Ecosystem resilience depends on functional redundancy (the number of species contributing similarly to an ecosystem function) and response diversity (how functionally similar species respond differently to disturbance). Here, we explore how land-use change impacts these attributes in plant communities, using data from 18 land-use intensity gradients that represent five biomes and > 2800 species. We identify functional groups using multivariate analysis of plant traits which influence ecosystem processes. Functional redundancy is calculated as the species richness within each group, and response diversity as the multivariate within-group dispersion in response trait space, using traits that influence responses to disturbances. Meta-analysis across all datasets showed that land-use intensification significantly reduced both functional redundancy and response diversity, although specific relationships varied considerably among the different land-use gradients. These results indicate that intensified management of ecosystems for resource extraction can increase their vulnerability to future disturbances.
In the conservation literature on land-use change, it is often assumed that land-use intensification drives species loss, driving a loss of functional trait diversity and ecosystem function. Modern research, however, does not support this cascade of loss for all natural systems. In this paper we explore the errors in this assumption and present a conceptual model taking a more mechanistic approach to the speciesfunctional trait association in a context of land-use change. We provide empirical support for our model's predictions demonstrating that the association of species and functional trait diversity follows various trajectories in response to land-use change. The central premise of our model is that land-use change impacts upon processes of community assembly, not species per se. From the model, it is clear that community context (i.e. type of disturbance, species pool size) will affect the response trajectory of the relationship between species and functional trait diversity in communities undergoing land-use change. The maintenance of ecosystem function and of species diversity in the face of increasing land-use change are complementary goals. The use of a more ecologically realistic model of responses of species and functional traits will improve our ability to make wise management decisions to achieve both aims in specific at-risk systems.
The PREDICTS project—Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)—has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make freely available this 2016 release of the database, containing more than 3.2 million records sampled at over 26,000 locations and representing over 47,000 species. We outline how the database can help in answering a range of questions in ecology and conservation biology. To our knowledge, this is the largest and most geographically and taxonomically representative database of spatial comparisons of biodiversity that has been collated to date; it will be useful to researchers and international efforts wishing to model and understand the global status of biodiversity.
We propose
PCAM
, a Probabilistic Cyber-Alert Management framework, that enables chief information security officers to better manage cyber-alerts. Workers in Cyber Security Operation Centers usually work in 8- or 12-hour shifts. Before a shift,
PCAM
analyzes data about all past alerts and true alerts during the shift time-frame to schedule a given set of analysts in accordance with workplace constraints so that the expected number of “uncovered” true alerts (i.e., true alerts not shown to an analyst) is minimized.
PCAM
achieves this by formulating the problem as a bi-level non-linear optimization problem and then shows how to linearize and solve this complex problem. We have tested
PCAM
extensively. Using statistics derived from 44 days of real-world alert data, we are able to minimize the expected number of true alerts that are not manually examined by a team consisting of junior, senior, and principal analysts. We are also able to identify the optimal mix of junior, senior, and principal analysts needed during both day and night shifts given a budget, outperforming some reasonable baselines. We tested
PCAM
’s proposed schedule (from statistics on 44 days) on a further 6 days of data, using an off-the-shelf false alarm classifier to predict which alerts are real and which ones are false. Moreover, we show experimentally that
PCAM
is robust to various kinds of errors in the statistics used.
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