A spatially explicit methodology for<i>a priori</i>estimation of field survey effort in environmental observation networks

Guerra, Carlos A.; Metzger, Marc J.; Honrado, João P.; Alonso, Joaquim

doi:10.1080/13658816.2013.805760

Cited by 6 publications

(4 citation statements)

References 44 publications

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“…In the case when a focal species lacks appropriate target‐group data or has too few presence points for background restriction to be viable, another option is the use of potential covariates of sample effort. Maps of human population density and road networks can provide an alternative means of estimating sampling effort, and can be incorporated into SDMs (Guerra et al, 2013; Kadmon et al, 2004; Monsarrat et al, 2019). While accessibility maps may work well in some scenarios (Dubos et al, 2021), there is currently no review of their effectiveness against other bias correction methods.…”

Section: Methodsmentioning

confidence: 99%

Target‐group backgrounds prove effective at correcting sampling bias in Maxent models

Barber

Ball

Morris

et al. 2021

Diversity and Distributions

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Aim Accounting for sampling bias is the greatest challenge facing presence‐only and presence‐background species distribution models; no matter what type of model is chosen, using biased data will mask the true relationship between occurrences and environmental predictors. To address this issue, we review four established bias correction techniques, using empirical occurrences with known sampling effort, and virtual species with known distributions. Innovation Occurrence data come from a national recording scheme of hoverflies (Syrphidae) in Great Britain, spanning 1983–2002. Target‐group backgrounds, distance‐restricted backgrounds, travel time to cities and human population density were used to account for sampling bias in 58 species of hoverfly. Distributions generated by bias correction techniques were compared in geographical space to the distribution produced accounting for known sampling effort, using Schoener's distance, centroid shifts and range size changes. To validate our results, we performed the same comparisons using 50 randomly generated virtual species. We used sampling effort from the hoverfly recording scheme to structure our biased sampling regime, emulating complex real‐life sampling bias. Main conclusions Models made without any correction typically produced distributions that mapped sampling effort rather than the underlying habitat suitability. Target‐group backgrounds performed the best at emulating sampling effort and unbiased virtual occurrences, but also showed signs of overcompensation in places. Other methods performed better than no‐correction, but often differences were difficult to visually detect. In line with previous studies, when sampling effort is unknown, target‐group backgrounds provide a useful tool for reducing the effect of sampling bias. Models should be visually inspected for biological realism to identify any areas of potential overcompensation. Given the disparity between corrected and un‐corrected models, sampling bias constitutes a major source of error in species distribution modelling, and more research is needed to confidently address the issue.

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Section: Methodsmentioning

confidence: 99%

Target‐group backgrounds prove effective at correcting sampling bias in Maxent models

Barber

Ball

Morris

et al. 2021

Diversity and Distributions

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“…The cost of each grid cell was set using the field survey effort metric (Guerra et al . ), defined as the effort that a surveyor will have to undertake to sample a given location. This metric was derived based on a set of topographic, land‐use and road accessibility variables and varies between 0·5 (low effort required for sampling) and 6·5 (high effort required for sampling).…”

Section: Methodsmentioning

confidence: 99%

Systematic site selection for multispecies monitoring networks

Carvalho

Gonçalves

Guisan

et al. 2015

Journal of Applied Ecology

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Summary1. The importance of monitoring biodiversity to detect and understand changes throughout time and to inform management is increasingly recognized. Monitoring schemes should be globally unified, spatially integrated across scales, long term, and cost-efficient. 2. We propose a framework to design optimized multispecies-targeted monitoring networks over large areas. The method builds upon previous developments on systematic conservation planning in terms of optimizing resource allocation in space, and comprises seven steps: (a) determine which questions will be addressed, (b) define species to be monitored, (c) compile occurrence data for all defined species, (d) predict the overall distribution of each species, (e) collect relevant environmental data and identify homogeneous strata, (f) set targets for the minimum number of monitoring sites per species and/or stratum and (g) identify optimal monitoring sites. 3. We tested whether the monitoring networks designed with our framework have increased performance when compared to networks obtained with simple-random or stratified-random sampling by using a set of different indicators. To that end, we designed monitoring networks using optimized and non-optimized sampling schemes, applied to a case study in Portugal, where the goal was to design a monitoring network for amphibians and reptiles, to complement the one currently established in Spain. 4. Results allowed us to conclude that monitoring networks designed with our method tend to outperform the non-optimized ones, in terms of higher species diversity (i.e. higher number of species and equity across monitoring sites), higher representation of environmental strata, and particularly higher coverage of rare species, with less survey effort. 5. Synthesis and applications. We developed a framework to allocate monitoring sites for multiple species at broad scales using predictive models and optimization algorithms currently applied in systematic conservation planning. This framework presents field survey cost-efficiency advantages when compared to other standard sampling designs and can significantly contribute to improving the design of monitoring schemes. Thus, we recommend its application to design new multispecies monitoring networks or to extend existing ones.

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“…A cost was assigned to each replica using the survey effort index developed by Guerra et al . (). We also generated 100 random networks of the same network size ( n 5) to define benchmarks to which the generated networks were compared (a procedure with long tradition in conservation planning; e.g.…”

Section: Methodsmentioning

confidence: 97%

“…Given that the spatial arrangement of a monitoring network affects financial costs, we also assessed the cost to survey each solution, using a monitoring effort map developed by Guerra et al . () (see Appendix S3 for a detailed description).…”

Section: Methodsmentioning

confidence: 99%

Cost‐effective monitoring of biological invasions under global change: a model‐based framework

Vicente

Alagador

Guerra

et al. 2016

Journal of Applied Ecology

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1. Ecological monitoring programmes are designed to detect and measure changes in biodiversity and ecosystems. In the case of biological invasions, they can contribute to anticipating risks and adaptively managing invaders. However, monitoring is often expensive because large amounts of data might be needed to draw inferences. Thus, careful planning is required to ensure that monitoring goals are realistically achieved. 2. Species distribution models (SDMs) can provide estimates of suitable areas to invasion. Predictions from these models can be applied as inputs in optimization strategies seeking to identify the optimal extent of the networks of areas required for monitoring risk of invasion under current and future environmental conditions. A hierarchical framework is proposed herein that combines SDMs, scenario analysis and cost analyses to improve invasion assessments at regional and local scales. We illustrate the framework with Acacia dealbata Link. (Silver-wattle) in northern Portugal. The framework is general and applicable to any species. 3. We defined two types of monitoring networks focusing either on the regional-scale management of an invasion, or management focus within and around protected areas. For each one of these two schemes, we designed a hierarchical framework of spatial prioritization using different information layers (e.g. SDMs, habitat connectivity, protected areas). We compared the performance of each monitoring scheme against 100 randomly generated models. 4. In our case study, we found that protected areas will be increasingly exposed to invasion by A. dealbata due to climate change. Moreover, connectivity between suitable areas for A. dealbata is predicted to increase. Monitoring networks that we identify were more effective in detecting new invasions and less costly to management than randomly generated models. The most cost-efficient monitoring schemes require 18% less effort than the average networks across all of the 100 tested options. 5. Synthesis and applications. The proposed framework achieves cost-effective monitoring networks, enabling the interactive exploration of different solutions and the combination of quantitative information on network performance with orientations that are rarely incorporated in a decision support system. The framework brings invasion monitoring closer to European legislation and management needs while ensuring adaptability under rapid climate and environmental change.

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A spatially explicit methodology fora prioriestimation of field survey effort in environmental observation networks

Cited by 6 publications

References 44 publications

Target‐group backgrounds prove effective at correcting sampling bias in Maxent models

Target‐group backgrounds prove effective at correcting sampling bias in Maxent models

Systematic site selection for multispecies monitoring networks

Cost‐effective monitoring of biological invasions under global change: a model‐based framework

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