Spatial population dynamics of small mammals: some methodological and practical issues

Small mammals have been widely used to further our understanding of spatial and temporal population dynamical patterns, because their dynamics exhibit large variations, both in time (multi-annual cycles vs. seasonal variation only) and space (regional synchrony, travelling waves). Small mammals have therefore been the focus of a large number of empirical and statistical (analysis of time-series) studies, mostly based on trapping indices. These studies did not take into account sampling variability associated with the use of counts or estimates of population size. In this paper, we use our field study focusing on population dynamics and demography of small mammals in North Norway at three spatial scales (0.1, 10 and 100 km) to illustrate some methodological and practical issues. We first investigate the empirical patterns of spatial population dynamics, focusing on correlation among time-series of population abundance at increasing spatial scales. We then assess using simulated data the bias of estimates of spatial correlation induced by using either population indices such as the number of individuals captured (i.e., raw counts) or estimates of population size derived from statistical modeling of capture-recapture data. The problems encountered are similar to those described when assessing density-dependence in time-series -a special case of the consequence of measurement error for estimates of regression coefficients- but are to our knowledge ignored in the ecological literature. We suggest some empirical solutions as well as more rigorous approaches

Dinámica poblacional espacial de pequeños mamíferos: algunas cuestiones metodológicas y prácticas

Small mammals have been widely used to further our understanding of spatial and temporal population dynamical patterns, because their dynamics exhibit large variations, both in time (multi–annual cycles vs. seasonal variation only) and space (regional synchrony, travelling waves). Small mammals have therefore been the focus of a large number of empirical and statistical (analysis of time–series) studies, mostly based on trapping indices. These studies did not take into account sampling variability associated with the use of counts or estimates of population size. In this paper, we use our field study focusing on population dynamics and demography of small mammals in North Norway at three spatial scales (0.1, 10 and 100 km) to illustrate some methodological and practical issues. We first investigate the empirical patterns of spatial population dynamics, focusing on correlation among time–series of population abundance at increasing spatial scales. We then assess using simulated data the bias of estimates of spatial correlation induced by using either population indices such as the number of individuals captured (i.e., raw counts) or estimates of population size derived from statistical modeling of capture–recapture data. The problems encountered are similar to those described when assessing density–dependence in time-series —a special case of the consequence of measurement error for estimates of regression coefficients— but are to our knowledge ignored in the ecological literature. We suggest some empirical solutions as well as more rigorous approaches.Los pequeños mamíferos se han utilizado ampliamente para ayudarnos a comprender mejor las pautas dinámicas espaciales y temporales de las poblaciones. Ello obedece a que sus dinámicas presentan importantes variaciones, tanto por lo que respecta al tiempo (ciclos multianuales frente a una única variación estacional) como al espacio (sincronía regional, ondas progresivas). Por consiguiente, los pequeños mamíferos han sido objeto de gran número de estudios empíricos y estadísticos (análisis de series temporales), basados principalmente en índices de capturas por trampa. Dichos estudios no tomaban en consideración la variabilidad del muestreo asociada al empleo de recuentos o de estimaciones del tamaño poblacional. En el presente artículo utilizamos nuestro estudio de campo para analizar la dinámica poblacional y la demografía de los pequeños mamíferos del norte de Noruega en tres escalas espaciales (0,1, 10 y 100 km), además de ilustrar algunas cuestiones prácticas y metodológicas. En primer lugar, investigamos las pautas empíricas de la dinámica poblacional espacial, centrándonos en la correlación existente entre las series temporales de la abundancia poblacional en escalas espaciales cada vez mayores. Seguidamente, utilizamos datos simulados para evaluar el sesgo de las estimaciones de la correlación espacial inducida mediante el empleo, bien de índices poblacionales como el número de individuos capturados (es decir, recuentos brutos) o estimaciones del tamaño oblacional derivadas de la modelación estadística de los datos de captura–recaptura. Los problemas encontrados son similares a los descritos cuando se evalúa la dependencia de la densidad en las series temporales —un caso especial de la consecuencia del error de medición con respecto a las estimaciones de coeficientes de regresión—, pese a que, según parece, se han ignorado en la literatura ecológica. Por último, sugerimos algunas soluciones empíricas, así como planteamientos más rigurosos

Primary care physician perceptions of non-steroidal anti-inflammatory drug and aspirin-associated toxicity: results of a national survey

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74799/1/j.1365-2036.2006.02810.x.pd

Enhancing accuracy and precision of transparent synthetic soil modelling

Over recent years non-intrusive modelling techniques have been developed to investigate soil-structure interaction problems of increasingly complex geometry. This paper concerns the development of a small-scale, 1 g, modelling technique using a transparent analogue for soil with particle image velocimetry for internal displacement measurement. Larger model geometry achieved in this research using fine-grained transparent synthetic soils has led to an increased need for rigorous photogrammetric correction techniques. A correction framework, based upon a modified version of the pinhole camera model, is presented that corrects for lens and camera movement induced errors as well as scaling from image space to object space. An additional statistical approach is also developed to enhance the system precision, by minimising the impact of increased non-coplanarity between the photogrammetry control plane and the target plane. The enhanced data correction and statistical precision is demonstrated using a case study examining the failure mechanism around a double helical screw pile installed in transparent synthetic soil representative of a soft clay

Introducing an online community into a clinical education setting: a pilot study of student and staff engagement and outcomes using blended learning

<p>Abstract</p> <p>Background</p> <p>There are growing reasons to use both information and communication functions of learning technologies as part of clinical education, but the literature offers few accounts of such implementations or evaluations of their impact. This paper details the process of implementing a blend of online and face-to-face learning and teaching in a clinical education setting and it reports on the educational impact of this innovation.</p> <p>Methods</p> <p>This study designed an online community to complement a series of on-site workshops and monitored its use over a semester. Quantitative and qualitative data recording 43 final-year medical students' and 13 clinical educators' experiences with this blended approach to learning and teaching were analysed using access, adoption and quality criteria as measures of impact.</p> <p>Results</p> <p>The introduction of the online community produced high student ratings of the quality of learning and teaching and it produced student academic results that were equivalent to those from face-to-face-only learning and teaching. Staff had mixed views about using blended learning.</p> <p>Conclusions</p> <p>Projects such as this take skilled effort and time. Strong incentives are required to encourage clinical staff and students to use a new mode of communication. A more synchronous or multi-channel communication feedback system might stimulate increased adoption. Cultural change in clinical teaching is also required before clinical education can benefit more widely from initiatives such as this.</p