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Detecting tropical wildlife declines through camera-trap monitoring : An evaluation of the Tropical Ecology Assessment and Monitoring protocol
Beaudrot, Lydia ; Ahumada, Jorge ; O'Brien, Timothy G. ; Jansen, Patrick A. - \ 2019
Oryx 53 (2019)1. - ISSN 0030-6053 - p. 126 - 129.
Camera trap - conservation - monitoring - power analysis - sampling design - Tropical Ecology Assessment and Monitoring - wildlife management
Identifying optimal sampling designs for detecting population-level declines is critical for optimizing expenditures by research and monitoring programmes. The Tropical Ecology Assessment and Monitoring (TEAM) network is the most extensive tropical camera-trap monitoring programme, but the effectiveness of its sampling protocol has not been rigorously assessed. Here, we assess the power and sensitivity of the programme's camera-trap monitoring protocol for detecting occupancy changes in unmarked populations using the freely available application PowerSensor!. We found that the protocol is well suited to detect moderate (≥ 5%) population changes within 3-4 years for relatively common species that have medium to high detection probabilities (i.e. p > 0.2). The TEAM protocol cannot, however, detect typical changes in rare and evasive species, a category into which many tropical species and many species of conservation concern fall. Additional research is needed to build occupancy models for detecting change in rare and elusive species when individuals are unmarked.
Data from: How much would it cost to monitor farmland biodiversity in Europe?
Geijzendorffer, I.R. ; Targetti, Stefano ; Schneider, Manuel K. ; Brus, D.J. ; Jongman, R.H.G. ; Knotters, M. ; Bogers, M.M.B. ; Staritsky, I.G. - \ 2015
species richness - farmland biodiversity - habitat - plants - spiders - bees - earth worms - agriculture - agri-environment schemes - biodiversity indicator - common agricultural policy - empirical data - farming system - sampling design - species trend - power analysis
To evaluate progress on political biodiversity objectives, biodiversity monitoring provides information on whether intended results are being achieved. Despite scientific proof that monitoring and evaluation increase the (cost) efficiency of policy measures, cost estimates for monitoring schemes are seldom available, hampering their inclusion in policy programme budgets. Empirical data collected from 12 case studies across Europe were used in a power analysis to estimate the number of farms that would need to be sampled per major farm type to detect changes in species richness over time for four taxa (vascular plants, earthworms, spiders and bees). A sampling design was developed to allocate spatially, across Europe, the farms that should be sampled. Cost estimates are provided for nine monitoring scenarios with differing robustness for detecting temporal changes in species numbers. These cost estimates are compared with the Common Agricultural Policy (CAP) budget (2014–2020) to determine the budget allocation required for the proposed farmland biodiversity monitoring. Results show that the bee indicator requires the highest number of farms to be sampled and the vascular plant indicator the lowest. The costs for the nine farmland biodiversity monitoring scenarios corresponded to 0·01%–0·74% of the total CAP budget and to 0·04%–2·48% of the CAP budget specifically allocated to environmental targets. Synthesis and applications. The results of the cost scenarios demonstrate that, based on the taxa and methods used in this study, a Europe-wide farmland biodiversity monitoring scheme would require a modest share of the Common Agricultural Policy budget. The monitoring scenarios are flexible and can be adapted or complemented with alternate data collection options (e.g. at national scale or voluntary efforts), data mobilization, data integration or modelling efforts.
Measurement network design including traveltime determinations to minimize model prediction uncertainty
Janssen, G.M.C.M. ; Valstar, J.R. ; Zee, S.E.A.T.M. van der - \ 2008
Water Resources Research 44 (2008). - ISSN 0043-1397 - 17 p.
groundwater solute transport - waste management sites - monitoring network - shallow groundwater - sampling design - cape-cod - environmental tracers - parameter-estimation - engineering design - regulatory policy
Traveltime determinations have found increasing application in the characterization of groundwater systems. No algorithms are available, however, to optimally design sampling strategies including this information type. We propose a first-order methodology to include groundwater age or tracer arrival time determinations in measurement network design and apply the methodology in an illustrative example in which the network design is directed at contaminant breakthrough uncertainty minimization. We calculate linearized covariances between potential measurements and the goal variables of which we want to reduce the uncertainty: the groundwater age at the control plane and the breakthrough locations of the contaminant. We assume the traveltime to be lognormally distributed and therefore logtransform the age determinations in compliance with the adopted Bayesian framework. Accordingly, we derive expressions for the linearized covariances between the transformed age determinations and the parameters and states. In our synthetic numerical example, the derived expressions are shown to provide good first-order predictions of the variance of the natural logarithm of groundwater age if the variance of the natural logarithm of the conductivity is less than 3.0. The calculated covariances can be used to predict the posterior breakthrough variance belonging to a candidate network before samples are taken. A Genetic Algorithm is used to efficiently search, among all candidate networks, for a near-optimal one. We show that, in our numerical example, an age estimation network outperforms (in terms of breakthrough uncertainty reduction) equally sized head measurement networks and conductivity measurement networks even if the age estimations are highly uncertain.
The Rauischholzhausen agenda for road ecology
Roedenbeck, I.A. ; Fahrig, L. ; Findlay, C.S. ; Houlahan, J.E. ; Jaeger, J.A.G. ; Klar, N. ; Kramer-Schadt, S. ; Grift, E.A. van der - \ 2007
Ecology and Society 12 (2007)1. - ISSN 1708-3087 - 21 p.
breeding bird populations - precautionary principle - environmental impacts - swareflex reflectors - sampling design - habitat - conservation - density - deer - biodiversity
Despite the documented negative effects of roads on wildlife, ecological research on road effects has had comparatively little influence on road planning decisions. We argue that road research would have a larger impact if researchers carefully considered the relevance of the research questions addressed and the inferential strength of the studies undertaken. At a workshop at the German castle of Rauischholzhausen we identified five particularly relevant questions, which we suggest provide the framework for a research agenda for road ecology: (1) Under what circumstances do roads affect population persistence? (2) What is the relative importance of road effects vs. other effects on population persistence? (3) Under what circumstances can road effects be mitigated? (4) What is the relative importance of the different mechanisms by which roads affect population persistence? (5) Under what circumstances do road networks affect population persistence at the landscape scale? We recommend experimental designs that maximize inferential strength, given existing constraints, and we provide hypothetical examples of such experiments for each of the five research questions. In general, manipulative experiments have higher inferential strength than do nonmanipulative experiments, and full before-after-control-impact designs are preferable to before-after or control-impact designs. Finally, we argue that both scientists and planners must be aware of the limits to inferential strength that exist for a given research question in a given situation. In particular, when the maximum inferential strength of any feasible design is low, decision makers must not demand stronger evidence before incorporating research results into the planning process, even though the level of uncertainty may be high