El Plan Territorial de Protección Civil de la Región de Murcia

This thesis made original contributions to knowledge by addressing the knowledge gaps identified in Chapter 1 in the manner discussed below.

6.1.1 Chapter 2: Identifying timber tree taxa in trade: A working list of commercial timber trees

Knowledge gap:

The number of angiosperm tree species currently exploited and traded commercially for their timber is unknown.

Findings:

Chapter 2 identified 1,578 tree taxa that were traded for timber under Latin binomials or trinomials, and consolidated these taxa into a working list of angiosperm timbers. Of these, 12 taxa in the Arecaceae (palm) family were pinpointed as being misidentified, bringing the working list down to 1,566 tree taxa identified as being traded

commercially for timber. These findings therefore go some way towards answering Research Question 1: ‘How many angiosperm tree taxa are currently harvested and traded for timber?’, and fulfil Objectives 1a and 1b.

Implications:

It is possible that more of these 1,566 tree taxa have been misidentified and are not in fact timber trees, and it is highly likely that many timber tree taxa were not added to the working list due to search specifications (i.e. many will be documented under common, trade or genus name) and the need to limit the search due to project time- constraints. However, we can use the Chapter 2 working list to estimate that at least 1,500 timber tree taxa may be at risk from over-exploitation.

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6.1.2 Chapter 3: Applications of GBIF data in assessing extinction risk of timber trees Knowledge gap:

Use of ‘big data’ such as large, open-access repositories of species distribution records in species range mapping represents an important time-saving resource for

conservation if we are to meet CBD and GSPC 2020 Targets. Record datasets from the Global Biodiversity Information Facility (GBIF) are increasingly being used to this end. However, it is not known whether species distribution records from GBIF are adequate for calculating reliable extent of occurrence (EOO) and area of occupancy (AOO) for timber tree species.

Findings:

Chapter 3 assessed volume, coverage, and reliability of GBIF records for a random subset of 304 timber tree species. It found that, although mean record number was over 4,000 per species, discards after cleaning and range-matching were high, with only 54.2 % of records useable. Record coverage was also higher for species in temperate latitudes and lowest in the tropics. However, results demonstrated that range-matched records from GBIF gave native ranges (at the country level) that were not significantly different to native ranges derived from regional floras or The World List of Threatened Trees (Oldfield et al., 1998).

Implications:

Although the analysis in Chapter 3 confirmed that there are coverage gaps in tropical regions (Cayuela et al., 2009) and that GBIF data have a high discard rate after cleaning, the number of usable records was far higher than that found by Hjarding et al. (2014) for East African amphibians, and that record reliability was sufficient to calculate EOO, though not AOO. Thus, GBIF records were shown to be useful in prioritising timber tree species for full Red List assessment on the basis of range- restriction (EOO <20,000 km2). This Chapter successfully met Objectives 2a, 2b and 2c, thus answering Research Question 2: ‘Are species distribution records from the Global

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Biodiversity Information Facility (GBIF) sufficient for use in calculating timber tree species’ IUCN Red List extent of occurrence (EOO) and area of occupancy (AOO)?’.

6.1.3 Chapter 4: IUCN Red List extinction risk assessments of timber tree species Knowledge gap:

Up-to-date extinction risk status of over 1,500 commercially and ecologically valuable angiosperm timber tree taxa remains unknown, despite ongoing threats of

deforestation and over-exploitation.

Findings:

Chapter 4 prioritised 324 timber tree species on the basis of small EOO (<20,000 km2) and/or previous Threatened or Near Threatened IUCN Red List categorisation. Red List Criteria were then applied to these priority species, under three deforestation

scenarios. Full preliminary extinction risk assessments were produced for all study species, thirty of which had never before been Red Listed at the global scale. The most conservative assessments used Global Forest Change (GFC) deforestation data (Hansen et al., 2013) as a proxy for population reduction. Under this scenario, 222 of the 324 study species (69 %) were considered Threatened, 24 Near Threatened (7 %) and 77 Least Concern (24 %), with one species Data Deficient. Species were predominately assigned final Categories on the basis of Criterion A sub-criterion A3 – future projections of population reduction.

Implications:

The assessments produced in Chapter 4 indicate that if deforestation continues at current rates, within an approximation of three generations (100 years) into the future, the majority of tropical and subtropical angiosperm timbers may qualify for IUCN Red List Threatened Categories. However, Red List assessments typically contain a degree of uncertainty, and use of GBIF and GFC datasets is likely to compound this uncertainty. Thus, Chapter 4 met Objectives 3a and 3b, and laid the groundwork for answering Research Question 3: ‘How many of the world’s wild-harvested, angiosperm

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timber tree species are currently threatened with extinction, according to IUCN Red List Categories and Criteria Version 3.1?’

6.1.4 Chapter 5: Assessing the uncertainty of IUCN Red List categorisations for timber tree species using open-source and expert datasets

Knowledge gap:

Chapter 4 assessed extinction risk of 324 angiosperm timbers by applying IUCN Red List Categories and Criteria. However, the extent of uncertainty around these preliminary assessments is not known.

Findings:

Some changes in species categorisations using expert/alternative datasets versus Chapter 4 datasets indicate that some aspects of Chapter 4 Red List assessments had a high degree of uncertainty – AOO, severe fragmentation, and population declines in the past. However, Bayesian Belief Network outcomes for case study 3 suggested that GBIF data may provide EOO categorisations that are as reliable as EOO categorisations produced using expert records collections and peer-reviewed, published species distribution maps. Criterion A categorisations were shown to be strongly influenced by future projections under both sub-criterion A3 and sub-criterion A2. Additionally, exploitation information on CITES listed timber tree species was found to be insufficient to apply IUCN Red List Categories and Criteria, except in a few cases.

Implications:

The case study results indicate that, although categorisations made with Chapter 4 datasets had varying degrees of uncertainty, EOO values calculated using cleaned and matched GBIF records may be more reliable than other studies suggest (Hjarding et al., 2014). Overall, despite some categorisation uncertainties, Chapter 4 datasets were much more readily-available for many more taxa than expert – and particularly exploitation – datasets for timber trees. Thus, Chapter 5 findings suggest that open-

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access ‘big data’, as used in Chapter 4, still represents a valuable source of readily- accessible information for Red List assessments, though it should be used with caution. Chapter 5 addressed Objectives 4a-4e and Research Question 4: ‘How uncertain are the IUCN Red List categorisations that were made in Chapter 4 using open-source distribution record and deforestation datasets?’