ORCID Profile
0000-0002-0022-1060
Current Organisation
University of Tasmania
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Publisher: Authorea, Inc.
Date: 25-05-2023
DOI: 10.22541/AU.168502324.42187548/V1
Abstract: As a source of information on species’ geographic distributions, macroecologists and biogeographers have had to rely on expert-derived range maps to study bio ersity patterns at large scales. In addition to being biased towards well-studied taxa and subjective by nature, such maps suffer from a lack of consistency in how species’ absences are treated within the wider distribution. Using the finer resolution of the Interim Biogeographic Regionalization for Australia (subregions) and ex le sets of Australian species as study system, we developed a reproducible, data-driven approach to map the extent of occurrence (EOO) of hundreds—or even thousands—of species by combining presence-only data and subregions (i.e., non-equal-sized operational units that represent homogenous areas of unique environmental features) within a unifying quantitative framework. From data-driven and expert-derived range maps for 533 birds, species richness’ estimates differ at three biogeographical scales—whit bias (mean error) at coarser resolution (ecoregion) being half that at subregional scale—and the spatial association between pairs of these birds’ presence-absence maps vary from nearly zero to almost one (representing such pattern almost either differently or identically, respectively). Holes within the wider distribution of the EOO maps for pairs of hibians, mammals, reptiles, and plants seem to respond to the demarcation of different subpopulations over Australia rather than causing an underestimation of a species’ empirical distribution. These results demonstrate that this approach can reliably map EOO of species whose distributions aligns with three broad types of geographic patterns (wide-range, habitat-specialists, and range-restricted species). This alternative to expert-derived range maps can serve as a basis for more robust, data-driven studies of biogeographic bio ersity patterns, thus improving our understanding and conservation efforts of global bio ersity.
Publisher: Cambridge University Press (CUP)
Date: 02-11-2016
DOI: 10.1017/S0376892916000424
Abstract: Protected areas (PAs) have long been the foundation of conservation strategies to halt bio ersity losses and ecosystem degradation. In the South Caucasus (SC), coverage of PAs increased after the collapse of the Soviet Union in 1991, yet how well bio ersity is represented in them is unknown. We utilized the PA downgrading, downsizing and degazettement (PADDD) conceptual framework and the gap analysis approach to assess how changes in the PAs of Armenia, Azerbaijan and Georgia between 1991 and 2014 have affected the representation of bio ersity. Throughout this period, vegetation formations associated with high mountain ecosystems changed (≥17% representation). Colchic lowland vegetation formations which are only present in Georgia, also changed from unrepresented (0%) to under-represented (from 0% to %). The effect of PADDD events on bio ersity representation varied among countries depending on the amount of area gazetted after 1991. There is an inherent bias in the expansion of PAs in the SC. Our findings could be a first step towards changing the status quo by helping conservationists to strategically allocate resources towards ecosystems that are below 17% representation. Yet this will require governments in the SC to shift their views about PAs from being only national efforts to being key pieces of a larger-scale conservation strategy.
Publisher: Fondazione Pro Herbario Mediterraneo
Date: 18-11-2016
Publisher: Springer Science and Business Media LLC
Date: 29-11-2020
Publisher: Cold Spring Harbor Laboratory
Date: 09-2021
DOI: 10.1101/2021.08.31.458457
Abstract: Despite the increasing interest in developing new bioregionalizations and assessing the most widely accepted biogeographic frameworks, no study to date has sought to systematically define a system of small bioregions nested within larger ones that better reflect the distribution and patterns of bio ersity. Here, we examine how an algorithmic, data-driven model of ersity patterns can lead to an ecologically interpretable hierarchy of bioregions. Australia. Present. Terrestrial vertebrates and vascular plants. We compiled information on the biophysical characteristics and species occupancy of Australia’s geographic conservation units (bioregions). Then, using cluster analysis to identify groupings of bioregions representing optimal discrete-species areas, we evaluated what a hierarchical bioregionalization system would look like when based empirically on the within-and between-site ersity patterns across taxa. Within an information-analytical framework, we then assessed the degree to which the World Wildlife Fund’s (WWF) biomes and ecoregions and our suite of discrete-species areas are spatially associated and compared those results among bioregionalization scenarios. Information on bio ersity patterns captured was moderate for WWF’s biomes (50– 58% for birds’ beta, and plants’ alpha and beta ersity, of optimal discrete areas, respectively) and ecoregions (additional 4–25%). Our plants and vertebrate optimal areas retained more information on alpha and beta ersity across taxa, with the two algorithmically derived biogeographic scenarios sharing 86.5% of their within- and between-site ersity information. Notably, discrete-species areas for beta ersity were parsimonious with respect to those for alpha ersity. Nested systems of bioregions must systematically account for the variation of species ersity across taxa if bio ersity research and conservation action are to be most effective across multiple spatial or temporal planning scales. By demonstrating an algorithmic rather than subjective method for defining bioregionalizations using species- ersity concordances, which reliably reflects the distributional patterns of multiple taxa, this work offers a valuable new tool for systematic conservation planning.
No related grants have been discovered for Cristian S. Montalvo-Mancheno.