mining landscapes in eastern Australia Biogeosciences. Prevention of negative mining legacies-a mine rehabilitation perspective on legislative changes in Queensland AusIMM Bulletin.

Interactions between landcover pattern and geospatial processing methods: effects on landscape metrics and classification accuracy Ecological Complexity. Ostrom, and O. Connectivity and the governance of multilevel social-ecological systems: the role of social capital. Annual Review of Environment and Resources 34 1 — Brown, K. Integrating conservation and development: a case of institutional misfit. Frontiers in Ecology and the Environment — Cantwell, M.

Landscape graphs: ecological modeling with graph theory to detect configurations common to diverse landscapes. Landscape Ecology 8 4 — Cohen, M. Carstenn, and C. Floristic quality indices for biotic assessment of depressional marsh condition in Florida. Ecological Applications 14 3 — Colding, J. Lundberg, S. Lundberg, and E. Golf courses and wetland fauna. Ecological Applications 19 6 — Costanza, R. Wilson, A. Troy, A. Voinov, S. Liu, and J. Cumming, G. Cumming, and C. Scale mismatches in social-ecological systems: causes, consequences, and solutions.

Olsson, F. Chapin, III, and C. Resilience, experimentation, and scale mismatches in social-ecological landscapes. Landscape Ecology 28 6 — Cushman, S. Effects of habitat loss and fragmentation on amphibians: a review and prospectus. Biological Conservation 2 — Dale, M. From graphs to spatial graphs.

Annual Review of Ecology, Evolution, and Systematics 41 1 — Dekker, D. Krackhardt, and T. Psychometrika — Ekstrom, J. Evaluating functional fit between a set of institutions and an ecosystem. Ecology and Society 14 2 Emmelin, L.

Special order items

Ernstson, H. Barthel, E. Andersson, and S. Scale-crossing brokers and network governance of urban ecosystem services: the case of Stockholm. Ecology and Society 15 4 Fahrig, L. Effects of habitat fragmentation on biodiversity. Annual Review of Ecology, Evolution, and Systematics 34 1 — Folke, C.

Hahn, P. Olsson, and J. Adaptive governance of social-ecological systems. Annual Review of Environment and Resources 30 1 — Pritchard, F. Berkes, J. Colding, U. Svedin, and L. The problem of fit between ecosystems and institutions. Colding, and U. The problem of fit between ecosystems and institutions: ten years later. Ecology and Society 12 1 Fortuna, M. Spatial network structure and amphibian persistence in stochastic environments.

Frost, D. Amphibian species of the world: an online reference. Version 5. Galpern, P. Manseau, and A.

The Social Organization of Work Free PDF - video dailymotion

Patch-based graphs of landscape connectivity: a guide to construction, analysis and application for conservation. Biological Conservation 1 — Guerrero, A. McAllister, J. Corcoran, and K. Scale mismatches, conservation planning, and the value of social network analyses. Conservation Biology 27 1 — Gunnarsson, U.

Haig, S. Mehlman, and L. Avian movements and wetland connectivity in landscape conservation. Conservation Biology 12 4 — Hanneman, R. Introduction to social network methods. Hooghe, L. Unraveling the Central State, but how? Types of multi-level governance. American Political Science Review 97 2 — Ingo, S. Frithiof, B. Rader Olsson. Samverkan i Stockholmsregionen Collaboration in the Stockholm region. In Swedish with English summary. Joly, P. Miaud, A. Lehmann, and O.

Habitat matrix effects on pond occupancy in newts. Conservation Biology 15 1 — Legendre, P. Comparison of the Mantel test and alternative approaches for detecting complex multivariate relationships in the spatial analysis of genetic data. Molecular Ecology Resources 10 5 — Lopez, R. Davis, and M. Ecological relationships between landscape change and plant guilds in depressional wetlands.

Landscape Ecology 17 1 — Testing the floristic quality assessment index as an indicator of wetland condition. Ecological Applications 12 2 — Mantel, N. The detection of disease clustering and a generalized regression approach. Cancer Research 27 2 — Marsh, D.

Survey Research

Metapopulation dynamics and amphibian conservation. Matthews, J. Tessene, S. Wiesbrook, and B. Effect of area and isolation on species richness and indices of floristic quality in Illinois, USA wetlands. Wetlands 25 3 — Olsson, P. Folke, V.

Galaz, T. Hahn, and L. Enhancing the fit through adaptive co-management: creating and maintaining bridging functions for matching scales in the Kristianstads Vattenrike Biosphere Reserve, Sweden. Pascual-Hortal, L. Comparison and development of new graph-based landscape connectivity indices: towards the priorization of habitat patches and corridors for conservation. Landscape Ecology 21 7 — Pelosi, C. Goulard, and G. The spatial scale mismatch between ecological processes and agricultural management: Do difficulties come from underlying theoretical frameworks?

Peterman, W. Rittenhouse, J. Earl, and R. Demographic network and multi-season occupancy modeling of Rana sylvatica reveal spatial and temporal patterns of population connectivity and persistence. Landscape Ecology 28 8 — Pickett, S. Cadenasso, J. Grove, C. Nilson, R. Pouyat, W. Zipperer, and R. Urban ecological systems: linking terrestrial ecological, physical, and socioeconomic components of metropolitan areas.

Annual Review of Ecology, Evolution, and Systematics — Burch, Jr.

Emplaced and embodied mobility in organizations

Dalton, T. Foresman, J. Morgan Grove, and R.

A conceptual framework for the study of human ecosystems in urban areas. Urban Ecosystems — Rathwell, K. Connecting social networks with ecosystem services for watershed governance: a social-ecological network perspective highlights the critical role of bridging organizations. Ecology and Society 17 2 Ray, N. Lehmann, and P.

Modeling spatial distribution of amphibian populations: a GIS approach based on habitat matrix permeability. Reiss, K. Florida Wetland Condition Index for depressional forested wetlands. Ecological Indicators 6 2 — Ribeiro, R. Carretero, N. Sillero, G. Alarcos, M. Ortiz-Santaliestra, M. Lizana, and G. The pond network: Can structural connectivity reflect on amphibian biodiversity patterns?

Landscape Ecology 26 5 — Robins, G. Lewis, and P. Statistical network analysis for analyzing policy networks. Policy Studies Journal 40 3 — Saura, S. Bodin, and M. Journal of Applied Ecology — Schmitt, P. Intra-metropolitan polycentricity in practice-reflections, challenges and conclusions from 12 European metropolitan areas. Nordregio, Stockholm, Sweden. This coincided with the initiation of agricultural intensification and tree felling on the coffee farm. The emergence of this significant exogenous effect, along with the changing character of the density-dependent effect of lagged nest density, provides clues to the mechanism behind a unique phenomenon observed in the plot, that of an increase in nest population despite resource limitation in nest sites.

Our results have implications in coffee agroecological management, as this system provides important biocontrol ecosystem services. Less examined are the many cases of pattern formation in animal populations, most relevant to sessile species. The important issue of cluster scale remains understudied generally, especially at large scales. What would be that critical clustering scale, and would it make a difference in terms of the observed spatial pattern? Might we be forced to conclude that pattern formation would be endogenous at one cluster scale but exogenous at a different scale?

While our interrogations are cast in the context of one species of ant, however, we note that they are relevant to any sessile organism. In this work, we take on these questions using a spatial dataset spanning 9 years in a permanent 45 hectare plot. Our approach first used spatial descriptive statistics to estimate the spatial relationship between nests.

We fitted nest patterns from each year to point process models using two sets of spatial covariates representing hypothesized endogenous and exogenous contexts. Nest distributions in these models were alternately modelled under assumptions of spatial independence a Poisson process or dependence a cluster process between nests. The utility of adding the clustering process to our models was that it parametrized and fitted the pattern formation process within the spatial correlation analysis.

Our expectations were: i if endogenous processes played an important role in spatial pattern, nest distribution would have a significant density-dependent relationship over space and time, and ii if A. We compare the information explained by the fitted trends of the endogenous and exogenous covariates, interpret the fitted clustering parameters and the performance of the cluster process models, and discuss our results in the context of A. Like many areas of the tropics, this region experiences annual wet and dry seasons.

Our data were collected annually in the wet season, between the months of May and July from to Because A. Between each year, trees were removed or added to the dataset when they died or were cut down, or had grown large enough to be included. Both tests provide a population-wide summary of how individual points cluster with respect to other individuals as a function of an analysis scale r.

We used inhomogeneous point process models to fit nest patterns against exogenous and endogenous spatial variables that we hypothesized could have played a role in spatial distribution.

Navigation menu

Our application of this cluster process to ant colony distribution is novel but appropriate, given the cluster-forming behaviour of A. We fitted separate endogenous and exogenous models for each year of the study. Model variables were represented by spatial grids covering the entire plot. The endogenous models included the year-lagged ant nest density nest and its second-order term nest 2 to accommodate nonlinear density-dependent effects.

The exogenous models fit tree density trees , elevation elev , slope and topographical wetness index wet. Owing to the similar hill aspect across most of the plot, sun exposure was considered generally uniform throughout and was not included in the analysis. Densities of trees and nests were calculated with a Gaussian kernel. However, it should be noted that while our decision was based on some experience and corroborated by our PCF results, the issue of optimal analysis scale and its implications in A.

The slope grid was calculated as a function of the neighbouring values of each cell in the digital elevation model. To aid interpretation of the fitted model coefficients, these variable grids were scaled to have an approximately unit standard deviation across the plot and between all years of data, giving the coefficient estimates a uniform scale in terms of their variances across the study.

We first modelled spatial variation in nest intensity by fitting an inhomogeneous Poisson process IPP model to nest points, using the endogenous or exogenous sets of spatial covariates. The IPP assumes no interaction between points, so any variation in intensity was attributed to spatial heterogeneity of the variables.

We compared the relative information explained by each IPP model to a homogeneous Poisson process null, which assumed a uniform mean intensity of nests for the entire plot. The Thomas cluster process was an intuitive choice, because it uses an isotropic Gaussian shape to disperse offspring points around each cluster centre. Tree population remained constant until tree thinning began in Exact nest and tree populations are given in the electronic supplementary material, S1.

The amount of clustering in the graph is quantified as the difference from a theoretical random Poisson distribution defined by the mean point intensity over the entire plot. PCF statistic for annual nest distributions, plotted as the difference from the theoretical average expected value, represented by the dotted line.


Qualitatively, the shape of the curves changed as well. Nests remained clustered within this structure, however, as the existing patterns were still significantly different from random reallocations within each year's tree distribution. Maps of nest locations are provided in the electronic supplementary material, S2. Because the Thomas process was fitted to the IPP trend, the coefficients for both models are the same, but the uncertainty of the ITCP increases to account for clustering.

Coefficients are the same between models but have different confidence intervals. The p -values are reported in the parentheses below each coefficient, with the IPP p- value listed on top. When assuming no nest clustering in the endogenous models IPP , elevation was significant in and with a positive coefficient; slope was significant in , and with a negative coefficient; and wetness was significant in with a negative coefficient. The ITCP exogenous models did not assign significant coefficients to any abiotic variables. After , nest occurrence became more likely with an increasing number of surrounding trees.

This corresponds with the initiation of the tree felling campaign on the farm that decreased the tree population and maximum tree density found on the plot. This could reflect an increasing dependence of nest density on tree availability, although this relationship may be indirect, as the projected nest density remained much lower than the predicting tree density. Predicted density was derived from the intensity value predicted by the models. All spatial covariates were held at the plot mean except for the x -axis variable. The plotted range of each year reflects the actual range for that year.

The shape of these curves support the idea that nest growth and survival are nonlinear and density-dependent, as the predicted nest occurrence is greater than the diagonal line at lower lagged nest densities, but switches over i. This threshold increased from around 30 in the 3 years before tree thinning — , to a significantly greater peak above 40 in the model only predicted density increase, as the nest density levels were not sufficient to cross the projected threshold , and returned to a lower level around 35 in — At very low levels of previous nest density, the predicted nest density also fell below the previous year's density every year, suggesting an Allee effect for very isolated nests.

In the first 5 years, exogenous models added relatively little information to their respective null models, compared with the last 3 years' models, which improved AIC by at least Endogenous models decreased improved in AIC from the null model by much more—approximately for each model. Comparison of AIC values of the exogenous and endogenous inhomogeneous Poisson process models, and a homogeneous Poisson process null model. Datasets are shared within years, so AIC values are comparable by column.

Relatively lower AIC values indicate that more information is explained by that model. In some years, these differences were significant. A MAD value below 0. Low goodness-of-fit p- values for the MAD test indicated that the endogenous models were significantly different from the actual original nest distributions, and thus not appropriate representations of clustering. From plotting simulated L- test results of both models against the actual pattern electronic supplementary material, S3 , it was evident that the endogenous ITCP models over-predicted clustering, whereas exogenous models encompassed actual clustering patterns.

This may have been because the Thomas process in the endogenous models conflicted with the lagged nest density covariates, which already accounted for aspects of the nest clustering process. On the other hand, the Thomas process of the exogenous models was able to account for all aspects of multigenerational nest distribution, and could thus simulate clustering accurately. For this reason, we consider only the clustering parameters of the exogenous ITCP models in our discussion. Our results demonstrate how Poisson and cluster point process approaches, previously limited to vegetation studies in ecology, can provide insights into the spatial distribution and clustering of other sessile organisms such as ants.

We determined a relevant scale of clustering for A. However, both these methods assumed a simplified interpretation of nest dispersal, ignoring multigenerational processes or approximating their patterns as only offspring nests, though actual distributions were a combination of offspring and survivors.

The uniformly and lushly treed environment of this organic shade coffee farm may have originally formed an effectively homogeneous environment for the arboreal A. However, our study reveals the changing nature of the interactions in this system and the emerging significance of environmental context.

Two consistent patterns emerged from our point process modelling: exogenous models indicated that tree density became an increasingly important positive predictor of nest density, whereas endogenous models suggested a changing density-dependent relationship between nests. However, the discrepancy in AIC between the two models indicated that endogenous effects still explained more of the present patterns.

Our models also suggested an Allee effect at very low nest densities, although it is unclear why small, isolated clusters would experience higher mortality.

This result opens a line of questioning that has not yet been considered by the rich body of empirical work done in the system. That study demonstrated through cellular automata modelling that escape from a more dispersal-limited natural enemy presents a plausible hypothetical mechanism for nest increase.

From the point of view of natural enemies, tree reduction would decrease nest density at a local scale and increase distances between nests, potentially hindering movement and counter-balancing a response to increases in plot-scale nest density. This study found empirical evidence that the negative control on nest densities did indeed change in character, whatever its species identity.

The threshold nest density above which negative control became dominant in the subsequent year i. This threshold peaked after the start of the tree thinning programme and returned close to initial levels in later years, a trend that could be indicative of a transition in the controlling regime. However, we did not see evidence of increased nest dispersal that would support the dispersal-limited natural enemy hypothesis in our Thomas cluster models.

The fitted clustering parameters suggested that the dispersal distance of the nests within nest clusters did not change significantly, even in later years when management on the farm changed. Rather, only the intensity of cluster centres increased. The hypothesis was also contradicted by the significant positive relationship between nest density and tree density in later years, as we would have expected more nests with lower tree density, because this would have been a condition for greater internest distances. We attempted to make an informed choice in the scale of our correlations by basing spatial variables on previous experience in the system, a choice that was supported by the PCF results.

However, the assumption that this scale best represents how a colony interacts with conspecifics and the environment remains untested explicitly.