Feedforward Few-shot Species Range Estimation
Authors: Christian Lange
Max Hamilton
Elijah Cole
Alexander Shepard
Samuel Heinrich
Angela Zhu
Subhransu Maji
Grant Van Horn
Oisin Mac Aodha
Venue: ICML 2025 poster (2025)
Keywords: species distribution modeling
SDM
species range modelling
spatial implicit neural representation
SINR
low-shot learning
few-shot learning
Primary area: applications->everything_else
TL;DR: A new low-shot method for estimating the spatial range of species

Abstract: Knowing where a particular species can or cannot be found on Earth is crucial for ecological research and conservation efforts. 
By mapping the spatial ranges of all species, we would obtain deeper insights into how global biodiversity is affected by climate change and habitat loss. However, accurate range estimates are only available for a relatively small proportion of all known species. For the majority of the remaining species, we typically only have a small number of records denoting the spatial locations where they have previously been observed. We outline a new approach for few-shot species range estimation to address the challenge of accurately estimating the range of a species from limited data. During inference, our model takes a set of spatial locations as input, along with optional metadata such as text or an image, and outputs a species encoding that can be used to predict the range of a previously unseen species in a feedforward manner. We evaluate our approach on two challenging benchmarks, where we obtain state-of-the-art range estimation performance, in a fraction of the compute time, compared to recent alternative approaches.

Feedforward Few-shot Species Range Estimation
Christian Lange' Subhransu Maji? Grant Van Horn Oisin Mac Aodha'
Max Hamilton
Elijah Cole Alexander Shepard Samuel Heinrich
Angela Zhu"
Abstract
Inputs
Knowing where particular species can or can- not be found on Earth is crucial for ecological research and conservation efforts. By mapping the spatial ranges of all species, we would ob- tain deeper insights into how global biodiversity is affected by climate change and habitat loss. only avail-
"Observed open forest habitat ... extending South America.
FS-SINR
Observations
Text
Image
However, accurate range estimates able for a relatively small proportion of all known species. For the majority of the remaining species, we typically only have a small number of records denoting the spatial locations where they have previously been observed. We outline a new ap- proach for few-shot species range estimation address the challenge of accurately estimating the range of species from limited data. During infer- ence, our model takes a set of spatial locations as input, along with optional metadata such as text or an image, and outputs a species encoding that can be used to predict the range of previously un- seen species in feedforward manner. We evalu- ate approach two challenging benchmarks, where we obtain state-of-the-an art range estimation performance, in fraction of the compute time. compared to recent alternative approaches.
Outputs
Observations
Observations + Text
Figure I. Few-shot species range estimation with FS-SINR. Our FS-SINR approach is trained on citizen science collected species observation data (i.c., locations where species has been observed), and once trained, estimate the spatial range of previously unseen species with a single forward pass through the model, with retraining required inference time. supports different input modalities such as variable length sequences of location observations, in addition other metadata such as text images. In this illustration, we show two different range predictions: one using only location observations (bottom left) and the other using observations and text (bottom right).
1. Introduction
data is used to train models that can then generate detailed predictions over a spatial region interest (Elith et al., 2006; Beery al., 2021). When sufficient data is available, these models enable practitioners estimate important quanti- ties such the spatial range (i.e., where species be found) or abundance (i.e., the total number of individuals) of a species, in addition to quantifying how these quantities are changing over time.
Understanding the spatial distribution of plant and ani- mal species is essential to mitigate the ongoing decline in global biodiversity (Jetz et al.. 2019). Monitoring these distributions over time allows us to quantify the impacts of climate change, habitat loss, and conservation interven- tions Maantyk--rrnnghe
al., 2012). Estimating species' spatial distribution typically starts with collecting observations that denote the locations where the species has been confirmed to be present or absent. Traditionally, this
of
Despite the availability of well-established modeling tech- niques, our current understanding of species' distributions is extremely limited little or no observational data is available for most species. For example, iNaturalist, one of the largest citizen science platform documenting global biodiversity, has collected over 130 million research qual- ity observations for approximately 373,000 species glob- ally (iNaturalist, 2025). However, the data is severely long- tailed, i.e., a small percentage of common species account
University of Edinburgh *UMsss Amherst 'GeaBio AI "Naturllsst *Comell. Correspondence to: Christian Lange <c.p.lange@ ac.uk..
Proceedings of the ndnd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Feedforward Few-shot Species Range Estimation
for the majority of the observations, while many species have very few observations. In fact, over half of the 373,000 species cataloged by iNaturalist have been observed fewer than ten times to date. This data limitation is amplified by the fact that the vast majority of the several million species that are thought to exist have not yet even been documented by science (Mora et al., 2011), Identifying locations where under-observed species can be found a time- consumnnunn and laborious process, often requiring long expeditions to remote locations to search for species that are hard to find. Consequently, there is pressing need for computational methods that can reliably estimate the spatial distributions of species using only a small number of observations.
2. Related Work
Species Distribution Modeling. Estimating the spatial dis- tribution of a species is a widely explored topic in both statistical ecology and machine learning (Beery al., 2021). The goal is to develop models that can predict the distribu- tion of species over space, and possibly time, given sparse observation data. Different machine learning approaches, initially using traditional techniques, such as decision trees among others have been extensively explored, e.g., (Phillips et al., 2004; Elith et al., 2006). More recently, deep learning- based methods have been introduced (Botella al., 201 8; Mac Aodha et al., 2019; Cole et al., 2023; Kellenberger et al., 2024). One of the strengths of these deep methods is that they can jointly represent thousands of different species within the same model and have been shown to improve as more training data is added, even when the data is from di lifferent species (Cole et al., 2023).
Knowing the range of one species can help predict the range of another due shared ecological, environmental, and geo- graphic contexts. Recent advances in range estimation, such as Spatial Implicit Neural Representations (SINR), have leveraged this idea by training on millions of observations, across tens of thousands of species, inside one model (Cole et al., 2023). However, these models still rely on relatively large numbers of training observations for individual species, which limits their applicability to species with limited obser- vations. In this work, we introduce Few-shot Spatial Implicit Neural Representations (FS-SINR), novel Transformer- based model that overcomes this limitation and offers two key advantages over previous approaches. First, we obtain improved performance in the few-shot regime, a scenario that represents the reality for the majority of species, yet remains underexplored in prior work. Second, we make accurate predictions for species not present in the training set without any additional training, which can enable inter- active exploration and modeling. At inference time, we only require a set of observed locations for the unseen species to generate reliable range estimates. Furthermore, we show we can flexibly incorporate additional non-geographic context information (e.g.. a text summary of the species' habitat or range preferences or an image of the species) further im- prove prediction quality. Figure 1 illustrates how FS-SINR can be used at inference time.
There has also been work investigating different approaches to address some of the challenges associated with training and evaluating these models. Examples include attempts to address imbalances across species in the training ob- servation data (Zbinden et al., 2024b), sampling pseudo- absence data (Zbinden et al., 2024a), biases in training locations (Chen & Gomes, 2019), representing location information (RuBwurm al., 2024), discretizing continu- ous model predictions (Dorm et al., 2024), active learning approaches (Lange et al., 2023), using additional metadata such as images (Teng et al., 2023; Dollinger et al., 2024; Picek et al., 2024) or text (Sastry et al., 2023; 2025; Hamil- ton et al., 2024), and designing new evaluation datasets to benchmark performance (Cole et al., 2023; Picek et al., 2024). In our work, we investigate the underexplored few- shot setting, where only limited observations (e.g., fewer than ten) are available for each species at training time.
Few-shot Species Range Estimation. There are several aspects of the species range estimation task in the low-data regime that make it different from other few-shot problems more commonly explored in the literature (Parnami & Lee, 2022; Wang et al., 2020). For example, the input domain is fixed (i.e., all locations earth), each location can support more than one species (i.c., multi-label instead of multi- class), the label space is much larger (i.e., tens of thousands of species as opposed to hundreds of classes in image clas- sification), and only partial supervision is available (c.g.. presence-only data, with no confirmed absences).
In summary, we make the following contributions: (i) We introduce FS-SINR, a new approach for few-shot species range estimation. FS-SINR has novel capabilities, including the ability to predict the spatial range of a previously unseen species at inference time without requiring any retraining. (ii) We demonstrate that FS-SINR achieves state-of-the the art performance in the few-shot setting on the challenging IUCN and S&T benchmark datasets. (iii) We provide de- tailed ablation studies and visualizations to highlight the benefits of integrating observational data with textual and visual context, as well as to compare our approach with alternative methods.
Lange et al. (2023) introduced an active learning- based approach for species range estimation which makes pre- dictions based on linear combinations of learned species embeddings and showed its effectiveness in the few-shot regime. LE-SINR (Hamilton et al., 2024) showed that inter- net sourced free-form text descriptions of species" ranges can be used when training models for zero-shot range esti-
2

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Feedforward Few-shot Species Range Estimation
Output: Probability
(lat,.lon)
LDG
Presence of Query
Context
Locations
Transformer
C (lat.. lon
[(
1,0Gs
Latitude
X
c - Clat,. lon.)
LOG&
Optional Metadala
(-rtii specics.>)
10
TXT
Longitude
IMG
Query Location
GLS
CLS
(lat, Jon):
Transformer
Location Encoder
Text Encoder
Image Encoder
Species Decoder
Embedding
C Kinbedding Type
Figure 2. FS-SINR overview.
Here we depict our few-shot species range estimation model. The input consists of an arbitrary number of context locations C' for target species that are each independently tokenized using location encoder fe(), and optional auxiliary context information like text or an image. A class token (CLS) is also appended to the input. All input tokens are processed by Transformer my (). Given the of input context locations, the location encoder's embedding of x with the projected embodding of the CLS token which is output from the species decoder.
estimate the probability that species is present query location by multiplying
mation. They applied their approach to the few-shot setting, but requires retraining a classifier for each new species observation added. In our evaluation, we demonstrate that our FS-SINR approach, which can incorporate additional metadata training time and does not require retraining during inference, outperforms existing methods.
locations of interest by evaluating the model for all locations (i.e., x: E X)).
One of the main challenges associated with training mod- els for species range estimation is that there is dramatic asymmetry in the available training data. Specifically, it is much easier to collect presence observations (i.e., confirmed sightings of a species) compared to absence observations (i.c., confirmation that species is not present at a specific location). As a result, many methods have been developed to train models using presence-only data. the presence- only setting, we have access to training pairs (xx, 2). where x: is geographic location, and 2 E {1, ...,...6. 8) is integer indicating which species was observed there. To overcome the lack of confirmed absence data, one common approach is to generate pseudo-absences by sampling random loca- tions on the surface of the earth (Phillips et al., 2009). Given these pscudo-absences the parameters of g() can be trained in an end-to-end manner using variants of the cross-entropy loss. Specifically, we use full assume negative loss from Cole et al. (2023) to train the SINR baseline:
3. Methods
We first set up the species range estimation problem and then describe our approach for few-shot range estimation.
3.1. Species Range Estimation
We start by describing the SINR approach from Cole et al. (2023). Let æ = (lat, lon) E X be a location of interest sampled from spatial domain X' (e.g.. a location on earth). Our goal is train a model 9() X' -> [o, 1] to predict the probabilities of a different species of interest occurring at æ.
We let ý g(x),, where ùU E [0, 1] (i.e., the jth entry of i) represents the probability that species j occurs at location x.
We can decompose the model as 9() = h)() fe(), where so() : X' - R..d is a location encoder with parameters O and h0()= _d j0, 1]3 is multi-label classifier with pa- rameters Φb The location encoder fe() maps location to d-dimensional latent embedding fz( The multi-label classifier h() is implemented as a per-species linear pro- jection followed by an element-wise sigmoid non-linearity, meaning that ý = ((f((z) W) where W E R (i.c., h)() = Φ W) and o)) is the sigmoid function. Thus, each column vector w, of W can be viewed as a species embedding, which we can combine with location embed- ding fe(x) via an inner product to compute the probability that the species j is present at œ. Importantly, the location embedding is shared across all species. Once trained, it is possible to generate a prediction for given species for all
LAN (0. 2) 1
11 |-= Alog(ū;)
1 | log(1 - 11)) + log(1 - 17)1, (I)
where is the index of the species present for a given train- ing instance, ÚJ is the predicted probability of the presence of species j. 5/ is the model prediction for a randomly sam- pled pseudo-absence location, and the hyperparameter X balances the presence and pseudo-absence loss terms.
3.2. Few-shot Range Estimation
For the SINR model to make predictions for a new species, it necessary to learn a new embedding vector wy for that
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Feedforward Few-shot Species Range Estimation
species. If additional location data is later observed for that
include all species in the loss, and instead consider only those within the same batch of training examples of size s². We obtain a predicted species embedding vector for given species during the forward pass which can be used to estimate the prohabilities of presence of that species for all locations sampled in the batch. We denote this new loss as CAN- which indicates that we are considering only those elements contained within the current batch b:
species, the model must be updated again. However, the number of observations for rarer species can be limited and thus it is necessary to have methods that can be updated efficiently with less training data.
We address this challenge by proposing a new approach for sew-shot species range estimation called FS-SINR. Our model can predict the probability of presence for a previ- ously unobserved species directly at inference time given only the set of confirmed presence locations available, with- out any retraining or parameter updates. At inference time, we assume we have access to a set of context lo- cations C* = fcl.....,ca ]. which represent a set of loca- tions where the species j has been confirmed to be present. Each entry in this set denotes a geographic location, i.c.,
1 56 5=1
(AN-Fu--(0.2
(1
[ log(1 9,) + +og(11 - -1)]- (2)
3.2.1. ADDITIONAL CONTEXT INFORMATION
(lat, lon). Like SINR, our model is also conditioned on location x of interest (i.e., the 'query' location), but uses the context locations to inform the prediction for the query location. Note, the context locations can come from a species not previously observed during training.
The design of FS-SINR is flexible in that we can also pro- vide additional context information to the model if it is available. For example, if there is additional text (e.g.. a range description) or visual (i.e., images) information avail- able for a novel species, it can be added to the context, assuming that such information was also available at train- ing time for other species. This observation is inspired by recent work that also uses language-derived information to improve range predictions (Sastry et al., 2023; Hamilton et al., 2024) and work that uses species images and obser- vations (Sastry et al., 2025). This additional information can provide a rich source of metadata encoding aspects of species' habitat preferences, even when there might only be limited number of location observations available for it. We can represent the expanded contextual input tokens as {ty, aj. fe(ci).... fo (cx)}, where ty denotes a fixed- length text embedding from a large language model and aj an image embedding obtained from a pre-trained vision model for species j - see Figure 2. Note that we train FS- SINR so that it can use arbitrary subsets, including none, of these input tokens during inference.
We represent our model as g(x) (fo()) C*). Un- like in SINR, where the classifier head (xp() simple multi-label classifier and sigmoid non-linearity, in our case, the 'head' of the model my () is a Transformer- based en- coder ( (Vaswani et al., 2017). FS-SINI takes an unordered set of context locations C as input, where each location is encoded into an embedding vector (i.e., a token) via a SINR- style multi-layer perceptron location encoder- - see Figure 2 for an illustration. Importantly, our model can accept a vari- able number of context locations and is invariant to their ordering as we do not append any positional embeddings. This flexibility ensures that it can process a variable number of context locations during inference. We also append an additional register token (REG) as in Darcet et al. (2024) to provide the model with an additional token to 'store' infor- mation. Given that the input sequence is unordered and may or may not include additional context information, we add learned 'embedding type' vectors to each token such that the Transformer knows if a given input token is location, register, text, image, etc.
4. Experiments
Here we evaluate FS-SINR on the task of species range estimation and compare to alternative methods.
We represent the species embedding vector (i.e., wy in SINR) as the class token CLS of the Transformer after pass- ing through a small species decoder MLP s(). To make simply compute the inner product
4.1. Implementation Details
final prediction, between the location embedding of the query location æ and the species embedding vector, and pass it through a sigmoid. Our approach is computationally efficient in that once the species embedding is generated it can then be efficiently multiplied by the embeddings for all locations of interest to generate a prediction for species' range.
Architecture. Our location encoders use the same fully con- nected neural network with residual connections as in Cole et al. (2023). Each of the context locations is processed by the same shared location encoder which first pre-trained as in SINR after which the multi-label classifier head is discarded. Importantly, this pre-trained encoder is only trained on species from the training set, and does not observe any data from the evaluation species during training. The text embedding backbone is a frozen GritLM (Muennighoff et al., 2025) and the default image embedding backbone
FS-SINR uses similar training loss to LAN-full- How- ever, since it has no equivalent to h)() we cannot easily
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Feedforward Few-shot Species Range Estimation
frozen EVA-02 ViT (Fang et al., 2024) pre-trained on the iaaturalist species image classification dataset (Van Horn et al., 2021). Both backbones provide a fixed length em- bedding vector, and we train two-layer fully connected text and image encoders to transform these embeddings into their context tokens. FS-SINR's Transformer contains four encoder layers and the parameters are updated jointly with the location, text, and image encoders and species decoder during training. In total, FS-SINR has 8.2M learnable pa- rameters compared to 11.9M for SINR. This reduction is due to the fact that we do not have to learn a per-species embedding vector as in SINR. We train with a batch size of 2,048 instances and randomly drop-out text/image or lo- cation tokens during training with a probability of 0.5 and 0.1 respectively to enhance robustmess. See Appendix C.1 for more details. Code for FS-SINR is available at: https: //github. com/ Chr//Chriiii Chrrsshangeeeeeeecc wawaaaaannaaaaaaaeeeeeeeaeassr sinr
Baselines. Generating a species' range from FS-SINR for held-out species inference time only requires a single
forward pass through the model to obtain an embedding vector for the species. Current methods (e.g., LE-SINR or SINR) cannot be used in such a feedforward manner and need to be retrained for each species that was not observed at training time. To obtain an equivalent embedding for the SINR and LE-SINR baselines we train a per-species binary logistic regression classifier using any few-shot pres- ence observations that are available, in addition to adding 10,000 uniformly random and 10,000 target (i.e., in locations where species are) pseudo-absences as in LE-SINR. We also compare to the species embedding combination method from Lange et al. (2023) and a Prototypical Network-style baseline (Snell et al., 2017), denoted Active SINR and Pro- totype SINR, respectively. These baselines do not require retraining. For fairness, vations across each method, and the larger number of pres- ences are supersets of the smaller ones. Implementation de- tails of the baseline methods can be found in Appendix C.2.
use the same presence obser-
Data. We train FS-SINR on the presence-only dataset from Cole et al. (2023), which comprises 35.5 million citizen-science records- annotated with latitude, lon- gitude, and species label for 47,375 diverse species in- cluding plants, fungi, and animals from the iNaturalist plat- form (iNaturalist, 2025). We also leverage 127 thousand text descriptions of these species used in Hamilton et al. (2024) and 200 thousand images obtained from iNaturalist (iNatu- ralist, 2025). The text provided during training is composed of sections of Wikipedia (Wikipedia, 2025) articles of the target species. During training, we supply FS-SINR with 20 context locations per training example, although we find that model performance is robust to changes in the number of context locations provided during training.
4.2. Few-shot Evaluation
First, we evaluate how effective different range estimation models are at few-shot range estimation. The goal for each model is to generate a plausible prediction for a previously unseen species' range given limited location observations. Quantitative results are presented in Figure 3, and additional results can be found in Appendix A.
The SINR baseline performs poorly in the low-data regime, but as more data is added performance improves. As noted carlier, here a per-species embedding vector is learned using logistic regression using the provided presence locations and generated pseudo- absences. The recently introduced LEE- SINR approach extends the basic SINR model to use text information (here range text), when available, at inference time. LE-SINR tends to outperform SINR, particularly when text data is available. Like our FS-SINR approach, neither the Active SINR or Prototype SINR baselines require retraining at inference time, but perform much worse than FS- FSNR.
We evaluate models using the IUCN and S&T datasets also from Cole et al. (2023), which contain expert and model- derived range maps for 2,418 and 535 different species, respectively. The IUCN dataset is more globally distributed and contains larger variation in range size and more diverse animal species, while the S&T dataset only contains bird species that are found primarily, but not always, in North America and have a larger average range size. We follow the same preprocessing steps for these datasets as in Cole et al. (2023). While not perfect, these datasets represent the best evaluation data currently available and contain large variety in terms of range sizes and locations. The text used during evaluation consists of pre-trained large language model generated summaries of the range habitat of the target species as used in Hamilton et al. (2024). Importantly, we hold out any species from the union of these two datasets from the training set so that species from the evaluation set are not observed during training. As a result, by default, FS- SINR is trained on data from 44,422 species. Performance reported as mean average precision (MAP) for different numbers of input (i.c., context) locations.
In all instances, when the same metadata is available, FS- SINR outperforms existing methods. Furthermore, we also outperform SINR in the larger data regime (i.e., when 50 observations are available). In general. we observe that im- age information is not as informative as text and does not help on average outside of the zero-shot case, A range text description provides much more context than an image of previously unseen species. However, range estimates for some species benefit significantly from using images, but other species actually see no change or decreased perfor- mance. Making use of images to improve zero-shot range estimates is rewarded during training, but this can harm
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Feedforward Few-shot Species Range Estimation
IUCN
S&T
0.80
0.70
0.65
0.65
MAP
MAP
0.60
0.60
SINR
Prototype SINR
0.55
0.55
Active SINR
JE-SINA (No Text)
0.50
0.50
SINDINN (Test)
PS SINR (No Test/lmage)
FS- SIND (Image)
0.45
0.45
FS SINR (image + Text)
SEN-SINRR (Text)
0.40
50 0.40
10
10
50
Context. Locations
Context Locations
Figure 3. Few-shot results. Here we evaluate different models on the task of species range estimation on the IUCN (left) and S&T (right) datasets. On the x-axis we vary the number of context locations seen inference time for the held-out evaluation species. The y-axis represents Mean Average Precision (MAP), where higher values are better. The error bars display the standard deviation of three different runs. Our FS SINR approach outperforms existing methods, especially the very low-data setting (i.e., < five context locations). Note that LE-SINR and SINR need to be retrained during evaluation when more observations are provided. Tables A3 and A4 report expanded results including larger numbers of context locations.
performance for some species at inference time by biasing the ranges produced toward the rough estimates made using images, which provide limited information. Importantly, unlike SINR and LE-SINR, FS-SINR does not need to be retrained at inference time. Instead, it can make predic- tions in a feedforward manner irrespective of the context data available. This is advantageous in interactive settings, whereby the model can compute the location embeddings for all query locations on earth once, and then a user could experiment by adding different context information interac- tively. Removing the retraining step also allows FS-SINR to produce estimated ranges in a fraction of the compute time compared to other approaches. Compared to the publicly released implementation of LE-SINR on the same hardware, FS- FS-SINR generates range estimates from one context loca- tion and text for all species the IUCN and S&T datasets in 2% of the time on CPU, and 6% of the time on GPU.
predictions. We observe dramatically different predicted ranges when the text prompt encourages the model focus different habitat types. We note that each of the predicted ranges is still consistent with the location of the single con- text location provided. Finally, in Figure 6 we compare FS-SINR range predictions to other approaches, namely SINR, LS- LS-SINR, and Active SINR. We see that for this species FS- more closely resembles the expert range when only three context locations are provided. Additional qualitative examples are provided in Appendix E.
4.3. Zero-shot Evaluation
In addition to being able to generate range predictions in the few-shot setting when limited location observations are pro- vided, FS-SINR can also make predictions when no location information is provided but only additional metadata such as an image or text describing a previously unseen species is given, i.e., the zero-shot setting. These zero-shot results are presented in Table 1 for both the IUCN and S&T datasets.
We present qualitative results for three different species in Figure 7 where we visualize FS-SINR's predictions as we change the number of context locations. Given only a single context location, the model does a sensible job of localizing the species on Earth. This supports the findings from Fig- ure 3 where we observe strong performance even when only one context location is available. When more information is provided, the predicted range more closely resembles the expert-derived range shown in the first row. However, we do note that the model can still make mistakes in our low data setting, such as the erroneous predictions for the 'Black and White Warbler' South America. In Figure 4 we illus- trate some examples of how text information, when paired with one single context location, can influence the model
We report results for several variants of FS-SINR where different types of metadata are used. As baseline, we also present the performance of SINR (row 1) where the evalua- tion species are part of its training set i.e., not zero-shot. We can also add data from these species to the training set of our approach which unsurprisingly boosts performance (i.e., row vs. 9), though unlike SINR, FS-SINR does not have weights associated with individual species and so the impact of seeing evaluation species during training is fairly small, As trivial baseline, we also report performance of FS- SINR (row 4) when no location text metadata is provided, i.e.. this is simply the output of the class token. As expected,
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Feedforward Few-shot Species Range Estimation
[No Text]
"desert"
"rainforest"
"high altitude mountains"
Figure 4. Controlling range predictions using single context location with different text. Given the same single context location, denoted as 'o", FS-SINN can generate significantly different range predictions depending on the text provided. This example illustrates a use case where user may have limited observations some additional knowledge that can be encoded text regarding the type habitat species of interest could be found in. Note, while 'no text' and 'rainforest" look similar, they are actually subtly different.
Spotted-talled Quoll
Ashy Prinia
Eastern Small-eyed Snake
Fukian Gold-striped Pond Frog
Angkor Wat
Ruaha Tanzania
Figure Range predictions with single context image as input. We can condition FS SINR on arbitrary input image with no context locations text, e.g., a held-out species (top row and bottom left), famous landmarks (bottom middle), or landscape images (bottom right).
this model performs poorly, but interestingly seems to have learned some spatial prior that results in non-trivial predictions on S&T which contains bird species mostly con- centrated in North America. We also compare to version of FS-SINR (row 5) where we use taxonomic text (TRT) as in LD-SDM (Sastry et al., 2023) (see Appendix A.2 for further details).
amples in Figure A24), zero-shot image predictions can be sensible, but predicting an unobserved species' range from single input image is ill posed. Text descriptions of range habitat preferences simply much more informative than a single image.
4.4. Additional Results and Ablations
In all instances, our FS-SINR approach outperforms LE- SINR, even when both models are provided with the same information at inference and training time (i.e., row vs. 7 or row 8 vs. 9). Confirming observations from LE-SINR, we see that range text (RT) is more informative than habitat text (HT) (i.e., row 7 vs. 9). Additionally, image information provides some non-trive non-trivial signal (i.c., row 4 vs. 10), but it is not as informative as (i.e., row vs. 10), and can nega- tively impact performance when more informative sources provided (i.e., row 9 vs. 11 for the harder IUCN dataset), as the model may overfit to incorrect spurious features in the image. As we can see in Figure 5 (with additional ex-
In Appendix A we provide additional experimental results for FS-SINR. There we investigate uncertainty quantifica- tion to see how well calibrated the model predictions are. We also report results using a 'distance- metrie which penalizes errors more the further they are in distance away from the actual range. This metric more closely aligns human judgment of predicted range quality.
MAP'
We provide more ecologically relevant breakdown of results in Appendix B, where we find multiple potential sources of bias in our training data toward North America and Europe, and report higher evaluation performance in

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Feedforward Few-shot Species Range Estimation
FS-SINR (Ours)
SINR
LF
Active SINR
Figure 6. Qualitative comparison of range predictions for different methods. (Top) Predicted ranges from a single context location denoted as 'o" and additional metadata for the Gabar Goshawk. (Bottom) Predicted ranges from three context locations for the Monarch. From left to right, FS-SINR (ours) with expert range inset, SINR, LE-SINR, and Active SINR. Please zoom see details.
B
Table 1. Zero-shot results. We report zero-shot performance where no location information is provided to each model, only additional metadata, comparing to SINR (Cole et al., 2023) and LE-SINR (Hamilton et al.. 2024). We denote additional metadata used by models as KT for 'Range Text', HT for 'Habitat Text', and for 'Image.. TST represents 'Test Species in Train', indicating that model uses location observations for the evaluation species at training time (e.g.. SINR which provides an upper bound on per- formance), unlike other models where these species are excluded. TRT models are trained using "Taxonomic Rank Text' as in Sastry al. (2023), which are also provided with the full taxonomic description from 'class' to 'species' during evaluation. Results are presented MAP, where higher is better.
data used, and explore architectural modifications such as removing the final species decoder that operates on the output of the Transformer. We observe that FS-SINR is robust to these changes, justifying its design decisions.
5. Limitations
While FS-SINR outperforms other zero and few-shot ap- proaches for species range estimation, there are some limita- tions. First, given a set of input context locations FS-SINR is deterministic in that it will always generate the same out- put range map. In practice, in the few-shot regime, the same set of points could actually be representative of many differ- ent possible range maps. An obvious extension of our work is to introduce stochasticity into the model outputs, e.g., by treating class token output from the Transformer as a latent embedding for an additional sampling step. In Figure A25 observe that initializing FS- FS-SINR with different random seeds during training results in diverse range predictions across the different models, and in Appendix A we show that this can be exploited to give estimates of the uncertainty of predictions when using an ensemble of FS-SINR models. We leave further exploration of this for future work. Sec- ond, inference time, users may wish to provide example locations indicating where specific species has not been found, i.e., confirmed absences. Currently, our model is trained using presence-only data but could be adapted to use absence information, if available, which could be denoted via a different embedding type vector, to be learned dur- ing training alongside our existing token type embeddings. However, obtaining reliable large-scale absence data for tens of thousands of species is challenging problem.
Method
Variant
IUCN
S&T
SINR
TST
0.67
0.77
FS-SINR
HT. TST
0.38
0.59
FS-SINR
RT. TST
0.55
0,67
FS-SINR
0.05
0.18
FS-SINR TRT
0.21
0.34
LE-SINR HT
0.28
0.52
7
FS-SINR HT
0.33
0.53
LE-SINR RT
0.48
0.60
FS-SINR
RT
0.52
0.64
10
FS-SINR
I
0.19
0.38
11
FS-SINR
1+ RT
0.46
0.64
these regions for FS-SINR and LE-SINR. Similarly, see that biases in the text data potentially leads to increased per- formance for charismatic and well-studied mammals com- pared to other taxonomic groups, though providing more context locations reduces this gap. FS-SINR is somewhat robust to these biases and outperforms other approaches across almost all categories. We also find that for all ap- proaches tested, estimating very small ranges is difficult and performance varies strongly with range sizes, though FS-SINR again shows comparatively good performance.
Finally, biodiversity data and particularly global-scale citi- zen science datasets like the one we use to train FS- SINR can contain large biases (Geldmann et al., 2016; Hughes et al., 2021), e.g., location, temporal, and taxonomic bi- ases, among others. We do not explicitly account for these biases during training, though we make some attempt to
We provide additional ablation experiments for FS-SINR in Appendix D. where we evaluate the impact of different input features, location encoders, and the amount of training
8

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Feedforward Few-shot Species Range Estimation
Common Kingfisher
European Robin
Black and White Warbler
Figure 7. Few-shot range estimation with increasing context locations. Here illustrate few-shot range predictions from FS-SINR given an increasing number of context locations {0, 1, 2, 5, 10} and no other context information for the Common Kingfi isher (left), European Robin (center), and Black and White Warbler (right). the first row, we show the expert-derived range inset and the prediction for the model when context locations are provided (which is the same for all species). Then, in the remaining rows we increase the number of context locations, denoted as 'o". Please zoom to see the context locations. we increase the number context locations, the predictions become closer to the expert ranges.
evaluate the impact of them in Appendix B, and thus we would caution the use of the predictions of our model in any applications that would use our range predictions in the context of biodiversity assessments. However, we note that outperform existing and recent state-of-the-a range estimation methods, especially in the low observation data setting, and do not require any retraining at inference time.
manner to efficiently make plausible range predictions for previously unobserved species. Our quantitative analysis, using expert-derived range maps, shows 5-10% perfor- mance improvement compared to current approaches in the few-shot setting, i.e., when the number of observations equals ten, for previously unseen species. In addition, we also outperform existing methods in the zero-shot setting. While our results are promising, they also indicate that there are many open challenges in this important task.
6. Conclusion
Acknowledgements. We thank the iNaturalist community for making the species observation data available. OMA was supported by Royal Society Research Grant. MH and SM were supported by NSF grants 2329927 and 2406687.
The scientific community has limited knowledge on the ge- ographical distributions of the majority of species Earth. This lack of understanding is further hampered by the fact that we also have insufficient data to train models to esti- mate their ranges. To address this problem, we introduced FS-SINR, a new approach for few-shot species range esti- mation. We demonstrated that FS-SINR is able fuse data from different modalities inference time in a feedforward
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Feedforward Few-shot Species Range Estimation
Impact Statement
Dietterich, T. G. Ensemble methods in machine learning.
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Given the limited observations available for most species, there is a great need for reliable machine-learning based solutions for estimating their ranges. Such methods would provide us with unprecedented insight into how biodiver- sity is distributed on our planet and how it is changing over time. However, there are potential negative consequences associated with inaccurate range predictions generated by automated methods, c.g., a downstream conservation de- cision could be made based on an erroneous range map, resulting wasted resources. Thus, it important for prac- titioners to scrutinize the outputs of models such as ours.
Dollinger, J., Brun, P., Sainte Fare Garnot, V., and Weg- ner, J. D. Sat-sinr: High-resolution species distribution models through satellite imagery. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Informa- tion Sciences, 2024.
Dorm, F., Lange, C., Loarie, S., and Mac Aodha, O. Gener- ating Binary Species Range Maps. In Computer Vision for Ecology Workshop at ECCV, 2024.
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Fang. Y., Sun, Q.. Wang, X., Huang, T., Wang, X., and Cao, Y. Eva-02: A visual representation for neon genesis. Image and Vision Computing, 2024.
approach integrates predictions from pre-trained large language models. These models are known to be biased and capable of hallucinating and fabricating outputs. Spatially localizing the outputs of such models runs the risk of amplifying such biases if used inappropriately.
Fick, S. E. and Hijmans, R. J. Worldelim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 2017.
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Appendix
In this appendix we provide additional quantitative and qualitative results, analysis, implementation details, and ablations.
In Appendix A, we provide additional results for uncertainty quantification, use of taxonomic rank text, a 'distance weighted MAP metric, and show expanded results from Figure 3 for the non-low-shot setting. In Appendix B, we perform an ecologically relevant analysis of our results, showing how performance varies with region, range size, and taxonomic group. In Appendix C, we provide details on the implementation of FS-SINK and the baseline approaches, and of the training and evaluation procedure. In Appendix D, we provide additional ablations of FS-SINR, investigating the impact of training data, different input features, and modifications to the architecture. Finally, in Appendix E, we show additional qualitative results, including visualizing zero-shot and few-shot ranges for species and non-species concepts, and comparisons to ranges produced by LE-SINR and SINR approaches.
A. Additional Quantitative Results
A.1. Uncertainty Quantification
Here, we report results for an ensemble based on FS-SINR and quantify the uncertainty in the ensemble's predictions using methods adapted from Poggi et al. (2020). We create an ensemble by averaging the predictions of three FS-SINR models trained with different random seeds. Figure A25 shows range estimates from three such models, where we can see that each model can produce significantly different outputs. We take the average of these models as the ensemble prediction and treat the variance between individual model predictions as estimate of the uncertainty of the ensemble. If all models agree that species is either present absent at location, then the uncertainty will be low, while if models have different predictions, then the uncertainty will be high. in Table AI, comparing 'Ensemble' MAP to 'Model' MAP (repeated from FS- 'Text' MAP in Table A4 for convenience), we can see that creating an ensemble increases MAP by 0.01 to 0.02, agreeing with typical findings that ensembling can improve performance relative to individual models (Dietterich, 2000).
In order to quantify uncertainty of our ensemble we follow an approach used in Poggi et al. (2020). We iteratively remove locations from the evaluation dataset that have the highest estimated uncertainty and recalculate the MAP without these locations. In our case we remove 2% of the data at each step. If the ensemble uncertainty aligns with how likely it is to be incorrect, then the MAP each step will increase the data with higher estimated uncertainty is removed from the evaluation. We can then plot the MAP against the fraction of data used for the evaluation. Taking the area under the curve generated doing this gives us the estimated "Sparsification Error AUC' (SEAUC). If, instead, we remove 2% of locations randomly at each step then the MAP for each evaluation will remain approximately equal to the MAP when using the entire evaluation dataset. The 'random' SEAUC in this case is effectively equal numerically to the MAP using all evaluation data and can be estimated as such. Taking the difference between the estimated SEARC and the MAP then gives us the 'Area Under the Random Gain' (AURG). A positive AURG shows that the ensemble's estimate of how uncertain its predictions are is better than random guessing. In Table A1 we see that the AURG is positive for all number of context locations and increases more context locations are provided, showing that FS-SINR ensembles can provide useful estimate of how certain they are about a prediction and that providing more context locations allows the ensemble to be more accurate in this uncertainty estimate.
In Figure Al we visualize the ranges (means) and uncertainties (variances) for our FS-SINR ensemble for the felted Green P Pigeon, using 'Range' text, 'Habitat' text, or a single context location. We observe that the mean is high in the region of the expert-derived range, and lower in areas far from the range where single model has erroneously predicted presence. The variance is generally lower in the region of the expert-derived range where all models agree, higher at the edges of this region where different models have different estimates of the extent of the range, and high in areas far from the true range where single models have incorrectly predicted presence, such as South America when 'Habitat' text is provided, or parts of Africa when 'Range" text is provided.
A.2. Taxonomic Understanding
Here, we investigate the impact of providing FS-SINR with an understanding of the species taxonomy. For this we provide 'Taxonomic Rank Text' (TRT) instead of the Wikipedia-based free-form descriptions of a species that are used for our standard FS-SINR approach. This text gives the taxonomy of the species in decreasing taxonomic rank, in the form
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Feedforward Few-shot Species Range Estimation
Range (Mean)
Uncertainty (Variance)
Range
Habitat
No Text
Figure AI. Range estimates and visualized uncertainty estimates for FS-SINR ensemble. We display range estimates and uncertain- ties for the Yell Fellow-food llw-footed Green P gigeon from an ensemble of three FS-SINI models. Zero-shot estimates are based on 'Range' text (top) and "Habitat" text (middle). A few-shot estimate using no and single context location (bottom) is also shown. Range estimates for the ensemble (left) are mean average of individual model predictions, while uncertainties (right) are estimated using the variances of model predictions. The uncertainty lower the region the expert-derived range where models agree, higher at the edges of this region where different models have different estimates of the extent of the range, and high in areas far from the true range where single models have incorrectly predicted presence. Range Text: "The yellow-footed green pigeon is found in the Indian subcontinent and parts of Southeast Asia. It is the state bird of Maharashtra."
Habitat Text: "The species is habitat generalist, preferring dense forest areas with emergent especially Banyan trees, but also be spotted in natural remnants in urban areas. They forage in flocks and are often seen sunning on the tops of trees in the early morning."
'class order family genus species', so for a dog we would give the text 'Mammalia Carnivora Canidae Canis F Famil 1 iarii'.. During training, we select a rank uniformly at random and remove all ranks beneath that. We hope that this process will force the model to learn an understanding of the distributions of not only individual species, but also genera, families, etc.. This may helpful when facing unseen species as knowledge the genus or family may provide clues about where this species may be found. This similar the approach used by LD-SDM (Sastry et al.. 2023).
In Table A2 show zero-shot performance for FS-SINR models trained TRT the IUCN and S&T evaluation tasks. We see that as we provide additional taxonomic information zero-shot performance improves, though it is still much worse than using habitat or range text. This implies that the model has managed to develop some understanding of the distributions of genera etc. and can use this to help map a novel species that shares higher order taxonomy with species in the training set.
In Figure A2 we provide some qualitative zero and few-shot results showing the impact of training on taxonomic text. We see that the model appears to narrow down on the correct range as more specific taxonomy revealed to
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Feedforward Few-shot Species Range Estimation
Class Avess
Order Columb formes
Columb dae
Family
Genus Trercn
Phoeni Phoenicapte capterus
Species
[TRT Text]
[TRT Text] + Context Location
Figure A2. Zero-shot and one-shot range estimation using Taxonomic Rank Text (TRT). Range predictions for the species Yellow-footed Green Pigcon from SNNR model trained on taxonomic rank text as in LD-SDM (Sastry et al., 2023). with expert-derived range inset. As seen in Figure A3 and Table A2, the text-based zero-shot predictions seem to more closely match the expert-derived range as more the taxonomic rank text localize predictions to areas where species sharing the provided laxonomy ranks present in the training set. For example, Birds are globally distributed and we see the model attempt to output this in the zero-shot "Class' visualization. P Pigeons and Doves are not found in the extreme north and providing these ranks reduces predictions in these areas (and much of the northern hemisphere). The model mostly manages to identify that Green Pigeons are found only in Africa and parts of Asia. A single observation significantly contracts the predicted ranges, particularly when less taxonomic information is provided. Click the taxonomic rank names visit the iNaturalist page for that taxa where the geographic distribution of observations for it can be observed.
species is provided. Taxonomic rank text allows model to somewhat
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Feedforward Few-shot Species Range Estimation
Table Uncertainty quantification with ensembles. show MAP on S&T dataset for an ensemble of three FS-SINR models using 'Range' text and differing numbers of context locations (Ensemble MAP), with metrics for uncertainty quantification adapted from Poggi et (2020). report "Sparsification Error AUC" (SEAUC) and 'Area Under the Random Gain' (AURG) for ensemble. Positive AURG shows the ensemble is performing better than random chance at estimating its uncertainty. We also present results for the same text and context locations for individual FS-SINR models for comparison (Model MAP). Ensembling slightly improves performance
for all numbers of context locations.
# Context
Model MAP
Ensemble MAP
SEAUC
AURG
0
0.64
0.66
0.68
0.03
1
0.66
0.68
0.71
0.03
2
0.67
0.69
0.73
0.03
3
0.68
0.70
0.74
0.04
0.69
0.71
0.75
0.04
5
0.70
0.71
0.75
0.04
8
0.71
0.72
0.76
0.04
10
0.72
0.73
0.77
0.04
15
0.72
0.73
0.78
0.05
20
0.72
0.74
0.79
0.05
50
0.73
0.74
0.78
0.05
from predicting across the entire globe when just the class Aves is provided, to removing northern latitudes as the family Columbi dae added, and finally removing the new world when the genus is provided. This broadly matches the actual distribution of these taxonomic ranks. Note that the relationship between taxonomic hierarchy and species range is likely complex as many speciation events (i.e., when species splits into two or more new ones) can be the result of physical geographic barriers separating populations over time.
In Figure A3 show few-shot results for FS-SINR models trained TRT the IUCN and SNT evaluation datasets. Zero-shot performance improvement with increasing taxonomic information is evident, but after very few provided locations this effect seems to disappear.
Table Zero-shot results with taxonomy rank text. We denote additional metadata used by models as RT 'Range Text' and HT for 'Habitat Text'. 'Species', 'Genus', 'Family', 'Order', 'Class' refer to models trained and evaluated using taxonomic rank text. Taxonomic information up to and including specified rank is provided during evaluation.
Method Variant
IUCN
S&T
FS-SINR
0.05
0.18
FS-SINR
HT
0.33
0.53
FS-SINR
RT
0.52
0.64
FS-SINR
Class
0.05
0.19
FS-SINR
Order
0.06
0.20
FS- PS-SINR
Family
0.12
0.25
FS SINR
Genus
0.18
0.30
FS- FS-SINR
Species
0.21
0.34
A.3. Alternative Evaluation Metric
Here we provide additional results for the main models from Figure using 'distance weighted MAP evaluation metric. This is inspired by the evaluation conducted in LD-SDM (Sastry al., 2023). This metric is based on mean average precision (MAP), however now weight predictions by distance from the true range, i.e., predicting the presence of species far from where is said to be found is penalized more than predicting the presence of species in location that is very close to existing observations, but is still actually outside the range. We intend that this metric more closely aligns with a human's 's judgment on how 'correct' range is, compared to standard MAP. By considering both metrics we can be more confident that the improvement range mapping performance that FS-SINR provides not just consequence of how we are quantifying it. We determine the weight for location x as:
= 1 + drange h
(3)
dantipodal
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Feedforward Few-shot Species Range Estimation
0.7
0.7
0.6
0.6
0.5
0.5
0.4
MAP
0.4
Range
Range
0.3
Habitat Text
0.3
Habitat Text
No Text
No Text
0.2
Species
0.2
Species
Genus
Family
Family
0.1
Order
0.1
Order
Class
0.0
0.0
10
50
10
50
Context Locations
Context Locations
Figure A3. Impact of training and evaluating with Taxonomic Rank Text. Here we evaluate FS-SINR models trained using different context information the IUCN dataset (left), and the S&T dataset (right). 'Class' indicates that only the taxonomic class of the species is provided as text during evaluation. 'Order' indicates that the taxonomic class followed by the order is provided as a text string during evaluation, and so on, such that 'Species' indicates that text string in the format 'class order family genus species' is provided during evaluation. Providing more specific taxonomic text increases zero-shot performance. This is also presented Table A2. However we see that even the full taxonomy does not provide much signal as habitat and range text for zero-shot range mapping. These more detailed texts provide more useful information for zero-shot range mapping cither actually mentioning geographic locations in the case of range text, or allowing the model to narrow predictions down to arcas with specific fcatures such as mountains and forests the case of habitat text. When single context location is provided, the choice of laxonomy text no longer seems to impact performance at all. It is possible that training on these less informative tokens means the model learns to pay less 'attention' to these text tokens compared to the Wikipedia-hased text tokens usually used during training. This could explain why different rank taxonomy text tokens seemingly provide no benefit when any context locations are provided to the model.
where drange (z) is the distance along the earth's 's surface from point x to the nearest point of the expert-derived range using for evaluation, and dantipodat is the distance along the earth's 's surface between two points on opposite sides of the earth. While this distance does vary very slightly in different locations the earth is not perfect sphere, for this experiment we have set dantipodat to 20.037.5 5 km. h is the 'distance weight hyperparameter' and determines how much this metric penalizes incorrect predictions far from the range relative to close to the range. The metric is implemented equivalent to scikit-learn's 's average precisi on score sample e we igtt parameter (Pedregosa et al., 201 1). We evaluate performance using the standard 'unweighted MAP', i.e., where h = 0 and so we are calculating MAP as usual, and 'distance weighted MAP' with h and h 99. We selected these settings so that errors on the opposite side of earth from the true range are penalized 10 and 100 times more than errors close to the true range.
Results on the IUCN evaluation dataset can be found in Figure A4. We do not present results for the S&T dataset we require knowledge of the range of each species globally to fairly apply the sestance-weighter MAP, while the S&T dataset only provides range estimates for portions of the globe for each species. As the weight is increased, we observe a general reduction in overall performance. While there is no change in the relative ordering of different models, and FS-SINR outperforms LE-SINR and SINR across all settings of h, we do observe that FS-SINR and LE-SINR models that use habitat text during evaluation seem to decrease in performance more with larger h compared to other approaches. They are likely most effected by the larger weight, as habitat text can cause the model to predict presence in locations around the world with similar habitat features such as mountains, forest, or desert, despite these locations being far from the true range. This appears to be true of both FS-SINR and LE-SINR. For LE-SINR, using habitat text outperforms not using text for the unweighted MAP, but using habitat text performs worse than not using text for the weighted MAP. In Figure A5, we display zero-shot results for two species where there is a large difference in performance based on the two metrics. In both cases FS-SINR using only text incorrectly predicts the species to be present far from the expert-derived range.
A.4. Expanded Results
In Tables A3 and A4 we present and expanded set of results from Figure 3, for the IUCN and S&T datasets. We also report additional results for the non-few-shot setting using 500 and 1,000 context locations. Performance for most approaches including FS-SINR seems to plateau around 50 context locations, with some approaches gaining a minor boost
17

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Feedforward Few-shot Species Range Estimation
Unweighted MAP, / 0
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LE Range
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[E-Sasl (No Fual trat)
FS- SINR (Pange
FS-S.R Hange
Sast (Sange Tect!
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PS-SIMR planstat Teol
15-s Illabitt
1s: SINR (No Eval
PS-S (No Eva
15-s (No Eval Text)
Context Locations
Context Locations
Locadion.
Figure A4. Zero-shot and few-shot performance using our distance weighted MAP metric on the IUCN evaluation dataset. We find that increasing the distance weight hyperparameter, h. reduces performance across the board without significantly changing the order of different models i.e., FS SINR continues outperform LE-SINR and SINR. performance more as h increases, relative to approaches not using text or using range text.
see that approaches using habitat text decrease
Gravenhorst's Mabuya
African Jacana
Habitor Text: "T. gravenhorstii prefers rocky areas, grassland, shaubland, and forest, but is also found in collee plantations and Blang-ylang plantations. The species is adapted to a variety of environmenial conditions, including different types of ecosystems."
Habilat Text: "The African jacuna prefers shallow lakes and its preferred babitat is Booaiing vegetation. It has specilic environmental condition. which is not mentioned the article, but it can be inferved that it requires certain level of water depth and vegetation cover."
Figure A5. Examples of two species with poor distanced weighted MAP performance. Here visualize FS SINR'S shot predictions using habitat text for two species where there is a large difference between the evaluation scores using the standard MAP metric compared the distance weighted Madagascar, we obtain an MAP of 0.419 but a lower distance weighted MAP of 0.175. For the African Jacana (right), found in most of sub-Saharan Africa, we obtain an MAP of 0.457 and distance weighted MAP of 0.226. The distance weighted metric more heavily penalizes mistakes for these species that are very far from their true range.
(here using h 9). For the Gravenhorst's Mabuya (left), which endemic
in performance in the non-few-shot setting (SINR and Prototype-SINR) while others perform much worse (Active SINR). We also report results for a model trained and evaluated using context locations and features from a visual encoder with a DINOv2 backbone. This performs worse and has significantly more variable performance across runs compared to the pre-trained EVA-02 ViT, which was fine-tuned using species images during its original pre-training, used in the main paper.
A.5. Comparison to Traditional Machine Learning Methods
In Figure A6 we compare FS-SINR with two traditional machine learning approaches for classifying whether species present or absent at a given location. Both approaches were implemented using scikit-lear (Pedregosa al., 201 1). We compare with Gaussian Process approach adapted from Golding & Purse (2016). While this method is designed for presence-absence data, adapt it for the presence-only setting by providing number of negati-ne equal to the provided number of context locations, which performed best out of several strategies we investigated. Using large number of pseudo-negatives as we do for FS-SINR both performed poorly and took excessively long to run as Gaussian Process computations scale cubically with the amount of data. For this approach we use logit link function and a exprnentaa kernel. We also compare to a random forest classifier (Breiman, 2001). We investigated providing the same number of peeuto-negatives as for FS-SINE with appropriate class weights, however we found better performance by
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Table IUCN zero-shot few-shot results. Here we present expanded IUCN evaluation results models shown Figure tabular form. We also show results using DINOv2 based image encoder. SINR and LE-SINR without text cannot produce a range map without at least one context location. Results are presented MAP, where higher better.
FS-SINR
LE-SINR
SINR No Text
Prototype SINR
Active SINR No Text
Context
Text
Image
Text + Image
No Text Image
DINOv2
Text
No Text
No Text
0.52
0.19
0.46
0.05
0.13
0.48
0.57
0.45
0.55
0.48
0.40
0.55
0.47
0.42
0.48
0.48
0.60
0.54
0.59
0.56
0.49
0.57
0.52
0.47
0.53
0.55
0.62
0.58
0.61
0.53
0.58
0.54
0.50
0.56
0.58
0.62
0.55
0.59
0.56
0.52
0.57
0.64
0.62
0.63
0.63
0.56
0.60
0.57
0.54
0.58
0.60
0.65
0.64
0.64
0.65
0.59
0.61
0.59
0.56
0.60
0.62
10
0,66
0.65
0.65
0.66
0.60
0.62
0.60
0.57
0.61
0.62
0.67
0.66
0.66
0.67
0.61
0.63
0,62
0.59
0.61
0.62
20
0.67
0.66
0.66
0.67
0.61
0.64
0.63
0.61
0.62
0.62
0.67
0.61
0.66
0.64
0.62
500
0.68
0.66
0.66
0.67
0.57
0.67
0.67
0.65
0.63
0.37
1000
0.68
0.66
0.66
0.67
0.57
0.67
0.67
0.65
0.63
0.36
Table A4. S&T zero-shot and few-shot results. Here present expanded S&T evaluation results for the models shown in Figure 3 in tabular form. also show results using a DINOv2 based image encoder. SINR and LE-SINR without text cannot produce a range map without at least context location. Results presented MAP, where higher is better.
FS-SINR
LE-SINR
SINR Text
Prototype SINR
Active SINR No Text
Context
Text
Tmage
Text + Image
No Text |Image
DINOv2
Text
No Text
No Text
0.64
0.38
0.64
0.18
0.28
0.60
0.66
0.49
0.66
0.50
0.44
0.64
0.52
0.49
0.54
0.53
2
0.67
0.57
0.67
0.58
0.53
0.66
0.57
0.55
0.59
0.59
0.68
0.61
0.68
0.61
0.57
0.67
0.60
0.58
0.61
0.62
0.69
0.64
0.69
0.64
0.61
0.67
0.61
0.59
0.62
0.65
0.70
0.68
0.60
0.63
0.71
0.69
0.71
0.65
0.69
0.63
0.64
10
0.72
0.70
0.71
0.69
0.67
0.69
0.66
0.64
0.65
0.67
15
0.72
0.71
0.72
0.70
0.68
0.70
0.68
0.67
0.65
0.67
20
0.72
0.71
0.72
0.71
0.68
0.71
0.69
0.68
0.65
0.66
50
0.73
0.72
0.73
0.71
0.69
0.73
0.72
0.72
0.66
0.64
500
0.71
0.72
0.71
0.62
0.73
0.72
0.73
0.67
0.24
1000
0.72
0.71
0.72
0.71
0.62
0.73
0.72
0.73
0.67
providing the same number of pseudo-negatives context locations. In both cases, performance for this task is significantly worse than FS-SINR, LE-SINR and SINR.
B. Ecologically Relevant Analysis of Results
To give a more ecologically relevant analysis our results we present them here by continent, species range size, and taxonomic class. these cases we display results for the IUCN evaluation dataset only, as provides expert-derived presence-absence information globally and includes range of taxonomic groups. In comparison the S&T dataset only includes Aves, i.e., bird species, and evaluates the presence absence of each species portion of the globe, preventing us from calculating global range sizes and performance by continent.
B.1. Results by Region
In Figure A7 show performance of FS-SINR, LE-SINR, and SINR models by continent for few-shot and zero-shot text-only predictions. FS-SINR outperforms other approaches all continents except South America and Oceania, where at larger numbers of context locations L.E-SINR becomes comparable. Biodiversity data and particularly global-scale citizen science datasets such as the Naturalisstderived data we use to train FS-SINR can contain large biases (Geldmann et al., 2016; Hughes et al., 2021), and here we can see the impact this bias within our training data.
We have more species observation training data (taken from Cole al. (2023), which also visualizes the data distribution)
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IUCN
S&T
0.8
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PS: SINR (Text)
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0.0
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Context Locations
Context Locations
Figure A6. Comparison to traditional machine-learning approaches. Here we evaluate a Gaussian Process approach and a Random Forest the IUCN (left) and S&T (right) datasets. We find that both approaches perform far worse than FS-SINR, LE-SINE SINR for species range estimation using presence-only data. 'Range" text is used here for FS- SINR and LE-SINR, while other approaches take only context locations as input.
from Europe and North America than from other areas, and the observations these regions are relatively well distributed spatially. In other continents, there are large areas with very few or no training observations for models learn from, along with small areas that are highly observed. On top of this, text descriptions and may be more descriptive for species found in areas where English is widely spoken, and our pre-trained large language model may have more knowledge of North American and European geography and ecology due to biases in the text data used for training. Combined, these factors lead to higher performance in North America and Europe compared to other regions. In Figure A10 we show the average false positive error for few-shot range estimation for FS-SINR on the IUCN evaluation dataset. This indicates greater error in regions where we have less training data.
taken from English language Wikipedia
B.2. Results by Species Range Size
Here, we display results showing the average MAP for species in our IUCN evaluation dataset, grouped by range size, where range size is computed from the expert-derived range maps. In Figure A9 we break down performance of zero-shot approaches by range size for FS-SINR, LE-SINR, and SINR. We find that for all models and settings, performance varies very strongly with range size. This is most significant in the zero-shot setting. FS-SINR performs well compared to the baselines, though all models struggle with very small ranges. As small-ranged species are especially vulnerable to extinction (Chichorro et al., 2019), current methods performing poorly for these species when evaluated globally may be of concern and improving the modeling of these species may be a priority from a conservation perspective. We also see that performance worsens for the very largest ranges.
B.3. Results by Taxonomic Class
Here we break down performance on the IUCN evaluation dataset by taxonomic class, Four taxonomic classes are present in our training data, namely performance for FS-SINR and LE-SINR using range and habitat text. We observe that Aves and especially Mammal ia outperform the other classes, particularly when habitat text is provided. Albert et al. (2018) suggest that of the 20 most 'charismatic" species in the western world, all but the Great White Shark and Crocodile are mammals, and Trimble & Van Aarde (2010) show that scientific research is heavily focused on mammals. We may be seeing the impact of this, where mammals are more likely to have detailed Wikipedia pages which we draw our textual training and evaluation data from. In Figure A8 (b), (c), and (d), we investigate how these differences in performance between taxonomic classes change as more location data is provided. We see that for both PS- and LE-SINR, providing context locations significantly reduces the differences in performance between taxonomcc class, though mammals do continue to very slightly outperform other taxonomic groups for a given model and setting.
Aves, Mammal ia, and Reptilia. In Figure A8 (a) we display zero-shot
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Feedforward Few-shot Species Range Estimation
P5- dING Raupe
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Bacopa
Borth Aturics
Sourb Adutton
Creepi
Aucopa
North Aloricc
Souts Amedica
(c) 20 Context Locations
(d) 50 Context Locations
Figure A7. CUCN Performance by continent. Error hars show standard error of the mean.
C. Implementation Details
C.I. FS-SINR
C.I.1. MODEL ARCHITECTURE
Code for FS-SINR is available at http://ghhuub.ooo//rrraraaag components: the location encoder, f: the text encoder, the image encoder, a; the transformer encoder, m; and the species decoder, s. These components comprise 8,154,368 learnable parameters in total. All non-linearities in FS-SINR are ReLUs.
The architecture for FS-SINR consists of four
The location encoder, f. is identical to the one used in Hamilton et al. (2024), which is taken from Cole et al. (2023). It is composed of an initial linear layer and ReLUU non-linearity followed by four residual layers, where each is a two-layer fully connected network with residual connections (He et al., 2015) between the input and output of each residual layer. Each layer contains 256 neurons, and there are 527,616 learnable parameters in total.
The text encoder, follows the structure of the text-based species encoder from Hamilton al. (2024). In f, pre-trained and frozen large language model, GritLM (Muennighoff' et al., 2025), is used to produce a fixed 4,096 length embedding from input text. This is then passed through a smaller network to reduce the dimensionality to 256. This smaller network consists of two residual layers with a hidden layer size of 512. In total, the text encoder contains 3,410,432 learnable parameters.
The image encoder, a, has a structure similar to I. In a, a pre-trained and frozen vision transformer, EVA-02 (Fang et al., 2024), pre-trained on images from 10,000 species from the iNaturalist species classification dataset (Van Horn et al., 2021). is used to produce a fixed 1,024 length embedding from an input image, by extracting the CLS token from the final layer of the model. This is then passed through a smaller network to reduce the dimensionality to 256. This smaller network consists of two residual layers with a hidden layer size of 512. In total, the image encoder contains 1,837,568 learnable parameters. Tables A3 to A5 include results using DINOv2-large image encoder (Oquab et al., 2024) instead of the EVA-02 ViT where all other architecture choices remain the same.
The transformer encoder, m, takes in an arbitrary length set of unordered 256 dimensional tokens produced by f. 6, and o,
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Feedforward Few-shot Species Range Estimation
diur Raape
dier Ronee
0.8
15 dtar Hratisd
diar Hlstiad
Meath Nowregn Preeteom (MOXT)
244
Mede
033
FS: Ster Banqe
Roo
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Sost
SicSter No Tiok
Arphiilass
Binkk
Manerals
Popdlins
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deseage Prestate
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2.4
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0.8
Ronge
Stor Borqpe
F5: Stor Ilabbis
3.8
6.0
(c) 20 Context Locations
(d) 50 Context Locations
Figure A8. IUCN Performance by taxonomic group. Error hars show standard of the mean.
as well as two learned tokens that are added to each set of inputs. The CLS, class, token produces the species range, and a 'Register" token, inspired by Darcet et al. (2024), which acts as an additional repository of global information during encoding. Flement-wise addition between each token and one five learned 256 dimensional 'token type embeddings performed allow the model differentiate between tokens from different sources. The transformer itself is composed of four transformer encoder layers, implemented using PyTorch's Trans fo rme www.eeernoooereeeoeee 2019), based Vaswani et al. (2017). Key-query-value multi-head attention is used with two 'heads.' The feed forward components contain 512 neurons per layer, while the token dimensionality is 256. Layer norm is used in each layer, using default epsilon value of le-5 for enhanced numerical stability. In total, m contains 2, 2,176,256 learnable parameters. Finally the species decoder, s, is simple fully connected network with two hidden layers. Each layer contains 256 neurons, and in total the decoder contains 197,376 learnable parameters.
(Paszke et al.,
C.1.2. TRAINING
For all training we the Adam optimizer (Kingma & Ba, 2015) with learning rate of 0.0005, and exponential learning rate scheduler with learning rate decay of 0.98 per epoch, and use batch size of 2048. Our training data comes from Cole et al. (2023), comprising 35.5 million species observations with locations, covering 47,375 species observed prior to 2022 on the iNaturalist platform. However, we remove all species found our evaluation datasets, leaving us with 44,181 species in our training set.
Training comprises of two steps. First, the location encoder, f. is trained. This follows the training procedure of Cole et al. (2023) using the LaN-ful loss function with positive weighting, X, 2,048, training for 20 epochs with a dropout of 0.5. To reduce training time without significantly impacting performance only train maximum of 1,000 examples per-species, done in Cole et al. (2023). Thus, our training dataset for this step contains 13.8 million location observations. Secondly, we train all components of FS-SINR, except the pre-trained large language model and the pretrained vision transformer, using our LAN-GaLe loss with À set to 2,048. We train the location encoder, f. again as this improves performance compared to freezing it,as seen in Figure A15, For this part of training, we use a dropout of 0.2. We further reduce the training data used to a maximum of 100 examples per-species, leaving 4.0 million training examples, which again increases
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Feedforward Few-shot Species Range Estimation
3.8
2S-
SiNi Roingh
Raupe
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LG SDNR Rooge
SUNIS Fuage
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25-5INGA Itatiia
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PS: SUNI Iraiila:
SDNI No
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Mewn Dovergee
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Runge Sizee (Jax)))
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LE SINK Holror SINK That
Mewth Freecoom
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Mean Aoerage Frechom
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LESSE paage
L1:/0/00 Tind.
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1N--0M
10000
10 Ronge Size Ocn")
3M-33M
10M6193M
-1000
(c) 20 Context Locations
(d) 50 Context Locations
Figure A9. IUCN Performance by range size. Error bars show standard error of the mean.
training speed without a significant impact on performance, as seen in Figure A16. For this step we additionally train with images and text descriptions of the training species. Each instance in the training set is used once per epoch as a training example to compute the loss. The training example is not passed through the transformer encoder, m, and so does not contribute to the species embedding vector produced by this part of the model. Instead, additional context information is provided to produce the species embedding. By default, this context information consists of 20 context locations, a section of text describing the target species, and image of the species. With 0.2 probability the context locations are dropped from the context information, and with 0.5 probability each the text or image is dropped. These context locations are taken from the training data for the target species. As such, a single instance from the training set can be used multiple times per epoch, once as a training example, and potentially many times as a context location. The impact of different distributions of locations and text provided during training is shown in Figure A13.
For the text inputs required during this stage of training, we use the text dataset from Hamilton al. (2024) consisting of multiple sections of Wikipedia (Wikipedia, 2025) articles for each species in the train set where these available. This dataset contains 127,484 sections from 37,889 species' articles. The evaluation text either describes the habitat or range a species, where habitat text tends to describe the local environment and range text is typically more informative as it often lists specific countries or geographic regions where the species can be found. Note that not all 44,181 train species have text data available. The images used are taken from iNaturalist (2025), and this dataset comprises 204,064 images of our train species. When an image or piece of text is not available for a species during training, and we are trying to provide these modalities and context locations to the model, we simply ignore the additional modality and only provide the context locations. When we are attempting provide just an image text context, instead skip that training instance.
In practice, during training, we pass all text sections through the frozen large language model once and then store the embeddings produced to use in the current training run and all future runs, and similarly extract and store all image embeddings after passing the images through the frozen vision transformer. This prevents us having to repeatedly query these frozen, but resource-intent intensive, models during training. Training takes approximately ten hours on a single NVIDIA A6000 GPU, requiring approximately six gigabytes of RAM.
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Feedforward Few-shot Species Range Estimation
Range Text
Habitat Text
No text
10010
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Figure A10. Average false positive error by location for few-shot approaches. Here we see average false positive ertor of FS-SINR on the TUCN dataset. Providing any text leads increase the false positive error, although Figure 3 suggests that this still helps with range estimation performance. As the number of provided context locations increases, the impact of the text is reduced and the distribution errors appear similar.
C.2. Baselines
C.2.1. LE-SINR
We compare our approach to the recently introduced species range model LE-SINR (Hamilton al., 2024) that can incorporate information. We follow the original architecture and training procedure for LE-SINR and SINR, with the exception that enforce that SINR, like LE-SINR and our approach, is trained reduced set of 44,181 species which do not include any of the evaluation species.
We also follow the original evaluation procedure for LE-SINR. For few-shot evaluation without text, logistic regression with L2 regularization is performed with location features as input using the few positive examples provided alongside set of -scudo-negatives drawn half from a uniform random distribution and half from the training data distribution. The regularization weight is set to 20. For text-based zero-shot evaluation, we directly make use of the output of the text encoder with the dot product between this and location features giving probability of species presence. For few-shot evaluation, when text provided, 'target' that the weights are drawn towards in modified L2 regularization term,see Hamilton et al. (2024) for more details, The regularization weight is again set to 20. In total, this model comprises 25,715,202 learnable parameters.
again perform logistic regression, but the output of the text encoder is used the
C.2.2. SINR
We also compare to SINR (Cole et al., 2023). The original SINR implementation requires all evaluation species to be part of the training set. We match the adaptations from Hamilton al. (2024) to allow evaluation on unseen species. After training
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Feedforward Few-shot Species Range Estimation
remove the learned species heads and keep only the location encoder. During evaluation, we perform logistic regression with 1.2 regularization using location features input. The regularization weight is again 20, and the same method of selecting pseudo-negatives as above is used. In total, this model comprises 1 11,941,120 learnable parameters.
C.2.3. PROTOTYPE SINR
Here we describe our few-shot baseline based on Prototypical Networks (Snell et al., 2017), which we refer to as Prototype SINR. Our approach is very similar to Snell et al. (2017) although we use the SINR location encoder of our models as the "embedding function', allowing us to generate few-shot results for a novel species without any retraining. This SINR location encoder is trained only on species found in the training set and not those used for evaluation. Using this method, SINR and LE-SINR models can be used to estimate the range of novel species without requiring training to learn new species embedding vector.
In order to do this, we first encode our known 'presence" locations using the location encoder of our chosen model and then take an average of these points to generate a 'prototype" for the presence class. We select pseudo-negatives in the same manner as in Hamilton et al. (2024) and similarly encode and average these in order to generate a prototype for the 'absent' class. We represent these prototypes as:
fo(æ:),
(4)
SK
ES
where & E {present, absent) indicates the class of the prototype, and S is the 'support set', i.e., the set of locations x that we use create our prototypes. In our case, Spresent is the small set of available context locations for target species, i.e., C². while Sobsent is the set of pscudo-negative locations that we have selected according to Hamilton et al. (2024). fo denotes the location encoder of our model.
To generate a probability of presence or absence for any location æ, we encode x using our location encoder and calculate the cosine distance in 'location encoder space' between æ and each prototype. We then use these values as the 'logits' in a softmax function to generate our probabilities. The parameters of the location encoder are not changed. Putting this together, we can calculate the probability of presence as:
(f((()))
Ppresent (x) edlff( (prosc))) + ed((o(())
(5)
where d(a, b) represents distance metric between and b, in this case, cosine distance.
While the original implementation in Snell et al. (2017) uses the squared Euclidean distance instead of cosine distance we find that this performs significantly worse and actually results in decreased MAP as the number of context locations increases. We suggest the SINR location encoder is more suited to using cosine distance, as during training presence predictions are generated by taking the dot product of the location and species embeddings. However, when the location encoder is trained from scratch for Prototype SINR as in Snell et al. (2017) we find that using the squared Euclidean distance performs better than cosine distance, although performance is still lower than cosine distance with a SINR location encoder.
In Figure 3 see that the performance of Prototype SINR is worse than FS-SINR and the SINR and LE-SINR baselines. In Table A.5 we provide zero-shot results where the positive prototype is species embedding produced from text, in the same manner as LE-SINR zero-shot predictions. In both cases, Prototype SINR underperforms compared to our approach. In Figure All we present qualitative results visualizing the few-shot estimated range for the Ka Kalahari Scrub-Roc Robin produced by FS-SINR and by the Prototype SINR baseline.
C.2.4. ACTIVE SINR
We also compare to the model introduced for active learning in Lange et al. (2023), which we call Active SINR, although in setting there is no active learning component. This approach begins with SINR model trained our reduced 44,181 species which do not include the evaluation species. The weights W of the lulti-label classifier of this model can be viewed as a set of species embeddings where each column vector w of W represents an individual species j. We can combine these species embeddings with a location embedding fe (z) via an inner product to compute the probability that the species j is present at x. At inference time, we compute the presence probabilities for all species in the training set, for all locations e in the set of available context locations C for our target species f. We then produce a new species embedding u. by taking
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Context Location
2 Context Locations
(No Eval text)
FS-SINR
Prototype- SINR
Figure ALI. Qualitative comparison of the Prototype SINR baseline, Here we compare the predictions of our FS- FS-SINR approach (without any text) and our Protolype SINR baseline on the Kalahari Prototype SINR approach obtains an MAP 0.54 and 0.79, for one and two context locations respectively, while FS-SINR oblains 0.79 and 0.85. As MAP is tied to the ranking of predicted probabilities rather than their absolute values, can remain high even if the model is somewhat overeonfident across the board. As long as the highest probabilities consistently align with areas where the species is truly present, the model will achieve strong MAP, which we see with the predictions from Prototype SINR.
serub-Robin species that is found in Southern Africa. The
a weighted average of the existing wy² where the weight for each is the product of the probabilities of presence for that species:
W. P(w,lC")w).
(6)
We can then use this new species embedding for our target species to produce a probability presence for any location x as in SINR (Cole al., 2023). We present few-shot results using this method in Figure 3. We see that the performance of the Active SINR approach is competitive with FS-SINR when provided with no text, though worse than FS-SINR when provided with this additional context. However, increasing the number of provided context locations beyond small number actually hurts performance, it is unable to accurately represent the range of a previously unseen species via the weighted combination of those from the training set.
C.3. Evaluation
We perform three runs for each experiment using different initial random seeds and report the mean. We display the standard deviation as еттог bars in our figures. For all evaluations across SINR, LE-SINR, Prototype SINR, Active SINR, and FS-SINR, the same set of context locations are used for a given species, and these context locations are accessed in the same order, so all evaluations using five context locations are performed with the same five points, and four of those points are those used for evaluations using four context locations, etc. our few-shot setting, we use at most 50 context locations during both training and evaluation.
D. Additional Ablations
Here we present additional results to investigate the impact of a variety of design choices and training procedures for PS-SINR. We present plots on "Symlog" scale, where linear seale is used between and 10, in order to allow us to show zero-shot results alongside few-shot results. We display the mean three runs with standard deviations shown as error bars and also show just the mean values alongside for easier interpretation.
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D.I. Ablating Training Context Locations
In Figure A12 we show 'Range Text' evaluation performance on the IUCN dataset for FS-SINR models trained using different amounts of context information training time. We see that generally increasing the context used during training improves performance, and that having fixed number of context locations is also beneficial.
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Figure A12. Impact of number of training context locations. Here evaluate FS-SINR models trained using different numbers of context locations. Results are shown with standard deviations from three runs (left), and without (right) for clarity. Evaluation performed with 'Range Text' the IUCN dataset. "Fixed" indicates the same number of context locations were provided for every training example. 'Variable' indicates that a uniform random distribution of context locations up to the specified number were provided with each training example. We see that 'Variable' generally under-perforee compared 'Fixed' and that increasing the train context length tends to increase evaluation performance.
D.2. Ablating Context Information
In Figure A13 we display 'Range Text' evaluation performance on the IUCN dataset for FS-SINR models trained using different combinations of text and context locations during training. We observe that good text-only zero-shot performance requires sometimes providing just text as context information during training. This forces the model to learn to produce ranges from only text information. Models that sometimes provided with both text and locations for the same training examples perform best as the number of provided context locations increases. We also see that models trained without text can perform on par with those that see text during training when enough context locations are provided (5 - 10). As we might expect, models that are provided with token types they have not seen during training perform poorly.
D.3. Ablating Input Features
In Table A5 we provide additional zero-shot results expanding on those in Table I from the main paper. Specifically, we add comparisons to using a different location encoder (i.e., SATCLIP (Klemmer et al., 2025) instead of SINR), comparisons to using a DINOv2 pre-trained image encoder (DINOv2-large) (Oquab et al., 2024), comparisons to using the environmental covariates as in SINR (Cole et al., 2023) that contain information about locations' climate in addition the spatial coordinates.
D.4. Ablating Location Encoder
In Figure A14, we vary the number of datapoints used to pre-train the SINR encoder used in FS-SINR. For both FS-SINR and the SINR baseline, we generally observe that more data is better, and for SINR approaches we see that pretraining the encoder is much better than randomly initializing it. We also show results for SINR model trained evaluation species in addition to train species. As we saw in Table for FS-SINR, the impact of training the location encoder with evaluation species is small.
In Figure A15, we also investigate the impact of changing the location encoder entirely. We see that replacing our SINR location encoder with pre-trained and frozen 'SATCLIP' location encoder (Klemmer et al., 2025) significantly harms performance. This may be due to this model being frozen and trained on tasks that do not completely match ours. In
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Figure A13. Impact of train context information. Here we evaluate FS-SINR models trained using different context information on the IUCN dataset. Results shown with standard deviations from three runs (left), and without (right) for clarity. Evaluation is performed with 'Range Text' unless 'No Eval Text' is specified, in which case just locations are provided during eval. 70% of training examples for 'Default FS-SINR' provide both location and text context, 20% provide just locations 10% and provide just text. 'Always Obs. Only' has only seen locations during training. 'Always Text Only' has only seen Text during training. 'Always Text and Obs' is always provided with both locations and text during training. 'Always Text or Obs.' is provided with just locations for 90% of training examples, and just text for the remaining 10%.
comparison, a randomly initialized and untrained SINR backbone performs almost identically well as one that has seen a small amount of training data (10 examples per-species in the train set). We also investigate replacing the learned location encoder f( with simple form Fourier feature encoding (Tancik et al., 2020) to encode location inputs to the transformer m) In this setting, a pre-trained and fine-tuned SINR type location encoder f() is still used to encode evaluation locations to determine the probability of presence of species j via the inner product between the species embedding vector wy and (x)) However, s() is not used to encode the context locations C² before they are passed to m ()- Using these two different encoders performs increasingly poorly as the amount of context information increases.
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Figure A14. Impact of Location Encoder Training. Here we evaluate the performance of SINR and FS-SINR models when the size of the training dataset for the SINR backbone varied. Results for FS-SINR models are shown with standard deviations from three runs (left), and without (right) for clarity. Evaluation on FS-SINR is performed with 'Range Text', while SINR can only make use of location data. '1000' "100', and '10' represent the maximum number of examples per class the SINR backbone trained 'SINR (rand, init)' is initialized with random weights and is not trained. '(trained on eval species)" indicates training on all training and evaluation species.
D.5. Ablating Training Data
In Figure A16 we vary the number of examples per-species that are provided during training. The impact of this fairly small, with models trained on an intermediate amount of data performing best. It is worth noting, that not all species the
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Table Additional zero-shot results. report zero-shot performance where no location information provided each model. comparing SINR (Cole et al., 2023), LE-SINR (Hamilton et al., 2024), and variants, We denote additional metadata as: EN for additional environmental covariates (Fick Hijmans. 2017) used Cole al. (2023), for 'Habitat Text', RT 'Range Text', for 'Image' using default EVA-02 image encoder, I (DINOV2) for Image using a DINOV2 based image encoder (Oquab al., 2024), TST for "Test Species Train', TRT using full taxonomic rank text, SATCLIP where SINR encoders are replaced with the image derived location encoders from Klemmer al. (2025), and P-LE-SINR for 'Prototype LE-SINR', Results reported as MAP, where higher better.
(a) Methods without additional environmental covariates
(b) Methods with additional environmental covariates
Method
Variant
IUCN
S&T
Method
Variant
IUCN
S&T
TST (lest species in train)
TST (test species in train)
SINR
TST
0.67
0.77
SINR
EN. TST HT, EN, TST
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0.81
FS-SINR
HT, TST
0.38
0.59
FS-SINR
0.38
0.61
FS-SINR
RT, TST
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FS-SINR
RT, EN, TST
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With SATCLIP encoder
LE-SINR
FS-SINR
HT, SATCLIP
0.20
0.43
LE-SINR
HT. EN RT, EN
0.31
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RT, SATCLIP
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LE-SINR
0.51
0.61
Prototype SINR
FS-SINR
P-LE-SINR
HT
0.23
0.48
FS-SINR
EN HT, EN
0.07
0.64
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RT
0.40
0.55
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0.32
0.53
LE-SINR
FS-SINR
RT, EN
0.51
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LE-SINR
HT
0.28
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LE-SINR RT
0.48
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FS-SINR
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(DINOv2)
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training dataset have as many as 1000 observations. We find that a model trained only 10 examples per-species performs significantly worse.
D.6. Ablating FS-SINR Architecture
In Figure A17 vary the underlying FS-SINR architecture. Removing different components has a small effect on model performance, with the removal of the species decoder actually improving results when range text is provided. However, as several ablations perform very similarly, it is difficult to tease out the how much of this effect is due to variance. It is clear however that removing the learnable token type embeddings causes the model to completely fail to learn during training. In Figure A18 we show further ablations based around removing the learned location encoder for inputs to the transformer and replacing it with the simple Fourier feature encoding also seen in Figure A15. When this is removed, other ablations seem to further harm performance, although results for these ablations vary significantly between runs.
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Figure A15. Impact of location encoder. Here we evaluate the performance FS-SINR style models with different location encoders. Results shown with standard deviations from three runs (left), and without (right) for clarity. Evaluation is performed with 'Range Text' on the IUCN dataset. '1000", '100', '10' represent the maximum number of examples per class the SINR backbone was trained on. '(Frozen)' indicates that the location encoder parameters location encoder with a pretrained, frozen location encoder from Klemmer et al. (2025). 'FS-SINR (Fourier Location Encoder)' uses the simple Fourier feature encoding (Tancik et al., 2020) used in Mildenhall al. (2021) match the 256 dimensional outputs of the SINR location encoders. These outputs are used directly inputs to the transformer encoder. After a species token is produced in this way, it is attached to pre- pre-trained and fine-tunce SINR backbone to produce range.
not updated during FS-SINR training. 'FS-SATCLIP" replaces the SINR
E. Additional Qualitative Results
F. Visualizing Non-Species Concepts
By jointly training on text and locations, FS-SINR able to spatially ground abstract non- non-species concepts in a zero-shot manner, as is done with LE-SINR in Hamilton et al. (2024). In Figure A19 we provide another example similar to Figure in the main paper. Here, again fix the context location and show the impact of changing the text. We text prompts can result in quite different predicted ranges. In Figure A20 we see examples where different text concepts, which are very different from the species-based text provided during training, are grounded in sensible locations on the map. In Figure A21 we compare predictions made with increasing numbers of context locations in desert regions, with or without the accompanying text prompt "Desert". As we increase the number of context locations, the two different models converge to more similar range predictions.
that different
E.2. Visualizing Estimated Species Ranges
Here, we provide additional examples of the ranges produced by FS-SINR using context locations, text, and images. In Figures A22 and A23 we visualize FS-SINR range estimates for two different species when habitat or range text is provided. We observe that the combination of text and context locations seem to result in better estimates of the range. In Figure A24 show additional zero-shot image-only examples, where FS-SINR is provided single image from held-out test species at inference time. Again, we observe some plausible range predictions even with such limited input data.
In Figure A26 we show range estimates for the Brown-banded Watersnake, using 'range' text for FS-SINR and LE-SINR approaches. In Figure A27 we show range estimates for the Brown-headed Honeyeater, using 'habitat' text for FS-SINR and LE-SINR approaches. Finally in Figure A28 we show range estimates for the Crevice Swift, without providing text. Overall, SINR produces more diffuse ranges and requires more locations to narrow down the range. LE-SINR and FS-SINR appear have very different zero-shot behaviors, with LE-SINR frequently seeming to predict presence in almost no locations at all, while F.-SINR tends to produce zero-shot range that is too large.
In Figure A25 we visualize FS-SINR range predictions for the walllo--foteed Green Pi Pigeon for models that have had different random initializations (i.c., different random seeds). We observe that there is a relatively large amount of variance in the outputs produced given the same input data. The same set of input context locations could represent many different possible output ranges, and thus being able to represent this variety is advantageous. We also utilize these different predictions from different seeds for ensembling and uncertainty quantification in Appendix A.
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Figure A 16.. Impact training data. Here we evaluate FS-SINR models trained with different amounts data. Results are shown with standard deviations from three (left), and without (right) for clarity. Evaluation is performed with 'Range Text' the IUCN dataset. The labels show the maximum number of examples per-species that FS-SINR is trained on. We see that training on an intermediate amount of training data leads to best performance.
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Figure A17. Ablating model architecture components. Here evaluate the performance of FS-SINR style models ablate various design choices. Results are shown with standard deviations from three runs (left), and without (right) for clarity. Evaluation is performed with 'Range Text' the IUCN dataset. We small changes in performance when removing the register token and the species decoder. However removing the learned token type embeddings has a large impact.
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Figure A18. Further ablating model components. Here we evaluate the performance of FS-SINR style models as we ablate more components. Results are shown with standard deviations from three runs (left), and without (right) for clarity. Evaluation is performed with 'Range Text' on the TUCN dataset. "FF" indicates that the model does not use SINR backhone to encode location inputs to the transformer encnder. Instead, simple Fourier feature encoding (Tancik al., 2020) used in Mildenhall al. (2021) I) is used to increase the dimensionality location data match token dimension the transformer encoder. These are used directly as inputs to the standard SINR backbone to produce range.
transformer encoder. After species token is produced in this way, it is attached Removing the SINR backbone for encoding inputs to the transformer has large impact on performance, especially when more context locations supplied, and makes the model sensitive to the impact of other ablations.
Noo Text]
Q "mountain range"
Q "tropical climate"
Q "arid desert"
Figure A19. Controlling range predictions using single context location and text. Here we show another example similar to Figure in the main paper. Given the context location, denoted 'o', FS-SINR can produce significantly different range predictions depending on the text provided. This example illustrates a use case where a user may have limited observations but some additional knowledge regarding what type of habitat species of interest could be found in.
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"Tasmania"
"The Andes"
Q "Lord of The Rings"
"Baseball"
"Pirates"
"Oktoberfest"
Figure A20. Zero-shot non-species concepts. can evaluate FS-SINR a zero-shot manner using only text information, i.e., without any locations. Here, observe that FS- like LE-SINR (Hamilton et al., 2024), can localize abstract (i.c., non-species) concepts geographic space, despite never being trained to explicitly do so. The model achieves this as learns to make connections between species text and information already contained in the pretrained language encoder we use. However, we do note fallure/ambigoous cases such as the "Pirate" example in the bottom row.
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context
context
2 context
context
[No Text]
Q "Desert"
Figure A2I. Varying the context information provided. Here change the context information provided to FS-SINR. The model the left column receives no text input, but the one on the right gets the text "Desert". Additionally, each row we increase the number of context locations provided, from prior about the species being present at desert-like locations, e.g., see first row where no context locations are provided. As soon as one context location is added North Africa (sccond row), the model generates prediction with increased probahility that the species present there.
three, denoted as 'o". We observe that the model the right that
already has strong
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European Robin Range Text
European Robin
Habitat Text
Range Text: The European robin found Eumpe, Western Siheria and south to North Africa, sedentary most of its range except the far north. It also in the Atlantic islands far the Central Group the and Madeira. is vagrant in Iceland and has been introduced to other regions, including North
Habitat Text: The European rohin inhuhits variety hahitats, including gardens, parks, woodlands, and forests. prefers areas with dense vegetation and is often found human settlements. seen in urban ureas, such as cities and towns.
found in mountainous regions be
Australia,
introductions were unsuccessful.
Figure A22. Using text descriptions. Here illustrate the zero-shot (top row) and one-shot (bottom row) FS-SINR range estimations based on text descriptions for the European Robin, using 'Range' (left), and 'Habitat' (right) text, shown below the range estimates. Expert-derived range maps shown inset in the top row.
American Pika Range Text
American Pika Habitat Text
Range Text: The American pika (Ochotona princeps) found in the mountains of North America, usually houlder fields above the line, from central British Columbia and Alberta in Canada to the US states of Oregon, Washington, Idaho, Montana, Wyoming. Colorado, Utah,
Habitat Tex:: Pikus inhabit talus fields that are fringed by suitable vegelation in alpine They also live piles of broken rock. Sometimes, they live in man-mane made substrate such as mine tailings and piles of scrap lumber. Pikas usually have their and nest below rock. on larger and more prominent rocks.
Mexico.
20-100
in)
often
Figure A23. Using text descriptions. Here we illustrate the zero-shot (top row) and one-shot (bottom row) FS-SINR range estimations based descriptions for Emericas Pika, using 'Range' (left), 'Habitat" (right) text, shown below range estimates. Expert-derived range maps are shown inset.
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Regent Parrot
Blue Duck
African Civet
Wiiee Ibis
Black Turnstóne
Barred Monkey Frog
Figure A24. Image zero-shot range estimation. Here sero-shot range estimates for six species in the IUCN evaluation dataset, with expert-derived range and image inset. The blue duck image taken from iNaturalist (2025) only shows evidence of the species from footprints in wet sand. We see that this image generates predictions in coastal in various locations around the globe. The coastal background for the Black Turnstone could have helped the model to generate a relatively accurate prediction on the northwest coast of North America.
Run
Run
Run
Range
Habitat
No Text
Figure A25. Impact of random initialization FS-SINR. Here display range estimates for the Yellox-footed Green Pigeon from three different FS-SINR models where different random seeds were used to initialize each model during training. We show zero-shot results using 'range text' (top) and "habitat text' (middle), and also few-shot results using context location with (bottom). The IUCN expert-derived range is shown inset. We see that even when provided with the same inputs, different models can perform very differently when this input very sparse (c.g., just text or one context location). While most of the Indian part of the actual range is included for all input types and runs, there is significant variability across the runs in other geographic areas. Range Text: "The yellow-footed green pigeon is found Maharashtra."
Indian subcontinent parts Southeast Asia. It is state bird
Habitat Tex:: "The species is a habitat generalist, preferring dense forest areas with emergent trees, especially Banyan trees, can also be spotted in natural remnants in urban areas. They forage in flocks and are often seen sunning the tops of trees in the early morning."
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FS-SINR
LE-SINR
SINR
Context
20 Contesl
Figure A26. Comparing estimated ranges across models with context Icoations and range text. Here we see zero-shot and few-shot range estimates produced by FS SINR, LE-SINR, and SINR for Brown-banded Matersnake, with expert-derived range inset. We provide range text to FS-SINR and LE-SINR as well as context locations, but SINR is not capable of accepting text and we show blank map for the zero-shot range estimate. We see LE-SINR underestimates the range using only text, while FS-SINR overestimales it. SINR requires more location data than the other approaches to localize the range to South America. Range Text: "The Brown- Brown-banded water snake (Helicops angulatus) found in tropical South America Trinidad and Tobago."
37

---

Feedforward Few-shot Species Range Estimation
FS-SINR
LE-SINR
SINR
2 Contoxt
Contect
Context
Figure A27. Comparing estimated ranges across models with context locations and habitat text. Here we see zero-shot and few-shot range estimates produced by FS SINR. LE-SINR, and SINR for the brown-headed Honeyeater: with expert-derive range inset. FS SINR LE LE-SINR as well as context locations, but SINR not capable of accepting text we show
We provide habitat blank map for the zero-shot range estimate. We again see LE-SINR underestimate the range using only text, while FS-SINE has very to narrow down the range even after 20 locations the range is still significantly larger than the other models, extending into South Africa.
good zero-shot performance for this species. We see that SINR again requires more location
Habitat Text: "The brown- headed honcyeater inhabits temperate forests and Mediterrancan-yye typa shrubby vegetation. typically found in tall trees, where forages probing in the bark of trunks and branches."
38

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Feedforward Few-shot Species Range Estimation
FS-SINR
LE-SINR
SINR
2 Contoxt
Contect
Context
Figure A28. Comparing estimated ranges across models. we see few-shot range estimates produced by FS- IS-R,,,, LE-SINR, SINR for the Crevice Swift lizard, with expert-derived range in Mexico inset. No text is provided and so no sensible zero-shot prediction can made for any model. However while LE- SINS SINR cannot produce an output for this and so we show blank map. FS-SINR generate predicted range just from feeding the learned CLS and register tokens with no other information into the transformer encoder. The range that is produced contained within model or the learned tokens itself rather than from any further inputs. Absent additional information, the model scems guide predictions towards areas where it c.g., Europe and North America. This may be an unhelpful bias when attempting to model novel species. SINR again produces more diffuse ranges than the other methods, though all approaches struggle model these small ranges, seen in Appendix B.2.
many species during training
39


Reviews:
[{"review_id": "UIVT1pDKQ1", "reviewer": "Reviewer_QZrb", "summary": "This paper introduces FS-SINR, a novel Transformer-based approach for few-shot species range estimation that can predict ranges for previously unseen species without requiring retraining. The model architecture combines a location encoder for processing geographic coordinates, a frozen GritLM text encoder for species descriptions, a frozen EVA-02 ViT image encoder for species images, a Transformer encoder that processes combined input tokens, and a species decoder that generates final range predictions. The model uses learned token type embeddings to handle different input modalities and is trained using a modified version of the SINR loss function. On the IUCN and S&T benchmark datasets, FS-SINR achieves state-of-the-art performance, particularly in low-data scenarios (1-10 observations), and can make effective predictions even with a single context location. The model can also generate zero-shot predictions using only text or image inputs, with performance improving when multiple types of context information are combined. Unlike previous approaches, it requires no retraining for new species. The authors note several limitations, including that predictions are deterministic rather than probabilistic, performance depends on the quality and availability of text/image metadata, the approach is subject to biases in training data distribution, and currently only handles presence data rather than confirmed absences. The paper validates these claims through extensive experiments and ablation studies comparing different model components and training strategies.\n\n## update after rebuttal\nI thank the reviewers for their responses. As there is consensus on accepting the paper, I will keep my accept recommendation. Good work!", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank **QZrb** for their constructive comments. \n\n**[QZrb-1] Robustness to number of training locations.**  \nAn ablation of the number of training context locations is provided in Fig. A2. We will update L250 Col1 to more clearly point to this result. As can be seen, fewer context locations (i.e. 5) performs worse, but the difference between 20 and 50 is small.  \n\n**[QZrb-2] Quantifying efficiency.**  \nPlease see response **[nqXQ-1]**.\n\n**[QZrb-3] Downstream biodiversity analysis.**  \nPlease see response **[HT4t-4]**.\n\n**[QZrb-4] Value of images.**  \nPlease see response **[JzC8-2]**.\n\n**[QZrb-5] Data bias.**  \nAs noted on L422 Col1, there are spatial biases in the training data we use, which has more data from North America (NA) and Europe (EU) (Fig. A24). The IUCN evaluation data is not as biased, and from the results in Fig. A23 we can see that we obtain better performance for species in NA and EU, but performance in the less sampled South America is still strong. Addressing training biases is an important question which we leave for future work. \n\n**[QZrb-6] Co-occurrence and second term in eqn 2.**  \nThis loss is borrowed from prior work (Cole et al. 2023). Without the second term in eqn. 2, the loss is trivially optimized by predicting that every species is present in every location (see Sec 3.2 in Cole et al. 2023). It is true that the second term penalizes the model for predicting that multiple species are present at the same location. However, the large value we use for $\\lambda$ (see L596) means that this penalty is much weaker than the one for failing to predict that an observed species is present. Thus, if two different species occur near the same location, the model is encouraged over different batches to predict that they are both present.\n\n**[QZrb-7] Performance on out of distribution species.**  \nTo clarify, by default no data for any of the species in the evaluation datasets is observed during training (L261 Col1). By training on tens of thousands of species, FS-SINR can generalize to previously unseen species at test time. The results in Fig. A23 provide an indication of performance on less common regions. Evaluating truly out of distribution species, i.e. ones that bear no relationship to the training data, is an interesting question but would likely require new evaluation datasets. We opted to use the standard IUCN/S&T datasets so that we could compare fairly to existing work.  \n\n**[QZrb-8] Performance when training on evaluation species.**  \nIn Table 1 we compare to SINR when data from the evaluation species are observed during training (i.e. TST in row 1 vs 3). It is not surprising that SINR performs better as it learns a unique embedding vector for each species (even evaluation ones), whereas FS-SINR must learn the mapping from a small number of locations to a species’ range. These results could be considered as positive as they demonstrate that FS-SINR is not overfitting by simply memorizing the text for each species. Text supervision is very sparse compared to the informative location observations used by SINR to learn its per-species encoding.  \n\n**[QZrb-9] Performance wrt number of observations.**  \nIn Fig 3 we observe that for nearly all methods tested, performance improves as more observations are provided. The largest improvements are observed when going from few (e.g. 10) to many observations but begins to plateau as the number approaches 50. This is consistent with results from existing work where we see more data provides diminishing returns (e.g. Cole et al.). \n\n**[QZrb-10] Evaluation metric - recall?**  \nWe report performance using both mean average precision (MAP) (Fig 3) *and* a distance weighted variant of it (Fig A27). MAP is the standard metric from existing work (e.g. Cole et al. and Hamilton et al.). As a reminder, average precision is the area under the precision-recall curve, which is based on precision *and* recall across a range of thresholds.\n\n**[QZrb-11] Comparison to other approaches.**  \nThe Gaussian Process (GP) approach in Golding et al. is designed for presence-absence data, but we can adapt it to our presence-only setting using pseudo-negatives. We train a GP classifier using an RBF kernel and a logit link function and a Random Forest (RF) classifier. Both are implemented using sklearn using the raw coordinates as input, with a separate classifier trained per-species. We find that they do not perform as well as FS-SINR which is trained jointly on multiple species. This is especially noticeable on the more challenging IUCN dataset. The results can be found here: https://postimg.cc/0zxWzDFB\n\n\n\n**[QZrb-12] Misc.**  \nWe will improve the readability of the figures and simplify the notation.", "claims_and_evidence": "Most claims are well supported. There are certain claims that lack substantial. Examples:\n- \"During training, we supply FS-SINR with\n20 context locations per training example, though we find\nthat the model performance is very robust to the number\nof context locations provided during training.\"\n- There is no quantification of the computational efficiency claimed throughout the paper\n- The paper does not explain how the insights from few-shot species prediction will have downstream impact on biodiversity analysis\n- The initial part of the paper makes claims about support for images, but the experiments reveal limited success. It is unclear if images can be used in practical settings. \n- It is unclear how bias in training data from North America and Europe effect prediction globally.", "methods_and_evaluation_criteria": "Overall, method and evaluation make sense.\n\nI did not understand why the second term in the loss function is needed. It is possible that some species co-exist with each other. Wouldn't that loss term discourage learning that behavior?\n\nIt is unclear how the model adapts to new species which are out of distribution to the training data.", "theoretical_claims": "No theoretical claims", "experimental_designs_or_analyses": "Overall experiment design is sound. I found the zero-shot results difficult to follow, can be presented better. The figures also are unreadable in black and white, can be improved. \n\nIt is unclear why SINR outperforms the proposed method when the species data is in the training set. Why doesn't the proposed method performance scale with the number of observations?\n\nGiven the loss function used, I would like to see how well the model captures species co-occurrence. \n\nIt is unclear why only precision is used as a metric, and recall is ignored.", "relation_to_broader_scientific_literature": "The paper's contributions build upon and advance several lines of prior work in species range estimation and machine learning. In terms of few-shot learning for range estimation, it improves upon traditional methods like SINR (Cole et al., 2023) which require model retraining for new species, and LE-SINR (Hamilton et al., 2024) which introduced text-based range estimation but still needs retraining. It's the first method to enable feed-forward range prediction for new species without retraining, showing better performance in low-data scenarios (1-10 samples) than Active SINR (Lange et al., 2023). \n\nWhile previous works have explored using different data types separately - SINR with only locations, LE-SINR with text, Dollinger et al. (2024) with satellite imagery, and Teng et al. (2023) with species images - this paper provides a unified framework that can flexibly combine all these modalities. In terms of architectural innovation, it introduces transformers to species range estimation, building on recent work using attention mechanisms for geographic tasks (Russwurm et al., 2024), whereas most previous methods relied on MLPs or CNNs.", "essential_references_not_discussed": "It is unclear how the proposed method compares against traditional Bayesian approaches, such as Golding et al. (2016)\n\n[1] Golding, N. and Purse, B.V., 2016. Fast and flexible Bayesian species distribution modelling using Gaussian processes. Methods in Ecology and Evolution, 7(5), pp.598-608.", "other_strengths_and_weaknesses": "Covered everything above", "other_comments_or_suggestions": "The symbols used in Section 3.1 can be simplified", "questions_for_authors": "Included my questions above, none critical to the decision", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ca547J3TSE", "reviewer": "Reviewer_JzC8", "summary": "The authors work within the problem setting of species-range estimation, where, given a latitude--longitude pair and a target species, the task is to determine the probability of being able to find that species at that location. Motivated by the large number of species for which only sparse sightings have been recorded, the authors propose a method for few-shot species-range estimation. Their formulation enables them to generalize to new species at inference time without retraining, and to condition off non-location data to improve coverage.\n\n## update after rebuttal\nThe authors' willingness to address outstanding issues is appreciated. Following their rebuttal commitment to add experiments, improve contextualization of results, and update the title, the recommendation is updated from Weak Accept (3) to Accept (4).\n\nThe marginal quantitative improvement over prior work and the unresolved issues with image-conditioning performance limit the recommendation it can be given (from Strong Accept), but the paper is solid, well-written, and relevant to the ICML audience, and should be accepted.\n\n---\n\nThe plot image provided in the *Reply Rebuttal Comment* is also interesting. The authors should consider examining instances where image conditioning is most quantitatively harmful. Are the images consistently 1) of non-animal \"evidence of an organism\" or do they 2) closely resemble (either in human or model perceptual space) species observed during training that have distinct ranges?", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank **JzC8** for their helpful suggestions. \n\n**[JzC8-1] Performance compared to LE-SINR.**  \nWe outperform the recent LE-SINR in both the few-shot (Fig 3) and zero-shot (Table 1) settings even though we do not require any training on the evaluation species as in LE-SINR, thus making us much faster (see **[nqXQ-1]**). These differences can be as large as 4% MAP (e.g. row 8 vs 9 in Table 1). We will update the text on L360 Col2 to better characterize the performance improvement in a more measured way. \n\n**[JzC8-2] Value of adding images.**  \nWe discuss the image results on L294 Col1 where we say: “Perhaps unsurprisingly, in general we observe that image information is not as informative as text. This can be explained by the fact that a range text description provides much more context than an image of a previously unseen species”. Models trained on image data and no text (row 10) perform substantially better than models trained with no images or text (row 4) on both datasets (see Table 1). This shows that images are a valuable source of supervision, when no other signal is available. In practice, detailed text describing the ranges of most species does not exist, but we may have images. However, even weaker text (e.g. habitat description in row 6) is better than using images and combining images and text, at best results in the same performance (row 11 vs 9 for S&T) or slightly degrades it (row 11 vs 9 for IUCN). We will update this discussion in the revised text. \n\n**[JzC8-3] Using a different visual encoder.**  \nAs suggested, we conducted additional experiments using a different visual encoder -- a DINOv2 backbone. Perhaps unsurprisingly, this model performs worse than the features we obtain from the iNaturalist trained EVA-02 Vit (L205 Col2) in the paper. We will add these new results to the revised paper. \n\n|Context locs.|IUCN  EVA|IUCN  DINO|SNT  EVA|SNT  DINO|\n|-|-|-|-|-|\n|0|0.19|0.13|0.38|0.28|\n|1|0.45|0.40|0.49|0.44|\n|5|0.62|0.56|0.66|0.63|\n|10|0.65|0.60|0.70|0.67|\n|20|0.66|0.61|0.71|0.68|\n\n**[JzC8-4] Results without using any text information.**     \nBelow we also report results for a model trained using *only* images and no text. During training the model sees between 1 to 5 images per species. Again this performs worse than when using text information, but providing images at inference helps. As noted above, text supervision is simply much more informative than images. However, using image data is still helpful when no other data is available (e.g. row 10 vs 4 in Table 1), and for most species we do not have text describing their ranges, but we can more easily obtain images.   \n\n|Eval. images|IUCN|S&T|\n|-|-|-|\n|1|0.17|0.35|\n|2|0.19|0.38|\n|3|0.20|0.39|\n|4|0.21|0.39|\n|5|0.21|0.40|\n\n**[JzC8-5] Results using automatically generated text captions.**     \nThis is an interesting suggestion. Current vision-language models with open-weights (e.g. BLIP2) are not yet capable of generating detailed descriptions for fine-grained species images, and instead provide relatively coarse captions, e.g. “a horse standing in a field”. We generated text captions for our images using BLIP2 and evaluated an already trained FS-SINR on these captions. Here we used both no prompt and two different prompts to produce captions. “No prompt” produced captions like *\"a small bird is perched on a branch of a tree with flowers on it\"*, “What species is this?” produced captions like *\"rufous-bellied hummingbird\"* and “Where is this?” produced captions like *\"the savannas of south africa\"*.  Results on IUCN below show that in all cases captions are worse than using the original images they are generated from.\n\n|Context locs.|Image|No prompt|What species?|Where is this?|\n|-|-|-|-|-|\n|0|0.19|0.07|0.09|0.11|\n|1|0.45|0.27|0.30|0.24|\n|5|0.62|0.53|0.56|0.52|\n|10|0.65|0.60|0.62|0.58|\n|20|0.66|0.64|0.65|0.62|\n\n**[JzC8-6] Can the model make use of multiple images simultaneously.**   \nYes, this is possible. Conditioning on one image at inference time, as in the paper results in an MAP of 0.19 on IUCN (row 10 Table 1). If we instead use four images at inference, we obtain an MAP of 0.21. Thanks for the interesting suggestion.\n\n**[JzC8-7] Results in the non few-shot setting.**   \nAs requested, we conducted additional experiments beyond the 50 context locations used in the paper. Interestingly, even though this FS-SINR model was never trained on more than 20 context locations, adding more locations at inference time does not degrade performance. In the table below, shown for IUCN, we observe that performance saturates at around 50 context locations and the evaluated models gain a very small boost going from 20 to 1000 locations.\n\n|Context locs.|20|50|500|1000|\n|-|-|-|-|-|\n|SINR (No Text)|0.61|0.64|0.65|0.65|\n|LE-SINR (Text)|0.64|0.66|0.67|0.67|\n|FS-SINR (Text)|0.67|0.68|0.68|0.68|\n\n**[JzC8-8] Additional suggestions, minor comments, reordering images, and typos.**   \nThanks for flagging these. We will address them in the revised text.", "claims_and_evidence": "Yes, the claims are supported by the presented evidence.", "methods_and_evaluation_criteria": "Yes, the benchmark is appropriate for the chosen task.", "theoretical_claims": "N/A. The authors do not make claims that might necessitate a formal proof.", "experimental_designs_or_analyses": "Yes, the experimental design appears sound.", "relation_to_broader_scientific_literature": "The work is not the first to evaluate few-shot generalization in species-range estimation, which makes the title somewhat confusing, but the authors generally contextualize their work fairly.", "essential_references_not_discussed": "No, the works referenced appear fairly comprehensive.", "other_strengths_and_weaknesses": "### Strengths\n1. The paper is well-written and was easy to follow.\n2. The authors' extensive Appendix ablations are appreciated.\n3. The task is interesting and well-motivated by data scarcity.\n### Weaknesses\n1. Performance improvements over the LE-SINR (Hamilton et al., 2024) text-conditioned baseline in both the few-shot and zero-shot evaluations appears somewhat marginal. The proposed approach does outperform it, and the modeling decisions enable simpler generalization to new species, but this may be limiting of contribution. The characterization of the few-shot performance as \"impressive\" on L360 should either be better contextualized or removed.\n2. The addition of image conditioning appears on average quantitatively harmful, but the authors do not seem to acknowledge this. The result is unintuitive and is deserving of scrutiny. Questions include: 1) Does the issue persist with different visual encoders? 2) Are the same results observed when the model is trained without any text conditioning? 3) What happens if the model is instead trained on text embeddings of captions automatically extracted from the images (using another model)?", "other_comments_or_suggestions": "1. The title does not seem to clearly reflect the contribution of the paper, as the authors do not claim to introduce the few-shot setting, which was also evaluated in Hamilton et al. (2024). The authors should consider updating it to highlight what makes the work unique.\n2. The $t$ in $\\mathcal{C}^t$ doesn't appear to be defined until Appendix 2.4. Consider mentioning it after first usage.\n3. It appears an error to describe the model as \"invariant to the number . . . of the context locations,\" which seems should necessarily affect the output.\n4. The authors should additionally report model performance in the non-few-shot setting.\n5. Can the model make use of multiple images simultaneously? The lack of numbering on $t_j$ and $a_j$ on L183 lends the impression no.\n6. The authors should use `\\citet` for citations that are referred to as sentence objects.\n7. The \"Prototype\" baseline is referenced on L245, pages before it is defined.\n8. The authors should additionally define \"MAP\" on L237 as the figure caption is likely to be read before L265.\n9. The \"We\" referenced at the beginning of L436 is confusing. It should be clarified that it does not refer exclusively to the paper authors like it is used to a couple sentences later.\n10. The bibliography is very well formatted.\n11. The figures in the supplementary materials should be moved around so that they are located near where they are referenced. They're now often separated by multiple pages.\n\nL002 (and throughout): Few-shot -> Few-Shot\n\nL154: Give -> Given\n\nL209: (e.g., images) -> (i.e., images)\n\nL238: very low-setting setting -> ??\n\nL258: currently best -> best currently\n\nL267: Baslines -> Baselines\n\nL340: (ours) -> (Ours)\n\nL582: \"heads\". -> \"heads.\"", "questions_for_authors": "The authors are encouraged to in particular engage with Weakness #2.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {"ethical_review_concerns": "N/A"}}, {"review_id": "6myR9ICXsa", "reviewer": "Reviewer_HT4t", "summary": "This paper outlined a new approach for few-shot species range estimation. The goal is to outline geospatial regions where an animal is likely to live based on previous observations of occurrence. The authors' approach builds upon Spatially Implicit Neural Representation (SINR) models designed to estimate species range based on location alone. Their new FS-SINR approach leverages a Transformer-based head and a novel set of 'context' locations, giving the model examples of where a new out of distribution organism might be found, in addition to the desired query location at inference. They benchmark their approach against the original SINR model, an active learning approach, and another SINR-based model that encodes free-text descriptions of species range sourced from the internet. They tested performance in few-shot and zero-shot situations, measuring performance using the IUCN and S&T baselines articulated in the original SINR paper. They registered marginally improved performance against the existing models. While the headline MAP numbers are comparable, their model does not require retraining for every new species under observation at test time---FS-SINR achieves that performance improvement without requiring expensive new training cycles.\n\n## Update after rebuttal\nThanks to the authors for their responses to all the reviewer comments. My assessment remains unchanged and seems in line with that of the other reviewers.", "questions": "", "limitations": "", "rating": 5, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank **HT4t** for their helpful questions. By addressing these comments we believe that the description of the data processing and the evaluation protocol in the revised paper will be much clearer. \n\n**[HT4t-1] Spatial resolution of the model and data aggregation.**  \nWe use the same training and evaluation data as SINR (Cole et al. 2023), where the main difference is that by default the evaluation species are not included in our training data. As in SINR, FS-SINR uses continuous coordinates as input so there is no spatial scale explicitly defined for training, i.e. it is implicit. For the evaluation locations, we follow the same pre-processing steps as SINR, i.e. H3 cells at resolution five, which results in 2M total cells, each with an average area of ~250km^2. We will clarify this in the revised text.  \n\n**[HT4t-2] Held out data comment on L260.**   \nIn Table 1 there are results for models that also train on the evaluation species (i.e. TST), but otherwise by default these evaluation species are held out of the training data.  \n\n**[HT4t-3] Zero-shot experimental protocol.**  \nWe follow the same zero-shot evaluation protocol as LE-SINR (Hamilton et al. 2024). Specifically, for FS-SINR we either provide habitat (HT), range text (RT), or an image (I) for each evaluation species as input. Additionally, we also can evaluate our model when no meta-data is provided as we simply use the output of the class token as the species embedding vector (see Fig 2). In this case, the class token will be the same for all species. With the exception of the taxonomic rank text (TRT) variant which is inspired by the hierarchical species name encoding from Sastry et al., no specific species name text is provided to any of our models. We will update the relevant text to make this clearer. \n\n**[HT4t-4] Summarize ecological findings.**   \nOn L50 Col2 we note that a significant proportion of species only have a small number of location observations available. This indicates that, beyond common charismatic species, the task of species range estimation is a few-shot learning problem. We believe that the primary impact of our work relevant to ecology researchers will be better species range estimates in this few-shot regime compared to existing work (e.g. ~10% MAP compared to SINR in Fig 3 given only 10 observations). In Appendix D we also provide additional analysis that we believe will be of interest to ecologists, e.g. the spatial distribution of errors in Fig. A22, performance by continent in Fig. A23, performance versus range size in Fig. A25, and performance versus taxonomic group in Fig. A26. We will update the text to make these observations more clear and to point to relevant results in the Appendix. Thanks for the suggestion. \n\n**[HT4t-5] Hughes et al., 2021.**  \nThanks for flagging this paper. We will reference it in Sec 5 as it is an excellent reference related to the discussion of biases in natural world data.", "claims_and_evidence": "The claims made by the authors seem sound. Their results and claims---namely that their model performs well in the few-shot case---are supported by their experiments and well-articulated in the paper.", "methods_and_evaluation_criteria": "- The proposed methods seem appropriate as does the chosen evaluation data. The authors compared against the three closest model types and a few generic baselines. They also experimented with different multi modal inputs to assess the added value of including images, free-text metadata, etc. \n\n- The authors should specify the spatial resolution of their model. Did they aggregate the presence data in tiles along the line of the original SINR paper? Or did they use a different strategy and/or spatial scale?", "theoretical_claims": "N/A", "experimental_designs_or_analyses": "- At line ~260 they describe holding out species in the union of the IUCN and S&T baselines 'unless otherwise stated.' I haven't been able to spot any notes of exceptions. Are there particular instances where an animal from that union was included in training? \n\n- I am a little confused about the zero-shot experiments described in section 4.3 (starting on page 6). Was the experiment prompting the models with the name of an unseen species and some combination of additional metadata? Or something else? Some of that information shows up in the caption of Table 1, but it is not effectively laid out. A little clarification in the beginning of the section would be helpful. \n\n- In section 4.4 the authors reference the 'ecologically relevant breakdown' of their results in appendix D. Space permitting, it would really strengthen the paper to include some of that material in the main body. Part of the value of their model appears to be robustness to the domain biases and distribution shifts they articulate in that appendix. At least a paragraph summarizing the major findings from the appendix seems appropriate.", "relation_to_broader_scientific_literature": "This vein of work is of keen interest to ecologists and conservation biologists. The results in appendix D as it pretains to underlying sample biases is especially relevant given increasing recognition that sample bias impacts our understanding of ecological patterns and processes (e.g. Hughes et al., 2021; https://doi.org/10.1111/ecog.05926)", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "N/A", "other_comments_or_suggestions": "On page 7, the authors should provide a direct reference to appendix D containing the 'ecologically relevant breakdown' of results.", "questions_for_authors": "N/A", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "nQ8Vmg4wKj", "reviewer": "Reviewer_nqXQ", "summary": "The paper introduces FS-SINR (\"few-shot spatial implicit neural representations\") for few-shot species range estimations, which is trained on citizen science location data. A key feature of FS-SINR is that once it has been trained, it can be used during inference to predict the range even of previously unseen species in a feed-forward way. The overall approach of FS-SINR is that during inference it takes as input a set of spatial locations (called \"context locations\") -- together with (optional) metadata, e.g. a textual or image description -- and then it feeds all info through a Transformer, which outputs a species encoding. To assess presence / absence, a query location x is embedded separately (using the same embedding function f as is used for the context locations), and then the species and query location embeddings are multiplied and fed to a sigmoid for binary decision making. The experimental results show that FS-SINR obtains state-of-the-art results on this task (in two different and relevant metrics), especially in the low-data regime.\n\n## update after rebuttal\nI thank the reviewers for their responses to mine and the other reviewers' comments. There is consensus on accepting the paper and I also keep my accept recommendation. Nice paper!", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank **nqXQ** for their careful reading of the paper and constructive suggestions. \n\n**[nqXQ-1] Quantification of computational efficiency.**   \nBelow we report inference timings for different models (with 1 location + text), reported as the time taken in seconds to generate all evaluation species weights for LE-SINR and FS-SINR on the CPU. We observe that FS-SINR can generate the species embedding vector in as little as 2% of the time taken for LE-SINR which has to perform test time optimization to train a linear classifier for each species. In addition to not requiring any training for held-out species, FS-SINR also has fewer overall parameters compared to the SINR baseline (8.2M vs. 11.9M, see L214 Col2). We will expand on these timing results in the final revised text.  \n\n|Model|Time|\n|-|-|\n|LE-SINR|631.3 |\n|FS-SINR|14.3 |\n\n**[nqXQ-2] Results as more context locations are added to Fig 7.**  \nThe quantitative evaluation in Fig 3 shows that as more context locations are added we observe an increase in agreement with the expert-derived range maps. However, we agree with nqXQ that the results do start to plateau and additional context locations provide diminishing returns (at least when averaged across all species in the IUCN and S&T evaluation sets). As requested, we report the AP for each of the species from Fig 7 below. We observe an increase in performance when increasing the number of context locations for these species.\n\n|Context locs. |Common Kingfisher |European Robin |Black and White Warbler |\n|-|-|-|-|\n|1 |0.59 |0.70 |0.49 |\n|2 |0.63 |0.72 |0.52 |\n|5 |0.79 |0.74 |0.59 |\n|10 |0.82 |0.78 |0.68 |\n\n\n**[nqXQ-3] Uncertainty quantification.**  \nThis is a really interesting suggestion. Taking inspiration from Poggi et al. [a] we include a sparsification-based uncertainty evaluation where data is progressively removed based on the uncertainty estimates derived from an ensemble of three FS-SINR models (see Sec 4.1 in [a]). These results are on S&T using range text with different numbers of context locations. We report Sparsification Error AUC (SEAUC) and Area Under the Random Gain (AURG). AURG is positive and increases when more context locations are provided demonstrating that the ensemble is better at estimating its uncertainty than random chance and becomes more accurate as more context locations are provided.\n\n|Context locs. |MAP|SEAUC|AURG|\n|-|-|-|-|\n|0 |0.66 |0.68 |0.03 |\n|1 |0.68 |0.71 |0.03 |\n|5 |0.71 |0.75 |0.04 |\n|10 |0.73 |0.77 |0.04 |\n|20 |0.74 |0.79 |0.05 |\n\nWe also visualize the predicted mean and variance for the ensemble model using the species from Fig A16. We observe high variance in locations where the individual models in the ensemble differ (e.g. South America in 2nd row). The result can be found here: https://postimg.cc/VdNTgjD0 \n\n[a] Poggi, On the uncertainty of self-supervised monocular depth estimation, CVPR 2020\n\n\n\n\n**[nqXQ-4] Performance of text model -- with and without images.**   \nAs discussed on L294 Col1, text describing a range is inherently more informative than images. However, in Table 1 we observe that images are still a valuable supervision source when no other meta-data is available (i.e. row 10 vs 4). Adding images with text does not hurt performance on S&T (row 11 vs 9), but does result in a drop in the more challenging IUCN dataset. This same pattern is apparent in Fig 3. The potential explanation here is that images provide sufficiently weaker signal, and greater opportunity to overfit to incorrect spurious features, thus negatively impacting performance. However, it is worth noting that even in the case of IUCN, FS-SINR with image and text still outperforms the recent state-of-the-art LE-SINR (see Fig 3). \n\n**[nqXQ-5] FS-SINR abbreviation.**   \nThanks for the suggestion. We will clarify this at the start of the paper. \n\n**[nqXQ-6] Plant or animal species?**   \nWe will clarify this. As noted on L241 Col1, we use the same training data as Cole et al. 2023 which contains observations for 47,375 species of plants *and* animals. \n\n**[nqXQ-7] Typos.**  \nThanks for flagging these two typos, we will fix them.", "claims_and_evidence": "Yes, most claims are supported by clear and convincing evidence (such as the statement that FS-SINR obtains SOTA results on two benchmarks, which is clearly shown e.g. in Fig. 3, and also the results appear extra reliable given that the results holds across two relevant metrics, as well as due to the strong emphasis that has been put on making the baselines as good / comparable as possible, as detailed in Appendix A.2).\n\nThe one claim I did not find quite as fully convincing is (abstract): \"..., in a fraction of the compute time, ...\". I did not see much more discussion on compute time in the paper, except around L185 that reads: \"Our approach is computationally efficient in that once the species embedding is generated it can then be efficiently multiplied by the embeddings for all locations of interest to generate a prediction for a species’ range.\" <-- That makes sense to me, but given the statement around 'fraction of compute time', I was hoping to see some actual runtime numbers, ideally in the paper, of the proposed method vs baselines.", "methods_and_evaluation_criteria": "Yes, the methods / evaluation criteria definitely make sense for the problem at hand. \n\nExamples that support my assessment:\n\n* The evaluation is done on not only one, but two, of the common species range estimation (SRE) datasets, which makes claims e.g. about FS-SINR being a new SOTA more reliable.\n\n* Also, not only one but two evaluation criteria are used (mean average precision (MAP), but also distance-weighted MAP), both of which are standard for SRE (to the best of my knowledge), and in both of which FS-SINR is the best.\n\n* Good that many / most results (e.g. Fig 3) contain error bars with a few seeds, further reinforces the findings.\n\n* Finally, several relevant baselines are used for the comparisons (and these are well-detailed in Appendix A2).", "theoretical_claims": "No, theoretical claims were not made.", "experimental_designs_or_analyses": "Yes, I did so for all things in the main paper. I found the experimental designs / analyses to be sound and valid. Many of the reasons for this assessment are already explained under \"Methods And Evaluation Criteria\", but here are some additions:\n\n* **OBS:** Before I carefully checked Figure A3 (appendix), I had written the following as an experimental design I was _missing_, but then it is actually covered in the appendix, so it instead **furthers the soundness and validity of the experimental designs and analyses** (thus disregard the stuff in quotation marks as something to address in the rebuttal -- it is something good!): \"In Sec. 3.2.1, it is discussed how the flexible FS-SINR framework allows for leveraging e.g. image / text info, assuming such info is also available in training. That's great. But what I would have wanted to see somewhere (e.g. appendix) was how the _amount_ of such metadata training affects inference. In other words, what happens if such metadata is used for 10% of the \"training instances\", 20%, ... 100% (and of course also with 0%). Right now it is as I understand it 50% of the time that text is used, and image is used 90% of the time (?). Similarly -- and also relating to the sentence \"Note, we train FS-SINR such that it can use arbitrary subsets of these input tokens during inference\" (right before \"4. Experiments) -- it would have been nice to see how the 'extra-info-agnostic' approach currently used compares to a variant with 'always-extra-info'. One would perhaps expect that a model that _always_ obtains the metadata would outperform one that is agnostic to the amount of metadata (?).\"\n\n* I liked the use of qualitative analyses as well, as they furthered my intuitive understanding of FS-SINR, e.g.\n    - Fig. 4 clearly shows the impact that a text input can have\n    - Fig. 7 provides good intuitive insight to the effect of increasing the number of context locations in a few-shot setting.\n        + A small negative, however, is that in my view it is not entirely easy to see that the caption statement \"As we increase thh number of context locations, the predictions become closer to the expert ranges\" always holds. Perhaps it would be possible to compute some quantitative metric for each case, to see that this metric actually improves? (In particular, some times things seem to saturate between 5 and 10 locations.)", "relation_to_broader_scientific_literature": "I think the authors succeed in clearly placing this work relative to the broader scientific literature, and in particular how it improves upon / relates to the existing scientific literature on species range estimation (including that older non-DL-based approaches were included, not just modern DL-based variants).\n\nThe FS-SINR approach most clearly builds upon the SINR approach by Cole et al. (2023), and also the LE-SINR extension (Hamilton et al. 2024), but improves LE-SINR in that it FS-SINR can incorporate image (i.e. not just text, as LE-SINR) as metadata, and also that FS-SINR, different from LE-SINR, does not require retraining a classifier for each new species observation added. \n\nThe authors by the way note that other approaches, here based on (Lange et al., 2023) and (Snell et al., 2017), also do not require inference time retraining -- so FS-SINR is not entirely novel in that regard -- but those methods on the other hand perform much worse, as shown in the results chapter.", "essential_references_not_discussed": "To the best of my limited literature knowledge, no essential references were missing.", "other_strengths_and_weaknesses": "STRENGTHS\n\n* Great that an extensive Limitations as well as Impact Statement chapter were included. These are important parts that should not be neglected, and I think they provide insightful comments.\n\n* Building on the above, the problem of species range estimation is clearly a highly important one. So the topic of the paper itself is very relevant given the climate and ecological crises we are in.\n\nWEAKNESSES\n\n* It would have been good if the paper had looked into some form of uncertainty quantification. I would assume that is a very important thing to be aware of (i.e. a models rough uncertainty of predictions). A simple starting point may be to train individual models and look at ensembles and individual-model-deiviations from ensemble predictions.\n\n* It was unclear to me what \"FS-SINR\" was an abbreviation of first. I had to look up the SINR abbreviation by checking the Cole et al 2023 reference. Consider writing it out more clearly in the paper.", "other_comments_or_suggestions": "* It may be good to state whether \"species\" refers to animal species or plant species (or both). I believe the latter is the focus when talking about species range estimation, but would still be good to know.\n\n* I think perhaps there is a typo in eq. (1), where the capital S should be a lower-case s, in consistency with the previous notation?\n\n* Double word \"the the\" at Line 349.", "questions_for_authors": "* Do you have an idea as to why, based e.g. on Fig. 3, results are better using FS-SINR-text than FS-SINR-image+text (yes, I saw the discussion on text being more informative than image in the main paper, but the _combination_ text+image seems to me should be at least as good as text-only (?))? \n\n* Can you please provide some info on inference runtimes of FS-SINR vs other methods, to show that the claim on 'fraction of compute-time' holds?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: All reviewers agree to accept this paper. Please include all the necessary changes suggested by the reviewers, e.g., add experiments, improve contextualization of results, and update the title, in the final version.
Decision: Accept (poster)