With the expansion in the quantity and types of biodiversity data being collected, there is a need to find ways to combine these different sources to provide cohesive summaries of species' potential and realized distributions in space and time. Recently, model-based data integration has emerged as a means to achieve this by combining datasets in ways that retain the strengths of each. We describe a flexible approach to data integration using point process models, which provide a convenient way to translate across ecological currencies. We highlight recent examples of large-scale ecological models based on data integration and outline the conceptual and technical challenges and opportunities that arise. Species Distribution Models in EcologyLarge-scale ecological models of how species distributions and abundances vary over space and time are a critical tool in macroecology, biogeography, and conservation biology. They underpin our understanding of how biodiversity is shaped, how it is responding to anthropogenic activities, and how it might change in the future [1][2][3]. There is now a substantial literature on statistical tools for building species distribution models (SDMs) (see Glossary) and best practice in how to fit them [4][5][6][7]. SDMs also form a building block upon which more complex models, incorporating occupancy and/or abundance in space and time, can be built [8,9].
Summary1. The rapid expansion of systematic monitoring schemes necessitates robust methods to reliably assess species' status and trends. Insect monitoring poses a challenge where there are strong seasonal patterns, requiring repeated counts to reliably assess abundance. Butterfly monitoring schemes (BMSs) operate in an increasing number of countries with broadly the same methodology, yet they differ in their observation frequency and in the methods used to compute annual abundance indices. 2. Using simulated and observed data, we performed an extensive comparison of two approaches used to derive abundance indices from count data collected via BMS, under a range of sampling frequencies. Linear interpolation is most commonly used to estimate abundance indices from seasonal count series. A second method, hereafter the regional generalized additive model (GAM), fits a GAM to repeated counts within sites across a climatic region. For the two methods, we estimated bias in abundance indices and the statistical power for detecting trends, given different proportions of missing counts. We also compared the accuracy of trend estimates using systematically degraded observed counts of the Gatekeeper Pyronia tithonus (Linnaeus 1767). 3. The regional GAM method generally outperforms the linear interpolation method. When the proportion of missing counts increased beyond 50%, indices derived via the linear interpolation method showed substantially higher estimation error as well as clear biases, in comparison to the regional GAM method. The regional GAM method also showed higher power to detect trends when the proportion of missing counts was substantial. 2016, 53, 501-510 doi: 10.1111/1365-2664.12561 4. Synthesis and applications. Monitoring offers invaluable data to support conservation policy and management, but requires robust analysis approaches and guidance for new and expanding schemes. Based on our findings, we recommend the regional generalized additive model approach when conducting integrative analyses across schemes, or when analysing scheme data with reduced sampling efforts. This method enables existing schemes to be expanded or new schemes to be developed with reduced within-year sampling frequency, as well as affording options to adapt protocols to more efficiently assess species status and trends across large geographical scales. Journal of Applied Ecology
The importance of Cultural Ecosystem Services (CES) to human wellbeing is widely recognised. However, quantifying these non-material benefits is challenging and consequently they are often not assessed. Mapping approaches are increasingly being used to understand the spatial distribution of different CES and how this relates to landscape characteristics. This study uses an online Public Participation Geographic Information System (PPGIS) to elicit information on outdoor locations important to respondents in Wiltshire, a dynamic lowland landscape in southern England. We analysed these locations in a GIS with spatial datasets representing potential influential factors, including protected areas, land use, landform, and accessibility. We assess these characteristics at different spatial and visual scales for different types of cultural engagement. We find that areas that are accessible, near to urban centres, with larger views, and a high diversity of protected habitats, are important for the delivery of CES. Other characteristics including a larger area of woodland and the presence of sites of historic interest in the surrounding landscape were also influential. These findings have implications for land-use planning and the management of ecosystems, by demonstrating the benefits of high quality ecological sites near to towns. The importance of maintaining and restoring landscape features, such as woodlands, to enhance the delivery of CES were also highlighted.
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