Possingham, H. P. | Search bioRxiv

bioRxiv2018

Operationalizing ecological connectivity in spatial conservation planning with Marxan Connect

O_LIGlobally, protected areas are being established to protect biodiversity and to promote ecosystem resilience. The typical spatial conservation planning process leading to the creation of these protected areas focuses on representation and replication of ecological features, often using decision support systems such as Marxan. Unfortunately, Marxan currently requires manual input or specialised scripts to explicitly consider ecological connectivity, a property critical to metapopulation persistence and resilience.\nC_LIO_LI\"Marxan Connect\" is a new open source, open access Graphical User Interface (GUI) designed to assist conservation planners in the systematic operationalization of ecological connectivity in protected area network planning.\nC_LIO_LIMarxan Connect is able to incorporate estimates of demographic connectivity (e.g. derived from tracking data, dispersal models, or genetics) or structural landscape connectivity (e.g. isolation by resistance). This is accomplished by calculating metapopulation-relevant connectivity metrics (e.g. eigenvector centrality) and treating those as conservation features, or using the connectivity data as a spatial dependency amongst sites to be included in the prioritization process.\nC_LIO_LIMarxan Connect allows a wide group of users to incorporate directional ecological connectivity into conservation plans. The least-cost conservation solutions provided by Marxan Connect, combined with ecologically relevant post-hoc testing, are more likely to support persistent and resilient metapopulations (e.g. fish stocks) and provide better protection for biodiversity than if connectivity is ignored.\nC_LI

ecology↗

bioRxiv2017

Spatially Explicit Approach To Population Abundance Estimation In Field Surveys

The abundance of species is a fundamental consideration in ecology and conservation biology. Although broad models have been proposed to estimate the population abundance using existing data, available data is often limited. With no information available, a population estimation will rely on time consuming field surveys. Typically, time is a critical constraint in conservation and often management decisions must be made quickly under the data limited situation. Depending on time and budgetary constraints, the required accuracy of field survey changes significantly. Hence, it is desirable to set up an effective survey design to minimize time and effort of sampling given required accuracy. We examine a spatially-explicit approach to population estimation using spatial point processes, enabling us to explicitly and consistently discuss various sampling designs. We find that the accuracy of abundance estimation varies with both ecological factors and survey design. Although the spatial scale of sampling does not affect estimation accuracy when the underlying individual distribution is random, it decreases with the sampled unit size if individuals tend to form clusters. These results are derived analytically and checked numerically. Obtained insights provide a benchmark to predict the quality of population estimation, and improve survey designs for ecological studies and conservation.

ecology↗