Non-Linearity in Ecosystem Services: Temporal and Spatial Variability in Coastal Protection

Natural processes tend to vary over time and space, as well as between species. The ecosystem services these natural processes provide are therefore also highly variable. It is often assumed that ecosystem services are provided linearly (unvaryingly, at a steady rate), but natural processes are characterized by thresholds and limiting functions. In this paper, we describe the variability observed in wave attenuation provided by marshes, mangroves, seagrasses, and coral reefs and therefore also in coastal protection. We calculate the economic consequences of assuming coastal protection to be linear. We suggest that, in order to refine ecosystem-based management practices, it is essential that natural variability and cumulative effects be considered in the valuation of ecosystem services

A theory of organizational readiness for change

<p>Abstract</p> <p>Background</p> <p>Change management experts have emphasized the importance of establishing organizational readiness for change and recommended various strategies for creating it. Although the advice seems reasonable, the scientific basis for it is limited. Unlike individual readiness for change, organizational readiness for change has not been subject to extensive theoretical development or empirical study. In this article, I conceptually define organizational readiness for change and develop a theory of its determinants and outcomes. I focus on the organizational level of analysis because many promising approaches to improving healthcare delivery entail collective behavior change in the form of systems redesign--that is, multiple, simultaneous changes in staffing, work flow, decision making, communication, and reward systems.</p> <p>Discussion</p> <p>Organizational readiness for change is a multi-level, multi-faceted construct. As an organization-level construct, readiness for change refers to organizational members' shared resolve to implement a change (change commitment) and shared belief in their collective capability to do so (change efficacy). Organizational readiness for change varies as a function of how much organizational members value the change and how favorably they appraise three key determinants of implementation capability: task demands, resource availability, and situational factors. When organizational readiness for change is high, organizational members are more likely to initiate change, exert greater effort, exhibit greater persistence, and display more cooperative behavior. The result is more effective implementation.</p> <p>Summary</p> <p>The theory described in this article treats organizational readiness as a shared psychological state in which organizational members feel committed to implementing an organizational change and confident in their collective abilities to do so. This way of thinking about organizational readiness is best suited for examining organizational changes where collective behavior change is necessary in order to effectively implement the change and, in some instances, for the change to produce anticipated benefits. Testing the theory would require further measurement development and careful sampling decisions. The theory offers a means of reconciling the structural and psychological views of organizational readiness found in the literature. Further, the theory suggests the possibility that the strategies that change management experts recommend are equifinal. That is, there is no 'one best way' to increase organizational readiness for change.</p

Reliability and validity of a point-of-care sural nerve conduction device for identification of diabetic neuropathy.

BACKGROUND: Confirmation of diabetic sensorimotor polyneuropathy (DSP) relies on standard nerve conduction studies (NCS) performed in specialized clinics. We explored the utility of a point-of-care device (POCD) for DSP detection by nontechnical personnel and a validation of diagnostic thresholds with those observed in a normative database. RESEARCH DESIGN AND METHODS: 44 subjects with type 1 and type 2 diabetes underwent standard NCS (reference method). Two nontechnical examiners measured sural nerve amplitude potential (SNAP) and conduction velocity (SNCV) using the POCD. Reliability was determined by intraclass correlation coefficients (ICC [2], [1]). Validity was determined by Bland-Altman analysis and receiver operating characteristic curves. RESULTS: The 44 subjects (50% female) with mean age 56 ± 18 years had mean SNAP and SNCV of 8.0 ± 8.6 µV and 41.5 ± 8.2 m/s using standard NCS and 8.0 ± 8.2 µV and 49.9 ± 11.1 m/s using the POCD. Intrarater reproducibility ICC values were 0.97 for SNAP and 0.94 for SNCV while interrater reproducibility values were 0.83 and 0.79, respectively. Mean bias of the POCD was -0.1 ± 3.6 µV for SNAP and +8.4 ± 6.4 m/s for SNCV. A SNAP of ≤6 µV had 88% sensitivity and 94% specificity for identifying age-and height-standardized reference NCS values, while a SNCV of ≤48 m/s had 94% sensitivity and 82% specificity [corrected].. Abnormality in one or more of these thresholds was associated with 95% sensitivity and 71% specificity for identification of DSP according to electrophysiological criteria. CONCLUSIONS: The POCD demonstrated excellent reliability and acceptable accuracy. Threshold values for DSP identification validated those of published POCD normative values. We emphasize the presence of measurement bias--particularly for SNCV--that requires adjustment of threshold values to reflect those of standard NCS

Identification and prediction of diabetic sensorimotor polyneuropathy using individual and simple combinations of nerve conduction study parameters.

OBJECTIVE: Evaluation of diabetic sensorimotor polyneuropathy (DSP) is hindered by the need for complex nerve conduction study (NCS) protocols and lack of predictive biomarkers. We aimed to determine the performance of single and simple combinations of NCS parameters for identification and future prediction of DSP. MATERIALS AND METHODS: 406 participants (61 with type 1 diabetes and 345 with type 2 diabetes) with a broad spectrum of neuropathy, from none to severe, underwent NCS to determine presence or absence of DSP for cross-sectional (concurrent validity) analysis. The 109 participants without baseline DSP were re-evaluated for its future onset (predictive validity). Performance of NCS parameters was compared by area under the receiver operating characteristic curve (AROC). RESULTS: At baseline there were 246 (60%) Prevalent Cases. After 3.9 years mean follow-up, 25 (23%) of the 109 Prevalent Controls that were followed became Incident DSP Cases. Threshold values for peroneal conduction velocity and sural amplitude potential best identified Prevalent Cases (AROC 0.90 and 0.83, sensitivity 80 and 83%, specificity 89 and 72%, respectively). Baseline tibial F-wave latency, peroneal conduction velocity and the sum of three lower limb nerve conduction velocities (sural, peroneal, and tibial) best predicted 4-year incidence (AROC 0.79, 0.79, and 0.85; sensitivity 79, 70, and 81%; specificity 63, 74 and 77%, respectively). DISCUSSION: Individual NCS parameters or their simple combinations are valid measures for identification and future prediction of DSP. Further research into the predictive roles of tibial F-wave latencies, peroneal conduction velocity, and sum of conduction velocities as markers of incipient nerve injury is needed to risk-stratify individuals for clinical and research protocols

Measurement of cooling detection thresholds for identification of diabetic sensorimotor polyneuropathy in type 1 diabetes.

OBJECTIVE: Compared to recently-studied novel morphological measures, conventional small nerve fiber functional tests have not been systematically studied for identification of diabetic sensorimotor polyneuropathy (DSP). We aimed to determine and compare the diagnostic performance of cooling detection thresholds (CDT) in a cross-sectional type 1 diabetes cohort. RESEARCH DESIGN AND METHODS: 136 subjects with type 1 diabetes and 52 healthy volunteers underwent clinical and electrophysiological examination for DSP classification concomitantly with the Toronto Clinical Neuropathy Score (TCNS) and three small fiber function tests: CDT, heart rate variability (HRV), and laser doppler imaging of axon-mediated neurogenic flare responses to cutaneous heating (LDIFLARE). Area under the curve (AUC) and optimal thresholds were determined by receiver operating characteristic (ROC) curves in the type 1 diabetes cohort. RESULTS: Type 1 diabetes subjects were 42±17 years of age with mean HbA1c 7.9±1.7%. Fifty-nine (45%) met the case definition for DSP. CDT values were lowest in cases with DSP (18.3±8.4°C) compared to controls without DSP (28.4±3.5°C) and to healthy volunteers (29.6±1.8°C; p-value for both comparisons<0.0001). AUCCDT was 0.863 which was similar to AUCTCNS (0.858, p = 0.24) and AUCHRV (0.788, p = 0.05), but exceeded AUCLDIFLARE (0.750, p = 0.001). The threshold of <25.1°C was equivalent to the lower bound of the healthy volunteer 95% distribution [25.1, 30.8°C] and performed with 83% sensitivity and 82% specificity. CONCLUSIONS: Akin to novel small fiber morphological measures, CDT is a functional test that identifies DSP with very good diagnostic performance. These findings support further research that revisits the role of CDT in early DSP detection

Scatterplots (A,B) and Bland-Altman plots (C,D) for comparison of the point-of-care nerve conduction method versus standard NCS for SNAP or SNCV.

<p>Panels A and B: Scatterplot of SNAP (A) and SNCV (B) showing the line of unity (diagonal solid line) between the two methods. Panels C and D: The Bland-Altman plots demonstrating the mean difference (depicted by the solid line) between SNAP (C) or SNCV (D) obtained by the two methods. Points above or below zero on the y-axis represent over- and underestimation by the point-of-care device, respectively. The dotted lines represent the upper and lower limits of the 85% confidence interval. Unrecordable SNCV results for both nerve conduction methods were assigned a value of 30.4 m/s, representing the lowest value in the dataset. Such data handling was applied to 9 values for standard NCS and 3 values for the point-of-care device.</p

Intra- and interrater reliability of the sural nerve amplitude potential and conduction velocity using the point-of-care device for 44 subjects with type 1 and type 2 diabetes.

<p>IQR = Interquartile range; ICC = Interclass correlation coefficients class(2,1); SNAP = sural nerve amplitude potential; SNCV = sural nerve conduction velocity.</p

Sample nerve conduction recordings from standard NCS (A) and the point-of-care device (B) from a 60-year-old female with type 2 diabetes and an image of the point-of-care procedure (C).

<p>Panel A: Sample standard NCS recording. Sural nerve amplitude potential was 6.8 µV and conduction velocity was 48.3 m/s. Panel B: Sample recording from the point-of-care device. Sural nerve amplitude potential was 8 µV and conduction velocity was 56 m/s. Panel C: The device was placed on the lateral aspect of the leg and the sural nerve was stimulated and recorded by the electrical probes and biosensor, respectively.</p