A következő címkéjű bejegyzések mutatása: ecological monitoring. Összes bejegyzés megjelenítése
A következő címkéjű bejegyzések mutatása: ecological monitoring. Összes bejegyzés megjelenítése

2014. február 7., péntek

Burton et al. (2014) Environmental Monitoring and Assessment



Abstract
Effective ecological monitoring is imperative in a human-dominated world, as our ability to manage functioning ecosystems will depend on understanding biodiversity responses to anthropogenic impacts. Yet, most monitoring efforts have either been narrowly focused on particular sites, species and stressors — thus inadequately considering the cumulative effects of multiple, interacting impacts at scales of management relevance — or too unfocused to provide specific guidance. We propose a cumulative effects monitoring framework that integrates multi-scaled surveillance of trends in biodiversity and land cover with targeted evaluation of hypothesized drivers of change. The framework is grounded in a flexible conceptual model and uses monitoring to generate and test empirical models that relate the status of diverse taxonomic groups to the nature and extent of human “footprint” and other landscape attributes. An adaptive cycle of standardized sampling, model development, and model evaluation provides a means to learn about the system and guide management. Additional benefits of the framework include standardized data on status and trend for a wide variety of biodiversity elements, spatially explicit models for regional planning and scenario evaluation, and identification of knowledge gaps for complementary research. We describe efforts to implement the framework in Alberta, Canada, through the Alberta Biodiversity Monitoring Institute, and identify key challenges to be addressed.


Keywords
Cumulative effects, Ecological monitoring, Human footprint, Biodiversity conservation, Adaptive monitoring, Impact assessment

2012. november 5., hétfő

Sólymos et al. (2012) Environmetrics


Abstract
Current methods to correct for detection error require multiple visits to the same survey location. Many historical datasets exist that were collected using only a single visit, and logistical/cost considerations prevent many current research programs from collecting multiple visit data. In this paper, we explore what can be done with single visit count data when there is detection error. We show that when appropriate covariates that affect both detection and abundance are available, conditional likelihood can be used to estimate the regression parameters of a binomial–zero-inflated Poisson (ZIP) mixture model and correct for detection error. We use observed counts of Ovenbirds (Seiurus aurocapilla) to illustrate the estimation of the parameters for the binomial–zero-inflated Poisson mixture model using a subset of data from one of the largest and longest ecological time series datasets that only has single visits. Our single visit method has the following characteristics: (i) it does not require the assumptions of a closed population or adjustments caused by movement or migration; (ii) it is cost effective, enabling ecologists to cover a larger geographical region than possible when having to return to sites; and (iii) its resultant estimators appear to be statistically and computationally highly efficient.

Keywords
closed populations, conditional likelihood, ecological monitoring, mixture models, open populations, pseudo-likelihood