An estimate of the number of tropical tree species

The high species richness of tropical forests has long been recognized, yet there remains substantial uncertainty regarding the actual number of tropical tree species. Using a pantropical tree inventory database from closed canopy forests, consisting of 657,630 trees belonging to 11,371 species, we use a fitted value of Fisher’s alpha and an approximate pantropical stem total to estimate the minimum number of tropical forest tree species to fall between ∼40,000 and ∼53,000, i.e. at the high end of previous estimates. Contrary to common assumption, the Indo-Pacific region was found to be as species-rich as the Neotropics, with both regions having a minimum of ∼19,000–25,000 tree species. Continental Africa is relatively depauperate with a minimum of ∼4,500–6,000 tree species. Very few species are shared among the African, American, and the Indo-Pacific regions. We provide a methodological framework for estimating species richness in trees that may help refine species richness estimates of tree-dependent taxa

Phylogenetic classification of the world's tropical forests

Knowledge about the biogeographic affinities of the world’s tropical forests helps to better understand regional differences in forest structure, diversity, composition, and dynamics. Such understanding will enable anticipation of region-specific responses to global environmental change. Modern phylogenies, in combination with broad coverage of species inventory data, now allow for global biogeographic analyses that take species evolutionary distance into account. Here we present a classification of the world’s tropical forests based on their phylogenetic similarity. We identify five principal floristic regions and their floristic relationships: (i) Indo-Pacific, (ii) Subtropical, (iii) African, (iv) American, and (v) Dry forests. Our results do not support the traditional neo- versus paleotropical forest division but instead separate the combined American and African forests from their Indo-Pacific counterparts. We also find indications for the existence of a global dry forest region, with representatives in America, Africa, Madagascar, and India. Additionally, a northern-hemisphere Subtropical forest region was identified with representatives in Asia and America, providing support for a link between Asian and American northern-hemisphere forests.</p

Using Risk Assessment and Habitat Suitability Models to Prioritise Invasive Species for Management in a Changing Climate

<div><p>Accounting for climate change in invasive species risk assessments improves our understanding of potential future impacts and enhances our preparedness for the arrival of new non-native species. We combined traditional risk assessment for invasive species with habitat suitability modeling to assess risk to biodiversity based on climate change. We demonstrate our method by assessing the risk for 15 potentially new invasive plant species to Alberta, Canada, an area where climate change is expected to facilitate the poleward expansion of invasive species ranges. Of the 15 species assessed, the three terrestrial invasive plant species that could pose the greatest threat to Alberta’s biodiversity are giant knotweed (<i>Fallopia sachalinensis</i>), tamarisk (<i>Tamarix chinensis</i>), and alkali swainsonpea (<i>Sphaerophysa salsula</i>). We characterise giant knotweed as ‘extremely invasive’, with 21 times the suitable habitat between baseline and future projected climate. Tamarisk is ‘extremely invasive’ with a 64% increase in suitable habitat, and alkali swainsonpea is ‘highly invasive’ with a 21% increase in suitable habitat. Our methodology can be used to predict and prioritise potentially new invasive species for their impact on biodiversity in the context of climate change.</p></div

Woody species abundance (mean ± standard error for each plot category) in five gap plots and four control plots pre- and 24–35 years post-gap formation (Pit und = <i>Pittosporum undulatum</i>).

<p>The statistical test is the difference in the <i>change</i> from pre- to post- enumerations between the gap and the control plots using a two-tailed paired t-test (n.s. indicates no significance;* P≤0.05; *** P<0.001).</p

Quantitative Bray-Curtis similarity indices for stem density, basal area and recovery (mean of stem density and basal area) showing the recovery of four gap plots compared with their controls (plot pairs 1–4) and with their pre-gap composition.

<p>For comparison, the mean similarity index amongst 19 plots in the surrounding forest in 2009 is also shown. Maximum similarity for the spatial comparison was taken as the mean similarity between pre-gap and pre-control plots and, for the temporal comparison, as the mean similarity between the pre-control and post-control plots. The differences in mean quantitative Bray-Curtis similarity index within each of the three panels were computed using paired t-tests between bars labelled with the same letter; where the letter is shown as lower case it indicates a significant difference from where it is upper case, where both are shown as upper case the difference was not significant.</p

Differences in prioritization of new invasive species to Alberta when using risk assessment or habitat suitability modeling alone, compared with combining both risk assessment and habitat suitability modeling.

<p>Differences in prioritization of new invasive species to Alberta when using risk assessment or habitat suitability modeling alone, compared with combining both risk assessment and habitat suitability modeling.</p

Forest recovery determined by spatial and temporal comparisons measured by the Chao-modified Jaccard index, the Horn-Morista index and the Tanner index.

<p>Forest recovery determined by spatial and temporal comparisons measured by the Chao-modified Jaccard index, the Horn-Morista index and the Tanner index.</p

Combining risk assessment and change in suitable high risk habitat between baseline climate and future climate for 15 potentially new invasive species to Alberta.

<p>Combining risk assessment and change in suitable high risk habitat between baseline climate and future climate for 15 potentially new invasive species to Alberta.</p

The ten woody species with the highest importance values (IV) = {(relative density+relative basal area)/2}; stems ≥3 cm dbh in gap and control plots pre- and 24–35 years post-gap formation.

<p>Regeneration type is restricted to a two-class categorization from previous research (based on seedling population distribution and response to disturbance):</p>*<p>species favoured by gap formation,</p>+<p>shade-tolerant species. Alc lat = <i>Alchornea latifolia</i>, Bru com = <i>Brunellia comocladiifolia</i>, Cha glo = <i>Chaetocarpus globosus</i>, Cle occ = <i>Clethra occidentalis</i>, Clu hav = <i>Clusia havetioides</i>, Cyr rac = <i>Cyrilla racemiflora</i>, Eug mon = <i>Eugenia monticola</i>, Eug vir = <i>Eugenia virgultosa</i>, Gua gla = <i>Guarea glabra</i> Vahl., Hae inc = <i>Haenianthus incrassatus</i>, Hed arb = <i>Hedyosmum arborescens</i>, Ile mac = <i>Ilex macfadyenii</i>, Lyo oct = <i>Lyonia octandra</i>, Mic dod = <i>Miconia dodecandra</i>, Myr cor = <i>Myrsine coriacea</i>, Oco pat = <i>Ocotea patens</i> (Sw.) Nees, Pit und = <i>Pittosporum undulatum</i>, Pod urb = <i>Podocarpus urbanii</i> Pilger, Sol pun = <i>Solanum punctulatum</i>.</p