A synecological framework for systematic conservation planning

Abstract

Abstract.− Biodiversity conservation design, though difficult with fragmentary or insufficient biological data, can be planned and evaluated with several methods. One of them, the complementarity criterion, is commonly used to account for the distributions of a number of species (i.e., an autoecological approach). At the same time, the patchiness and spatial bias of available distribution data has also been dealt with through distribution modelling. However, both the uncertainty of the ranges estimated and the changes in species' distributions in response to changing climates, limit the potential of single-species distributions as the biodiversity attribute to be used in complementarity strategies. Several technical and theoretical advantages of composite biodiversity variables (i.e., a synecological approach) may, however, make them ideal biodiversity indicators for conservation area selection. The drawbacks associated with current biodiversity data are discussed herein, along with the possible advantages and disadvantages of conservation planning through a synecological or autoecological approach.

Can Species Richness Patterns Be Interpolated From a Limited Number of Well-Known Areas? Mapping Diversity Using GLM and Kriging

“…Typically, the information gathered in biodiversity databases provides an unreliable picture of the distribution of diversity, plagued with geographical and taxonomic gaps, limitations and biases (see Rocchini et al 2011 and references therein). However, such knowledge is needed to both (i) describe and study the causes of the geographic distribution of biodiversity, and to (ii) design effective conservation strategies and protected area networks (Ferrier 2002;Hortal & Lobo 2006) when reliable information on species composition is lacking.…”

“…Many modelling techniques have been proposed so far, mainly grouped in two kinds of approaches; (i) Species Distribution Models (SDM) intend to predict the spatial distribution of single species from data on their occurrences and an array of-often environmental-predictors (Guisan & Zimmermann 2000); (ii) Assemblage-level Models (ALM)-also called Synecological Models (Hortal & Lobo 2006), Community-level Models (Ferrier & Guisan 2006) or Macroecological Models (Guisan & Rahbek 2011)-aim to represent the spatial variations in the diversity of whole assemblages based on data from a few well-known areas and the corresponding set of predictors (Austin et al 1996). While SDM techniques are widely used to project species distributions into different geographical and temporal scenarios (see Lobo et al 2010), ALMs receive less attention as predictive tools, being more used to study the relationships between diversity and environment (e.g.…”

“…Ferrier 2002), it provides a first approximation to the geographic variations of species diversity in the absence of good quality data on species distributions or composition, which is more prone to error (see Hortal et al 2007;Pineda & Lobo 2009;Aranda & Lobo 2011 for comparisons of both kinds of data). Being based in a simpler variable, it also provides a simpler way of analyzing the effects of data quality on model reliability; we assume that the results of this study can be extrapolated to ALM applications based on other aspects of biodiversity (see Hortal & Lobo 2006;Guisan & Rahbek 2011). Thus, here we compare the adequacy of GLM and kriging to interpolate richness values.…”

Distribución de las especies de Aphodiinae (Coleoptera, Scarabaeoidea, Aphodiidae) en México

“…Para la realización de este tipo de modelos suele utilizarse información biológica procedente de grupos y regiones no tan insuficientemente prospectados como en nuestro caso, pero es indudable que los resultados de estas técnicas, aunque deben siempre considerase con precaución (Jiménez-Valverde et al 2008), adquieren un interés especial cuando son capaces de realizar predicciones bajo las condiciones de carencia de información que generalmente abundan en los grupos y en las regiones mas diversificadas (Soberón & Peterson 2004, Whittaker et al 2005, Cayuela et al 2009). El procedimiento metodológico seguido en este trabajo permite corregir la frecuente sobrepredicción de este tipo de modelizaciones (Fielding & Haworth 1995, Araújo & Williams 2000, Stockwell & Peterson 2002, Brotons et al 2004, Segurado & Araújo 2004, Stockman et al 2006, especialmente cuando los valores de riqueza se obtienen a través del sumatorio de mapas estimados individuales (Hortal & Lobo 2006), y también validar las predicciones de riqueza obtenidas utilizando los resultados de diversos estimadores no-paramétricos. Pese a ello, puede decirse que la aproximación seguida en éste y en otros trabajos (Trotta-Moreu et al 2008, Verdú & Lobo 2008, Pineda & Lobo 2009, Trotta-Moreu & Lobo 2010) es atrevida y de dudosa eficiencia ya que, como nosotros mismos estimamos, proporciona un error de comisión promedio del 94% y un error de omisión medio del 28% (71% y 18%, respectivamente, en Geotrupidae; Trotta-Moreu & Lobo 2010).…”

Using classification assignment rules to assess land-use change impacts on forest biodiversity at local-to-national scales

“…Vegetation types provide information on habitats that are utilised by species other than plants, are often linked to specific characteristics of the physical environment and depict more ecological complexity than individual taxa (Scott et al 1993;Margules et al 2002). Others have argued that vegetation types themselves should be a key conservation unit (Hortal and Lobo 2006;Keith 2009). Ecosystem representation is often assessed by employing spatial (GIS)-based analyses using maps of vegetation types as a key layer (e.g.…”