VARIATIONS IN BIRD ABUNDANCE IN THE TUMBESIAN REGION, PERU
‐ implications for conservation‐
Christian Devenish
Christian
Devenish1,2 & Stuart Marsden
& Stuart Marsden1
Methods
• Bird abundances obtained from Distance sampling line transects at 26 sites on north Peruvian coast, June – October 2013.
• Habitat characteristics, & land use measured at transects.
• 20 focal bird species, most endemic to Tumbesian
region, with restricted ranges. Two threatened.
• Study sites chosen with two criteria:
•
Geographic coverage of species ranges
g p
g
p
g
•
Probability of obtaining records of focal species
• Presence only species distribution models using bird occurrences
The issue
• Data on species abundances, range sizes and populations are scarce, especially in the Neotropics, but these data represent major inputs for conservation planning, such as IUCN extinction risk assessments (e.g. Collar 1996, Rodrigues et al. 2006).
• Obtaining such data is difficult and expensive where field conditions are
are often physically, socially and politically challenging.
often physically socially and politically challenging
• If similar environmental factors govern both distribution and abundance, then distribution modelling may provide shortcuts to obtaining population information.
Study area
• Dry forests of North Peru represent patchy habitat for birds, where river valleys descend from the Andes, often separated by expansive areas of desert scrub.
• All dry forest habitat is used to some degree, mostly for grazing, firewood/ charcoal production.
• Area is part of the Tumbesian region, a global conservation priority (e.g. Stattersfield et al. 1998, Myers 2003)
Myers 2003).
Site 16
Site 3
Site 4
Aims
• To relate bird abundance, obtained from field studies, to modelled probability of occurrence, habitat variables and land use
pressures with a view to improving estimates of species
abundances and ranges.
abundances and ranges.
Site 2
Site 22
Rufous Flycatcher Myiarchus semirufus ‐ Endangered
Photo: Gary Rosenberg
Relationship between abundance and predicted occurrence
Previous work
• Studies to date which have related modelled probability of occurrence, or similar metrics, to field based density estimates are inconclusive. For example:
Species, Location
Abundance methods, effort
Badger Meles meles, Republic of Ireland
Jaguar Panthera onca, South America
Butterfly Parnassius
y
apollo, Spain
White‐tailed deer Odocoileus virginianus, Mexico
Systematic survey ‐presence/ absence of setts (755 km2)
Camera trap capture‐recapture sampling (37, 17 sites)
Counts on transects at random and targeted sites (90, 59 sites)
Systematic sampling ‐ track count & fecal standing crop (14 sites x 2)
Distribution modelling methods
Logistic regression
Outcome
Source
Strong association
Byrne et al. 2014
Tôrres et al. 2012
Gutiérrez et al. 2013
Yañez‐Arenas et al. 2012
11 distribution modelling Weak relationship in 3 / methods
22 models
Logistic regression
g
g
Strong correlation
g
Maxent (& distance to niche centroid) Negative relationship between abundance and distance to niche centroid
‐ Distance to niche centroid and field‐based abundance
• Niche space was constructed using the same occurrence records as the Maxent model (right) with three environmental variables, annual mean temperature, precipitation and NDVI of wet season. • Euclidean distance from niche centroid shows significant correlation with density estimates
significant correlation with density estimates (r = ‐0.642, df = 24, p < 0.001) in example from Aimophila stolzmanni.
Point area is proportional to coefficient of variation of density estimates.
Cinereous Finch Piezorhina cinerea
Photo: Murray Cooper
References
Byrne A.W., Acevedo P., Green S., & O’Keeffe J. (2014) Estimating badger social‐group abundance in the Republic of Ireland using cross‐validated species distribution modelling. Ecological Indicators, 43, 94–102. Collar N.J. (1996) The reasons for Red Data Books. Oryx, 30, 121–130. Gutiérrez D., Harcourt J., Díez S.B., Illán J.G., & Wilson R.J. (2013) Models of presence–absence estimate abundance as well as (or even better than) models of abundance: the case of the butterfly Parnassius apollo. Landscape Ecology, 28, 401–413. Myers N. (2003) Biodiversity hotspots revisited. BioScience, 53, 916–917. Rodrigues A.S.L., Pilgrim J.D., Lamoreux J.F., Hoffmann M., & Brooks T.M. (2006) The value of the IUCN Red List for conservation. Trends in Ecology & Evolution, 21, 71–76. Stattersfield A.J., Crosby M.J., Long A.J., & Wege D.C. (1998) Endemic Bird Areas of the World: Priorities for Biodiversity Conservation. BirdLife International, Cambridge, UK. Tôrres N.M., De Marco P., Santos T., Silveira L., Jácomo de A., T A., & Diniz‐Filho J.A.F. (2012) Can species distribution modelling provide estimates of population densities? A case study with jaguars in the Neotropics. Diversity and Distributions, 18, 615–627. Yañez‐Arenas C., Martínez‐Meyer E., Mandujano S., & Rojas‐Soto O. (2012) Modelling geographic patterns of population density of the white‐tailed deer in central Mexico by implementing ecological niche theory. Oikos, 121, 2081–2089. I’m very grateful to the organizations shown at right for providing funding for fieldwork and participation at this conference. Also to all those who provided data, and support for fieldwork, above all, Elio Nuñez; the members of farming communities who kindly put us up; Nature and Culture International; Dirección
General Forestal y de Fauna Silvestre – Ministerio de Agricultura; Servicio Nacional de Áreas Naturales Protegidas por el Estado, Peru; CONDESAN.
Photo: La Viña , Lambayeque. Study site no. 22 (above)
‐ Maxent models and field based density estimates
• Density estimates were positively correlated with probability of occurrence from the Maxent model in examples from Tumbes Sparrow Aimophila stolzmanni and Superciliated Wren Cantorchilus superciliaris (r = 0.746, df = 24, p < 0.001; r = 0.448, df = 24, p = 0.023 respectively). Coefficient of variation of density estimates is shown by the size of the circles.
[email protected]
1. Manchester Metropolitan University
Division of Biology and Conservation Ecology
School of Science & The Environment
2. Centro de Ornitología y Biodiversidad
Lima, Peru