Carlos Manuel Guilherme Lage Teixeira

Address: Environment and Energy Section, Instituto Superior Técnico, Lisboa
Phone: (00351) 21 8419290
C. Vitae:
Specialization: biology
Courses: Dynamic Energy Budget theory

Bird diversity

State of the art

As biodiversity continues to decline worldwide, a growing number of studies and international reports underline the need to halt this process. Among the indentified causes for the current situation, is land-use change. Human activities such as agriculture and urban sprawl may be considered inducers of this kind of change.

Taking these elements into consideration, the need to develop frameworks for understanding and predicting the effect of habitat alteration on biodiversity or assessing the relative vulnerability of species to land-use change, became clear and urgent.

During the last decade and up to the moment, several theoretical frameworks and models have been developed, aiming to predict extinction probabilities and rates after land-use change. These studies focus mainly on habitat alteration whether it corresponds to conversion of native habitat to human-dominated uses, changes in agricultural practices or abandonment. They also usually include demographic models such as species–area relationship (SAR) or population viability analysis (PVA) models. Whereas the former do not discriminate among species, the latter can be applied to species individually but typically demand a large amount of life-history data.

Models used to estimate the maximum population growth rate, such as that developed by Cole, require values of fecundity and mortality rates. Other necessary elements include the age at first breeding and survivorship (proportion of individuals surviving to the beginning of each age class). Fecundity rates require such elements as the number of broods per female, per year (which may require the breeding season length and the length of brood time) and an estimate of female eggs per clutch. The best estimate for mortality rates may result from maximum life span information.

Considering even the most well known animal groups, such as the birds, there is a considerable amount of information lacking for most of the species. This may include a variety of necessary elements, from age at first breeding to maximum life span.

Ideally the necessary data to fill these information gaps would continue to be collected on the field. Considering present less ideal conditions, reliable data estimates are required, in order to fill the gaps and model the effects of land use change, in an optimized amount of time.

It has become quite common to calculate allometric relationships based on the available data for two life-history traits of a group of well known species, in order to estimate equivalent data for another group of less documented species. However, allometric scaling relationships, of an exclusively mathematical nature, assume similarities between species based mostly on body mass. They are highly sensitive to the inclusion or exclusion of particular groups of organisms. One example are bats (Chiroptera), that exhibit some life-history traits typically inconsistent with their small body size (long life spans, small litter sizes, and relatively long litter intervals).


  1. To apply the Dynamic Energy Budget (DEB) Theory in order to provide the estimates lacking for an extensive number of bird species of the western Palearctic ecozone
  2. To perform sensitivity analyses with the estimates previously calculated and considering different groups of bird species using taxonomy, dimensions, behavior and ecological strategies as criteria
  3. To perform an assessment of the land-use change vulnerability of a group of bird species that coexist in a selected region, constituting a study case.


Methodology is organized in five major steps:
  1. Collecting data
  2. Estimating DEB primary parameters
  3. Applying DEB primary, secondary or tertiary scaling relationships
  4. Performing sensitivity analyses for different groups of bird species
  5. Using estimated data for land-use change vulnerability assessment of bird species in a selected region (case study).

1) Collecting data

Life-history data will be collected from available literature for as much species of birds from the Palearctic ecozone, as possible.

2) Estimating DEB primary parameters

Considering that for most bird species of the Palearctic ecozone, the only quantitative data available includes:
  1. Adult body length measurements
  2. Adult weight
  3. Egg length measurements and weight
An analysis of the available life-history elements and corresponding DEB primary parameters will be performed.

Some life-history traits corresponding to primary DEB parameters:
Life-history traits DEB quantities
Adult body length measurements Observed ultimate length (L_inf_w)
Adult weight Observed wet weight at ultimate length (L_inf_w)
Length of brood time (incubation) Age at birth
Age at first breeding Age at puberty
Nests per year/clutch size Mean reproduction rate
When the necessary data is available some calculations may be performed. If we consider a specific density (d_V_w) typically close to 1 g cm-3, we can relate the physical volume (V_w) to wet weight (W_w = d_V_w . V_w). Using the scaling relationships, physical volume and physical ultimate length, we can estimate the shape coefficient, which relates physical length to structural length.

Length at birth and length at puberty require measurements done at the specific ages. However, these are usually not available. A series of compound parameters also depend on these estimates.

The age at birth and age at puberty will possibly be estimated considering the respective maturity thresholds (E^b_H and E^p_H). The calculations necessary in order to estimate these thresholds may not be possible for all the species considered.

3) Applying DEB primary, secondary or tertiary scaling relationships

The application of primary scaling relationships, using the zoom factor z estimated for each considered species, may allow the calculation of estimates for those parameters that are nevertheless still missing, using as reference one or more well known species for each family of birds.

However, in order to estimate the zoom factor, values for maximum length are required (z = L_m_ref / L_m). Estimates for primary parameters such as the surface-area-specific maximum assimilation rate ({p_Am}) can be estimated applying the correct proportionality relationship with the zoom factor.

Making use of the same rationale as for the primary scaling relationships, the application of secondary scaling relationships can be used to estimate necessary life-history traits / parameters such as:

Ultimately, if necessary, the same scaling relationships can be applied in order to estimate body weight (W_w), reserve capacity ([E_m]), the amount of energy at birth (E_b), the maximum ingestion rate, the half saturation coefficient, the maximum growth rate, the von Bertalanffy growth rate, the maximum reproductive rate and time till death by starvation.

The following conditions are to be considered initially:

4) Performing sensitivity analyses for different groups of bird species

After the life-history data tables are completed with the estimates previously calculated, comparative analyses will be performed for different groups of bird species. Groups will be defined according to different criteria, namely taxonomy (Families), physical characteristics (groups of birds of similar size and/or similar physiognomy), behaviour and ecological strategies. Discrepancies will be discussed taking into consideration ecological and evolutionary hypotheses.
5) Using estimated data for land-use change vulnerability assessment of bird species in a selected region (case study)
A region subjected to different potential future land-use scenarios, and with a variety of relevant bird species present, will be selected. Collected and estimated life-history data will be used in order to calculate fecundity and mortality rates. These rates will be used to calculate population growth rates and critical habitat patch areas (using previously published models). These elements will then be used in order to assess the vulnerability of those bird species to different scenarios of land-use change.
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